library(tidyverse)
library(keyringr)
library(DBI)
library(DT)
library(ggplot2)
library(scales)
library(knitr)
library(plotly)
library(kableExtra)
library(ggthemes)
library(lubridate)
library(plyr)
library(DescTools)
library(car)
library(rgl)
library(MKmisc)
library(psych)
library(apaTables)
library(gmodels)
library(ResourceSelection)
library(lavaan)
library(semPlot)
Placement<- read.csv("Placement.csv")
Placement<- Placement%>%
mutate(EnglishStart = if_else(StartTerm == Col_Eng_Term, 1, 0, missing = NULL))
Placement<- Placement%>%
mutate(MathStart = if_else(StartTerm == Col_Math_Term, 1, 0, missing = NULL))
MathC_Term<- Placement%>%
subset(College_Math_Pass %in% c(1,0))
MathC_Term<- MathC_Term %>%
dplyr:: select(Col_Math_Term, College_Math_Pass, College_Math_Grade, MathStart, HSGPA, ACT_Math, ALEKS)
MathC_Term<- MathC_Term %>%
subset(Col_Math_Term %in% c("10/FA", "11/FA", "12/FA", "13/FA", "14/FA", "15/FA", "16/FA", "17/FA", "18/FA", "19/FA", "20/FA"))
MathC_Term_First<- MathC_Term %>%
subset(MathStart == 1)
EnglC_Term<- Placement%>%
subset(College_English_Pass %in% c(1,0))
EnglC_Term<- EnglC_Term %>%
dplyr:: select(Col_Eng_Term, College_English_Pass, College_English_Grade, EnglishStart, HSGPA, ACT_Engl, ACT_Reading, McCann_R, McCann_W)
EnglC_Term<- EnglC_Term %>%
subset(Col_Eng_Term %in% c("10/FA", "11/FA", "12/FA", "13/FA", "14/FA", "15/FA", "16/FA", "17/FA", "18/FA", "19/FA", "20/FA"))
EnglC_Term_First<- EnglC_Term %>%
subset(EnglishStart == 1)
PlacementMath<- Placement%>%
subset(Col_Math_Term %in% c("10/FA", "11/FA", "12/FA", "13/FA", "14/FA", "15/FA", "16/FA", "17/FA", "18/FA", "19/FA", "20/FA"))
statistics<- Placement %>%
subset(CourseName.y.y == "STAT*2070") %>%
subset(Col_Math_Term %in% c("10/FA", "11/FA", "12/FA", "13/FA", "14/FA", "15/FA", "16/FA", "17/FA", "18/FA", "19/FA", "20/FA"))
math1400<- Placement %>%
subset(CourseName.y.y %in% c("MATH*1400", "MATH*1401", "Math*1010")) %>%
subset(Col_Math_Term %in% c("10/FA", "11/FA", "12/FA", "13/FA", "14/FA", "15/FA", "16/FA", "17/FA", "18/FA", "19/FA", "20/FA"))
library(gmodels)
FTTable<-with(Placement, table(StartTerm))
FTTable
## StartTerm
## 00/FA 00/SP 00/SU 01/FA 01/SP 01/SU 02/FA 02/SP 02/SU 03/FA 03/SP 03/SU 04/FA
## 14 15 1 23 7 3 42 23 4 56 32 8 55
## 04/SP 04/SU 05/FA 05/SP 05/SU 06/FA 06/SP 06/SU 07/FA 07/SP 07/SU 08/FA 08/SP
## 32 8 77 52 20 108 38 12 162 52 14 220 72
## 08/SU 09/FA 09/SP 09/SU 10/FA 10/SP 10/SU 11/FA 11/SP 11/SU 12/FA 12/SP 12/SU
## 18 294 102 40 390 121 59 408 195 55 415 176 46
## 13/FA 13/SP 13/SU 14/FA 14/SP 14/SU 15/FA 15/SP 15/SU 16/FA 16/SP 16/SU 17/FA
## 440 135 47 465 153 48 652 146 67 571 172 34 510
## 17/SP 17/SU 18/FA 18/SP 18/SU 19/FA 19/SP 19/SU 20/FA 20/SP 20/SU 21/FA 21/SP
## 142 43 471 106 39 451 105 21 350 72 13 293 74
## 21/SU 22/FA 84/SU 85/FA 85/SP 86/FA 86/SP 87/FA 87/SP 87/SU 88/FA 88/SP 89/FA
## 16 2 1 2 2 3 1 10 3 1 8 3 11
## 89/SP 89/SU 90/FA 90/SP 90/SU 91/FA 91/SP 91/SU 92/FA 92/SP 92/SU 93/FA 93/SP
## 5 2 10 2 2 15 6 1 8 2 4 13 4
## 93/SU 94/FA 94/SP 94/SU 95/FA 95/SP 95/SU 96/FA 96/SP 96/SU 97/FA 97/SP 97/SU
## 6 10 4 2 12 13 3 22 6 6 21 10 1
## 98/FA 98/SP 98/SU 99/FA 99/SP 99/SU
## 19 3 4 17 16 5
library(gmodels)
library(xlsx)
MathFirst<-CrossTable(Placement$Col_Math_Term, Placement$MathStart, chisq = FALSE, prop.r = TRUE, prop.c = FALSE, expected = FALSE, prop.t = FALSE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## |-------------------------|
##
##
## Total Observations in Table: 5462
##
##
## | Placement$MathStart
## Placement$Col_Math_Term | 0 | 1 | Row Total |
## ------------------------|-----------|-----------|-----------|
## 10/FA | 140 | 39 | 179 |
## | 0.287 | 0.849 | |
## | 0.782 | 0.218 | 0.033 |
## ------------------------|-----------|-----------|-----------|
## 11/FA | 162 | 43 | 205 |
## | 0.500 | 1.482 | |
## | 0.790 | 0.210 | 0.038 |
## ------------------------|-----------|-----------|-----------|
## 11/SP | 205 | 12 | 217 |
## | 11.286 | 33.415 | |
## | 0.945 | 0.055 | 0.040 |
## ------------------------|-----------|-----------|-----------|
## 12/FA | 135 | 36 | 171 |
## | 0.402 | 1.192 | |
## | 0.789 | 0.211 | 0.031 |
## ------------------------|-----------|-----------|-----------|
## 12/SP | 164 | 21 | 185 |
## | 4.779 | 14.149 | |
## | 0.886 | 0.114 | 0.034 |
## ------------------------|-----------|-----------|-----------|
## 13/FA | 193 | 65 | 258 |
## | 0.000 | 0.000 | |
## | 0.748 | 0.252 | 0.047 |
## ------------------------|-----------|-----------|-----------|
## 13/SP | 258 | 13 | 271 |
## | 15.161 | 44.890 | |
## | 0.952 | 0.048 | 0.050 |
## ------------------------|-----------|-----------|-----------|
## 14/FA | 179 | 63 | 242 |
## | 0.020 | 0.059 | |
## | 0.740 | 0.260 | 0.044 |
## ------------------------|-----------|-----------|-----------|
## 14/SP | 259 | 17 | 276 |
## | 13.452 | 39.830 | |
## | 0.938 | 0.062 | 0.051 |
## ------------------------|-----------|-----------|-----------|
## 15/FA | 264 | 78 | 342 |
## | 0.272 | 0.807 | |
## | 0.772 | 0.228 | 0.063 |
## ------------------------|-----------|-----------|-----------|
## 15/SP | 216 | 14 | 230 |
## | 11.295 | 33.444 | |
## | 0.939 | 0.061 | 0.042 |
## ------------------------|-----------|-----------|-----------|
## 16/FA | 173 | 145 | 318 |
## | 17.617 | 52.162 | |
## | 0.544 | 0.456 | 0.058 |
## ------------------------|-----------|-----------|-----------|
## 16/SP | 226 | 25 | 251 |
## | 7.847 | 23.233 | |
## | 0.900 | 0.100 | 0.046 |
## ------------------------|-----------|-----------|-----------|
## 17/FA | 151 | 118 | 269 |
## | 12.475 | 36.936 | |
## | 0.561 | 0.439 | 0.049 |
## ------------------------|-----------|-----------|-----------|
## 17/SP | 194 | 22 | 216 |
## | 6.555 | 19.409 | |
## | 0.898 | 0.102 | 0.040 |
## ------------------------|-----------|-----------|-----------|
## 18/FA | 118 | 116 | 234 |
## | 18.523 | 54.844 | |
## | 0.504 | 0.496 | 0.043 |
## ------------------------|-----------|-----------|-----------|
## 18/SP | 153 | 19 | 172 |
## | 4.640 | 13.738 | |
## | 0.890 | 0.110 | 0.031 |
## ------------------------|-----------|-----------|-----------|
## 19/FA | 110 | 131 | 241 |
## | 27.319 | 80.887 | |
## | 0.456 | 0.544 | 0.044 |
## ------------------------|-----------|-----------|-----------|
## 19/SP | 139 | 20 | 159 |
## | 3.414 | 10.107 | |
## | 0.874 | 0.126 | 0.029 |
## ------------------------|-----------|-----------|-----------|
## 20/FA | 198 | 153 | 351 |
## | 15.798 | 46.775 | |
## | 0.564 | 0.436 | 0.064 |
## ------------------------|-----------|-----------|-----------|
## 20/SP | 109 | 19 | 128 |
## | 1.853 | 5.487 | |
## | 0.852 | 0.148 | 0.023 |
## ------------------------|-----------|-----------|-----------|
## 21/FA | 145 | 172 | 317 |
## | 35.692 | 105.679 | |
## | 0.457 | 0.543 | 0.058 |
## ------------------------|-----------|-----------|-----------|
## 21/SP | 192 | 38 | 230 |
## | 2.342 | 6.936 | |
## | 0.835 | 0.165 | 0.042 |
## ------------------------|-----------|-----------|-----------|
## Column Total | 4083 | 1379 | 5462 |
## ------------------------|-----------|-----------|-----------|
##
##
MathPass<-CrossTable(MathC_Term$Col_Math_Term, MathC_Term$College_Math_Pass, chisq = FALSE, prop.r = TRUE, prop.c = FALSE, expected = FALSE, prop.t = FALSE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## |-------------------------|
##
##
## Total Observations in Table: 2815
##
##
## | MathC_Term$College_Math_Pass
## MathC_Term$Col_Math_Term | 0 | 1 | Row Total |
## -------------------------|-----------|-----------|-----------|
## 10/FA | 23 | 157 | 180 |
## | 3.261 | 0.744 | |
## | 0.128 | 0.872 | 0.064 |
## -------------------------|-----------|-----------|-----------|
## 11/FA | 52 | 156 | 208 |
## | 4.616 | 1.053 | |
## | 0.250 | 0.750 | 0.074 |
## -------------------------|-----------|-----------|-----------|
## 12/FA | 47 | 124 | 171 |
## | 7.301 | 1.666 | |
## | 0.275 | 0.725 | 0.061 |
## -------------------------|-----------|-----------|-----------|
## 13/FA | 50 | 209 | 259 |
## | 0.073 | 0.017 | |
## | 0.193 | 0.807 | 0.092 |
## -------------------------|-----------|-----------|-----------|
## 14/FA | 32 | 210 | 242 |
## | 3.736 | 0.853 | |
## | 0.132 | 0.868 | 0.086 |
## -------------------------|-----------|-----------|-----------|
## 15/FA | 53 | 289 | 342 |
## | 1.748 | 0.399 | |
## | 0.155 | 0.845 | 0.121 |
## -------------------------|-----------|-----------|-----------|
## 16/FA | 55 | 263 | 318 |
## | 0.282 | 0.064 | |
## | 0.173 | 0.827 | 0.113 |
## -------------------------|-----------|-----------|-----------|
## 17/FA | 41 | 228 | 269 |
## | 1.613 | 0.368 | |
## | 0.152 | 0.848 | 0.096 |
## -------------------------|-----------|-----------|-----------|
## 18/FA | 37 | 197 | 234 |
## | 0.964 | 0.220 | |
## | 0.158 | 0.842 | 0.083 |
## -------------------------|-----------|-----------|-----------|
## 19/FA | 53 | 188 | 241 |
## | 1.511 | 0.345 | |
## | 0.220 | 0.780 | 0.086 |
## -------------------------|-----------|-----------|-----------|
## 20/FA | 80 | 271 | 351 |
## | 3.353 | 0.765 | |
## | 0.228 | 0.772 | 0.125 |
## -------------------------|-----------|-----------|-----------|
## Column Total | 523 | 2292 | 2815 |
## -------------------------|-----------|-----------|-----------|
##
##
MathFTpass<- CrossTable(MathC_Term_First$Col_Math_Term, MathC_Term_First$College_Math_Pass, chisq = FALSE, prop.r = TRUE, prop.c = FALSE, expected = FALSE, prop.t = FALSE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## |-------------------------|
##
##
## Total Observations in Table: 987
##
##
## | MathC_Term_First$College_Math_Pass
## MathC_Term_First$Col_Math_Term | 0 | 1 | Row Total |
## -------------------------------|-----------|-----------|-----------|
## 10/FA | 3 | 36 | 39 |
## | 3.075 | 0.786 | |
## | 0.077 | 0.923 | 0.040 |
## -------------------------------|-----------|-----------|-----------|
## 11/FA | 16 | 27 | 43 |
## | 5.991 | 1.532 | |
## | 0.372 | 0.628 | 0.044 |
## -------------------------------|-----------|-----------|-----------|
## 12/FA | 17 | 19 | 36 |
## | 12.751 | 3.261 | |
## | 0.472 | 0.528 | 0.036 |
## -------------------------------|-----------|-----------|-----------|
## 13/FA | 12 | 53 | 65 |
## | 0.116 | 0.030 | |
## | 0.185 | 0.815 | 0.066 |
## -------------------------------|-----------|-----------|-----------|
## 14/FA | 10 | 53 | 63 |
## | 0.624 | 0.160 | |
## | 0.159 | 0.841 | 0.064 |
## -------------------------------|-----------|-----------|-----------|
## 15/FA | 12 | 66 | 78 |
## | 0.950 | 0.243 | |
## | 0.154 | 0.846 | 0.079 |
## -------------------------------|-----------|-----------|-----------|
## 16/FA | 29 | 116 | 145 |
## | 0.009 | 0.002 | |
## | 0.200 | 0.800 | 0.147 |
## -------------------------------|-----------|-----------|-----------|
## 17/FA | 21 | 97 | 118 |
## | 0.382 | 0.098 | |
## | 0.178 | 0.822 | 0.120 |
## -------------------------------|-----------|-----------|-----------|
## 18/FA | 20 | 96 | 116 |
## | 0.556 | 0.142 | |
## | 0.172 | 0.828 | 0.118 |
## -------------------------------|-----------|-----------|-----------|
## 19/FA | 28 | 103 | 131 |
## | 0.066 | 0.017 | |
## | 0.214 | 0.786 | 0.133 |
## -------------------------------|-----------|-----------|-----------|
## 20/FA | 33 | 120 | 153 |
## | 0.109 | 0.028 | |
## | 0.216 | 0.784 | 0.155 |
## -------------------------------|-----------|-----------|-----------|
## Column Total | 201 | 786 | 987 |
## -------------------------------|-----------|-----------|-----------|
##
##
write.csv(MathFirst,"MathFirst.csv")
write.csv(MathPass, "MathPass.csv")
write.csv(MathFTpass, "MathFTPass.csv")
library(gmodels)
EnglishFirst<-CrossTable(Placement$Col_Eng_Term, Placement$EnglishStart, chisq = FALSE, prop.r = TRUE, prop.c = FALSE, expected = FALSE, prop.t = FALSE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## |-------------------------|
##
##
## Total Observations in Table: 4995
##
##
## | Placement$EnglishStart
## Placement$Col_Eng_Term | 0 | 1 | Row Total |
## -----------------------|-----------|-----------|-----------|
## 10/FA | 149 | 112 | 261 |
## | 0.348 | 0.415 | |
## | 0.571 | 0.429 | 0.052 |
## -----------------------|-----------|-----------|-----------|
## 11/FA | 148 | 105 | 253 |
## | 0.783 | 0.934 | |
## | 0.585 | 0.415 | 0.051 |
## -----------------------|-----------|-----------|-----------|
## 11/SP | 144 | 25 | 169 |
## | 29.498 | 35.183 | |
## | 0.852 | 0.148 | 0.034 |
## -----------------------|-----------|-----------|-----------|
## 12/FA | 120 | 105 | 225 |
## | 0.047 | 0.056 | |
## | 0.533 | 0.467 | 0.045 |
## -----------------------|-----------|-----------|-----------|
## 12/SP | 132 | 27 | 159 |
## | 23.951 | 28.566 | |
## | 0.830 | 0.170 | 0.032 |
## -----------------------|-----------|-----------|-----------|
## 13/FA | 110 | 109 | 219 |
## | 0.699 | 0.833 | |
## | 0.502 | 0.498 | 0.044 |
## -----------------------|-----------|-----------|-----------|
## 13/SP | 130 | 21 | 151 |
## | 27.893 | 33.268 | |
## | 0.861 | 0.139 | 0.030 |
## -----------------------|-----------|-----------|-----------|
## 14/FA | 117 | 137 | 254 |
## | 3.241 | 3.866 | |
## | 0.461 | 0.539 | 0.051 |
## -----------------------|-----------|-----------|-----------|
## 14/SP | 114 | 16 | 130 |
## | 26.499 | 31.605 | |
## | 0.877 | 0.123 | 0.026 |
## -----------------------|-----------|-----------|-----------|
## 15/FA | 145 | 221 | 366 |
## | 14.692 | 17.524 | |
## | 0.396 | 0.604 | 0.073 |
## -----------------------|-----------|-----------|-----------|
## 15/SP | 127 | 35 | 162 |
## | 17.156 | 20.462 | |
## | 0.784 | 0.216 | 0.032 |
## -----------------------|-----------|-----------|-----------|
## 16/FA | 126 | 232 | 358 |
## | 24.259 | 28.934 | |
## | 0.352 | 0.648 | 0.072 |
## -----------------------|-----------|-----------|-----------|
## 16/SP | 162 | 37 | 199 |
## | 26.695 | 31.840 | |
## | 0.814 | 0.186 | 0.040 |
## -----------------------|-----------|-----------|-----------|
## 17/FA | 131 | 212 | 343 |
## | 16.553 | 19.743 | |
## | 0.382 | 0.618 | 0.069 |
## -----------------------|-----------|-----------|-----------|
## 17/SP | 122 | 42 | 164 |
## | 12.055 | 14.378 | |
## | 0.744 | 0.256 | 0.033 |
## -----------------------|-----------|-----------|-----------|
## 18/FA | 108 | 186 | 294 |
## | 16.856 | 20.105 | |
## | 0.367 | 0.633 | 0.059 |
## -----------------------|-----------|-----------|-----------|
## 18/SP | 121 | 26 | 147 |
## | 21.064 | 25.124 | |
## | 0.823 | 0.177 | 0.029 |
## -----------------------|-----------|-----------|-----------|
## 19/FA | 98 | 226 | 324 |
## | 34.732 | 41.426 | |
## | 0.302 | 0.698 | 0.065 |
## -----------------------|-----------|-----------|-----------|
## 19/SP | 120 | 30 | 150 |
## | 18.080 | 21.565 | |
## | 0.800 | 0.200 | 0.030 |
## -----------------------|-----------|-----------|-----------|
## 20/FA | 69 | 169 | 238 |
## | 28.235 | 33.676 | |
## | 0.290 | 0.710 | 0.048 |
## -----------------------|-----------|-----------|-----------|
## 20/SP | 95 | 30 | 125 |
## | 10.727 | 12.795 | |
## | 0.760 | 0.240 | 0.025 |
## -----------------------|-----------|-----------|-----------|
## 21/FA | 48 | 144 | 192 |
## | 30.498 | 36.376 | |
## | 0.250 | 0.750 | 0.038 |
## -----------------------|-----------|-----------|-----------|
## 21/SP | 81 | 31 | 112 |
## | 6.617 | 7.893 | |
## | 0.723 | 0.277 | 0.022 |
## -----------------------|-----------|-----------|-----------|
## Column Total | 2717 | 2278 | 4995 |
## -----------------------|-----------|-----------|-----------|
##
##
EnglishPass<-CrossTable(EnglC_Term$Col_Eng_Term, EnglC_Term$College_English_Pass, chisq = FALSE, prop.r = TRUE, prop.c = FALSE, expected = FALSE, prop.t = FALSE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## |-------------------------|
##
##
## Total Observations in Table: 3137
##
##
## | EnglC_Term$College_English_Pass
## EnglC_Term$Col_Eng_Term | 0 | 1 | Row Total |
## ------------------------|-----------|-----------|-----------|
## 10/FA | 15 | 246 | 261 |
## | 7.464 | 0.968 | |
## | 0.057 | 0.943 | 0.083 |
## ------------------------|-----------|-----------|-----------|
## 11/FA | 20 | 234 | 254 |
## | 2.872 | 0.372 | |
## | 0.079 | 0.921 | 0.081 |
## ------------------------|-----------|-----------|-----------|
## 12/FA | 23 | 202 | 225 |
## | 0.308 | 0.040 | |
## | 0.102 | 0.898 | 0.072 |
## ------------------------|-----------|-----------|-----------|
## 13/FA | 20 | 199 | 219 |
## | 1.048 | 0.136 | |
## | 0.091 | 0.909 | 0.070 |
## ------------------------|-----------|-----------|-----------|
## 14/FA | 18 | 236 | 254 |
## | 4.264 | 0.553 | |
## | 0.071 | 0.929 | 0.081 |
## ------------------------|-----------|-----------|-----------|
## 15/FA | 54 | 312 | 366 |
## | 3.427 | 0.444 | |
## | 0.148 | 0.852 | 0.117 |
## ------------------------|-----------|-----------|-----------|
## 16/FA | 51 | 307 | 358 |
## | 2.393 | 0.310 | |
## | 0.142 | 0.858 | 0.114 |
## ------------------------|-----------|-----------|-----------|
## 17/FA | 44 | 299 | 343 |
## | 0.546 | 0.071 | |
## | 0.128 | 0.872 | 0.109 |
## ------------------------|-----------|-----------|-----------|
## 18/FA | 36 | 258 | 294 |
## | 0.151 | 0.020 | |
## | 0.122 | 0.878 | 0.094 |
## ------------------------|-----------|-----------|-----------|
## 19/FA | 35 | 290 | 325 |
## | 0.141 | 0.018 | |
## | 0.108 | 0.892 | 0.104 |
## ------------------------|-----------|-----------|-----------|
## 20/FA | 44 | 194 | 238 |
## | 10.195 | 1.322 | |
## | 0.185 | 0.815 | 0.076 |
## ------------------------|-----------|-----------|-----------|
## Column Total | 360 | 2777 | 3137 |
## ------------------------|-----------|-----------|-----------|
##
##
EnglishFT<- CrossTable(EnglC_Term_First$Col_Eng_Term, EnglC_Term_First$College_English_Pass, chisq = FALSE, prop.r = TRUE, prop.c = FALSE, expected = FALSE, prop.t = FALSE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## |-------------------------|
##
##
## Total Observations in Table: 1814
##
##
## | EnglC_Term_First$College_English_Pass
## EnglC_Term_First$Col_Eng_Term | 0 | 1 | Row Total |
## ------------------------------|-----------|-----------|-----------|
## 10/FA | 8 | 104 | 112 |
## | 2.096 | 0.282 | |
## | 0.071 | 0.929 | 0.062 |
## ------------------------------|-----------|-----------|-----------|
## 11/FA | 9 | 96 | 105 |
## | 0.954 | 0.128 | |
## | 0.086 | 0.914 | 0.058 |
## ------------------------------|-----------|-----------|-----------|
## 12/FA | 14 | 91 | 105 |
## | 0.194 | 0.026 | |
## | 0.133 | 0.867 | 0.058 |
## ------------------------------|-----------|-----------|-----------|
## 13/FA | 11 | 98 | 109 |
## | 0.285 | 0.038 | |
## | 0.101 | 0.899 | 0.060 |
## ------------------------------|-----------|-----------|-----------|
## 14/FA | 8 | 129 | 137 |
## | 4.179 | 0.562 | |
## | 0.058 | 0.942 | 0.076 |
## ------------------------------|-----------|-----------|-----------|
## 15/FA | 29 | 192 | 221 |
## | 0.301 | 0.040 | |
## | 0.131 | 0.869 | 0.122 |
## ------------------------------|-----------|-----------|-----------|
## 16/FA | 26 | 206 | 232 |
## | 0.082 | 0.011 | |
## | 0.112 | 0.888 | 0.128 |
## ------------------------------|-----------|-----------|-----------|
## 17/FA | 26 | 186 | 212 |
## | 0.030 | 0.004 | |
## | 0.123 | 0.877 | 0.117 |
## ------------------------------|-----------|-----------|-----------|
## 18/FA | 25 | 161 | 186 |
## | 0.396 | 0.053 | |
## | 0.134 | 0.866 | 0.103 |
## ------------------------------|-----------|-----------|-----------|
## 19/FA | 25 | 201 | 226 |
## | 0.119 | 0.016 | |
## | 0.111 | 0.889 | 0.125 |
## ------------------------------|-----------|-----------|-----------|
## 20/FA | 34 | 135 | 169 |
## | 9.743 | 1.310 | |
## | 0.201 | 0.799 | 0.093 |
## ------------------------------|-----------|-----------|-----------|
## Column Total | 215 | 1599 | 1814 |
## ------------------------------|-----------|-----------|-----------|
##
##
write.csv(EnglishFirst,"englishFirst.csv")
write.csv(EnglishPass, "englishPass.csv")
write.csv(EnglishFT, "englFTPass.csv")
mathdescribe<- describeBy(MathC_Term, MathC_Term$Col_Math_Term)
mathdescribe<- do.call("rbind", mathdescribe)
mathdescribe
## vars n mean sd median trimmed mad min max
## 10/FA.Col_Math_Term* 1 180 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## 10/FA.College_Math_Pass 2 180 0.87 0.33 1.00 0.97 0.00 0.00 1.00
## 10/FA.College_Math_Grade* 3 180 2.32 1.17 2.00 2.17 1.48 1.00 5.00
## 10/FA.MathStart 4 179 0.22 0.41 0.00 0.15 0.00 0.00 1.00
## 10/FA.HSGPA 5 130 3.14 0.59 3.23 3.20 0.57 0.86 4.00
## 10/FA.ACT_Math 6 2 26.50 0.71 26.50 26.50 0.74 26.00 27.00
## 10/FA.ALEKS 7 0 NaN NA NA NaN NA Inf -Inf
## 11/FA.Col_Math_Term* 1 208 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## 11/FA.College_Math_Pass 2 208 0.75 0.43 1.00 0.81 0.00 0.00 1.00
## 11/FA.College_Math_Grade* 3 208 2.73 1.39 3.00 2.66 1.48 1.00 5.00
## 11/FA.MathStart 4 205 0.21 0.41 0.00 0.14 0.00 0.00 1.00
## 11/FA.HSGPA 5 154 3.19 0.54 3.28 3.24 0.50 1.59 4.00
## 11/FA.ACT_Math 6 0 NaN NA NA NaN NA Inf -Inf
## 11/FA.ALEKS 7 1 28.00 NA 28.00 28.00 0.00 28.00 28.00
## 12/FA.Col_Math_Term* 1 171 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## 12/FA.College_Math_Pass 2 171 0.73 0.45 1.00 0.78 0.00 0.00 1.00
## 12/FA.College_Math_Grade* 3 171 2.74 1.30 3.00 2.68 1.48 1.00 5.00
## 12/FA.MathStart 4 171 0.21 0.41 0.00 0.14 0.00 0.00 1.00
## 12/FA.HSGPA 5 135 3.11 0.52 3.20 3.14 0.62 1.82 4.00
## 12/FA.ACT_Math 6 1 26.00 NA 26.00 26.00 0.00 26.00 26.00
## 12/FA.ALEKS 7 1 36.00 NA 36.00 36.00 0.00 36.00 36.00
## 13/FA.Col_Math_Term* 1 259 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## 13/FA.College_Math_Pass 2 259 0.81 0.40 1.00 0.88 0.00 0.00 1.00
## 13/FA.College_Math_Grade* 3 259 2.56 1.30 2.00 2.45 1.48 1.00 5.00
## 13/FA.MathStart 4 258 0.25 0.43 0.00 0.19 0.00 0.00 1.00
## 13/FA.HSGPA 5 201 3.10 0.55 3.16 3.13 0.61 1.34 4.00
## 13/FA.ACT_Math 6 2 21.50 3.54 21.50 21.50 3.71 19.00 24.00
## 13/FA.ALEKS 7 1 39.00 NA 39.00 39.00 0.00 39.00 39.00
## 14/FA.Col_Math_Term* 1 242 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## 14/FA.College_Math_Pass 2 242 0.87 0.34 1.00 0.96 0.00 0.00 1.00
## 14/FA.College_Math_Grade* 3 242 2.29 1.19 2.00 2.15 1.48 1.00 5.00
## 14/FA.MathStart 4 242 0.26 0.44 0.00 0.20 0.00 0.00 1.00
## 14/FA.HSGPA 5 200 3.21 0.52 3.28 3.24 0.52 1.71 4.09
## 14/FA.ACT_Math 6 71 23.34 3.06 24.00 23.49 2.97 15.00 31.00
## 14/FA.ALEKS 7 0 NaN NA NA NaN NA Inf -Inf
## 15/FA.Col_Math_Term* 1 342 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## 15/FA.College_Math_Pass 2 342 0.85 0.36 1.00 0.93 0.00 0.00 1.00
## 15/FA.College_Math_Grade* 3 342 2.32 1.25 2.00 2.15 1.48 1.00 5.00
## 15/FA.MathStart 4 342 0.23 0.42 0.00 0.16 0.00 0.00 1.00
## 15/FA.HSGPA 5 281 3.00 0.56 3.04 3.02 0.59 1.41 4.81
## 15/FA.ACT_Math 6 108 20.64 3.61 21.00 20.61 4.45 13.00 28.00
## 15/FA.ALEKS 7 3 38.33 1.53 38.00 38.33 1.48 37.00 40.00
## 16/FA.Col_Math_Term* 1 318 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## 16/FA.College_Math_Pass 2 318 0.83 0.38 1.00 0.91 0.00 0.00 1.00
## 16/FA.College_Math_Grade* 3 318 2.35 1.33 2.00 2.19 1.48 1.00 5.00
## 16/FA.MathStart 4 318 0.46 0.50 0.00 0.45 0.00 0.00 1.00
## 16/FA.HSGPA 5 273 3.19 0.49 3.26 3.23 0.45 1.29 4.00
## 16/FA.ACT_Math 6 171 20.09 3.31 20.00 19.91 4.45 15.00 30.00
## 16/FA.ALEKS 7 48 48.98 17.73 47.50 48.65 20.02 14.00 81.00
## 17/FA.Col_Math_Term* 1 269 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## 17/FA.College_Math_Pass 2 269 0.85 0.36 1.00 0.93 0.00 0.00 1.00
## 17/FA.College_Math_Grade* 3 269 2.24 1.28 2.00 2.06 1.48 1.00 5.00
## 17/FA.MathStart 4 269 0.44 0.50 0.00 0.42 0.00 0.00 1.00
## 17/FA.HSGPA 5 242 3.21 0.47 3.28 3.25 0.43 1.62 4.02
## 17/FA.ACT_Math 6 177 20.89 3.35 21.00 20.82 4.45 15.00 29.00
## 17/FA.ALEKS 7 44 46.55 17.38 47.00 45.89 14.83 14.00 94.00
## 18/FA.Col_Math_Term* 1 234 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## 18/FA.College_Math_Pass 2 234 0.84 0.37 1.00 0.93 0.00 0.00 1.00
## 18/FA.College_Math_Grade* 3 234 2.28 1.25 2.00 2.11 1.48 1.00 5.00
## 18/FA.MathStart 4 234 0.50 0.50 0.00 0.49 0.00 0.00 1.00
## 18/FA.HSGPA 5 219 3.23 0.46 3.27 3.26 0.41 1.66 4.00
## 18/FA.ACT_Math 6 143 20.53 3.76 20.00 20.41 4.45 14.00 28.00
## 18/FA.ALEKS 7 27 41.70 19.03 46.00 42.00 14.83 5.00 82.00
## 19/FA.Col_Math_Term* 1 241 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## 19/FA.College_Math_Pass 2 241 0.78 0.42 1.00 0.85 0.00 0.00 1.00
## 19/FA.College_Math_Grade* 3 241 2.45 1.42 2.00 2.31 1.48 1.00 5.00
## 19/FA.MathStart 4 241 0.54 0.50 1.00 0.55 0.00 0.00 1.00
## 19/FA.HSGPA 5 234 3.22 0.51 3.30 3.27 0.39 1.26 4.02
## 19/FA.ACT_Math 6 144 20.58 4.12 20.00 20.33 4.45 13.00 33.00
## 19/FA.ALEKS 7 41 30.20 14.52 29.00 28.91 13.34 7.00 67.00
## 20/FA.Col_Math_Term* 1 351 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## 20/FA.College_Math_Pass 2 351 0.77 0.42 1.00 0.84 0.00 0.00 1.00
## 20/FA.College_Math_Grade* 3 351 2.56 1.43 2.00 2.45 1.48 1.00 5.00
## 20/FA.MathStart 4 351 0.44 0.50 0.00 0.42 0.00 0.00 1.00
## 20/FA.HSGPA 5 324 3.02 0.60 3.04 3.05 0.72 1.28 4.20
## 20/FA.ACT_Math 6 189 19.20 3.81 18.00 18.86 2.97 11.00 32.00
## 20/FA.ALEKS 7 76 20.50 14.76 17.00 18.34 11.86 4.00 86.00
## range skew kurtosis se
## 10/FA.Col_Math_Term* 0.00 NaN NaN 0.00
## 10/FA.College_Math_Pass 1.00 -2.21 2.91 0.02
## 10/FA.College_Math_Grade* 4.00 0.79 -0.01 0.09
## 10/FA.MathStart 1.00 1.36 -0.16 0.03
## 10/FA.HSGPA 3.14 -1.00 1.16 0.05
## 10/FA.ACT_Math 1.00 0.00 -2.75 0.50
## 10/FA.ALEKS -Inf NA NA NA
## 11/FA.Col_Math_Term* 0.00 NaN NaN 0.00
## 11/FA.College_Math_Pass 1.00 -1.15 -0.69 0.03
## 11/FA.College_Math_Grade* 4.00 0.41 -1.03 0.10
## 11/FA.MathStart 1.00 1.42 0.00 0.03
## 11/FA.HSGPA 2.41 -0.71 -0.17 0.04
## 11/FA.ACT_Math -Inf NA NA NA
## 11/FA.ALEKS 0.00 NA NA NA
## 12/FA.Col_Math_Term* 0.00 NaN NaN 0.00
## 12/FA.College_Math_Pass 1.00 -1.00 -1.01 0.03
## 12/FA.College_Math_Grade* 4.00 0.30 -0.97 0.10
## 12/FA.MathStart 1.00 1.41 -0.02 0.03
## 12/FA.HSGPA 2.18 -0.38 -0.66 0.05
## 12/FA.ACT_Math 0.00 NA NA NA
## 12/FA.ALEKS 0.00 NA NA NA
## 13/FA.Col_Math_Term* 0.00 NaN NaN 0.00
## 13/FA.College_Math_Pass 1.00 -1.55 0.39 0.02
## 13/FA.College_Math_Grade* 4.00 0.59 -0.63 0.08
## 13/FA.MathStart 1.00 1.14 -0.71 0.03
## 13/FA.HSGPA 2.66 -0.40 -0.45 0.04
## 13/FA.ACT_Math 5.00 0.00 -2.75 2.50
## 13/FA.ALEKS 0.00 NA NA NA
## 14/FA.Col_Math_Term* 0.00 NaN NaN 0.00
## 14/FA.College_Math_Pass 1.00 -2.16 2.67 0.02
## 14/FA.College_Math_Grade* 4.00 0.65 -0.36 0.08
## 14/FA.MathStart 1.00 1.09 -0.82 0.03
## 14/FA.HSGPA 2.38 -0.56 -0.32 0.04
## 14/FA.ACT_Math 16.00 -0.48 0.20 0.36
## 14/FA.ALEKS -Inf NA NA NA
## 15/FA.Col_Math_Term* 0.00 NaN NaN 0.00
## 15/FA.College_Math_Pass 1.00 -1.90 1.61 0.02
## 15/FA.College_Math_Grade* 4.00 0.79 -0.28 0.07
## 15/FA.MathStart 1.00 1.29 -0.34 0.02
## 15/FA.HSGPA 3.40 -0.22 -0.14 0.03
## 15/FA.ACT_Math 15.00 -0.04 -1.01 0.35
## 15/FA.ALEKS 3.00 0.21 -2.33 0.88
## 16/FA.Col_Math_Term* 0.00 NaN NaN 0.00
## 16/FA.College_Math_Pass 1.00 -1.72 0.97 0.02
## 16/FA.College_Math_Grade* 4.00 0.75 -0.52 0.07
## 16/FA.MathStart 1.00 0.18 -1.98 0.03
## 16/FA.HSGPA 2.71 -0.83 0.85 0.03
## 16/FA.ACT_Math 15.00 0.46 -0.50 0.25
## 16/FA.ALEKS 67.00 0.22 -0.93 2.56
## 17/FA.Col_Math_Term* 0.00 NaN NaN 0.00
## 17/FA.College_Math_Pass 1.00 -1.92 1.71 0.02
## 17/FA.College_Math_Grade* 4.00 0.91 -0.16 0.08
## 17/FA.MathStart 1.00 0.25 -1.95 0.03
## 17/FA.HSGPA 2.40 -0.73 0.48 0.03
## 17/FA.ACT_Math 14.00 0.16 -0.94 0.25
## 17/FA.ALEKS 80.00 0.34 0.17 2.62
## 18/FA.Col_Math_Term* 0.00 NaN NaN 0.00
## 18/FA.College_Math_Pass 1.00 -1.86 1.47 0.02
## 18/FA.College_Math_Grade* 4.00 0.78 -0.35 0.08
## 18/FA.MathStart 1.00 0.02 -2.01 0.03
## 18/FA.HSGPA 2.34 -0.66 0.50 0.03
## 18/FA.ACT_Math 14.00 0.18 -1.24 0.31
## 18/FA.ALEKS 77.00 -0.14 -0.60 3.66
## 19/FA.Col_Math_Term* 0.00 NaN NaN 0.00
## 19/FA.College_Math_Pass 1.00 -1.34 -0.19 0.03
## 19/FA.College_Math_Grade* 4.00 0.66 -0.86 0.09
## 19/FA.MathStart 1.00 -0.17 -1.98 0.03
## 19/FA.HSGPA 2.77 -0.99 1.30 0.03
## 19/FA.ACT_Math 20.00 0.49 -0.53 0.34
## 19/FA.ALEKS 60.00 0.77 0.12 2.27
## 20/FA.Col_Math_Term* 0.00 NaN NaN 0.00
## 20/FA.College_Math_Pass 1.00 -1.29 -0.33 0.02
## 20/FA.College_Math_Grade* 4.00 0.54 -0.97 0.08
## 20/FA.MathStart 1.00 0.26 -1.94 0.03
## 20/FA.HSGPA 2.92 -0.29 -0.69 0.03
## 20/FA.ACT_Math 21.00 0.79 0.42 0.28
## 20/FA.ALEKS 82.00 1.95 5.14 1.69
write.csv(mathdescribe, "mathdescribe.csv")
engldescribe<- describeBy(EnglC_Term, EnglC_Term$Col_Eng_Term)
engldescribe<- do.call("rbind", engldescribe)
engldescribe
## vars n mean sd median trimmed mad min
## 10/FA.Col_Eng_Term* 1 261 1.00 0.00 1.00 1.00 0.00 1.00
## 10/FA.College_English_Pass 2 261 0.94 0.23 1.00 1.00 0.00 0.00
## 10/FA.College_English_Grade* 3 261 1.94 1.05 2.00 1.80 1.48 1.00
## 10/FA.EnglishStart 4 261 0.43 0.50 0.00 0.41 0.00 0.00
## 10/FA.HSGPA 5 229 3.04 0.52 3.10 3.06 0.57 1.57
## 10/FA.ACT_Engl 6 3 19.33 2.08 20.00 19.33 1.48 17.00
## 10/FA.ACT_Reading 7 3 19.67 3.06 19.00 19.67 2.97 17.00
## 10/FA.McCann_R 8 0 NaN NA NA NaN NA Inf
## 10/FA.McCann_W 9 0 NaN NA NA NaN NA Inf
## 11/FA.Col_Eng_Term* 1 254 1.00 0.00 1.00 1.00 0.00 1.00
## 11/FA.College_English_Pass 2 254 0.92 0.27 1.00 1.00 0.00 0.00
## 11/FA.College_English_Grade* 3 254 1.93 1.07 2.00 1.75 1.48 1.00
## 11/FA.EnglishStart 4 253 0.42 0.49 0.00 0.39 0.00 0.00
## 11/FA.HSGPA 5 230 3.13 0.52 3.18 3.15 0.50 1.66
## 11/FA.ACT_Engl 6 2 15.50 2.12 15.50 15.50 2.22 14.00
## 11/FA.ACT_Reading 7 2 18.00 1.41 18.00 18.00 1.48 17.00
## 11/FA.McCann_R 8 3 114.67 4.62 112.00 114.67 0.00 112.00
## 11/FA.McCann_W 9 1 6.00 NA 6.00 6.00 0.00 6.00
## 12/FA.Col_Eng_Term* 1 225 1.00 0.00 1.00 1.00 0.00 1.00
## 12/FA.College_English_Pass 2 225 0.90 0.30 1.00 0.99 0.00 0.00
## 12/FA.College_English_Grade* 3 225 2.09 1.16 2.00 1.90 1.48 1.00
## 12/FA.EnglishStart 4 225 0.47 0.50 0.00 0.46 0.00 0.00
## 12/FA.HSGPA 5 189 3.09 0.51 3.13 3.11 0.54 1.62
## 12/FA.ACT_Engl 6 3 20.33 2.89 22.00 20.33 0.00 17.00
## 12/FA.ACT_Reading 7 3 21.67 1.15 21.00 21.67 0.00 21.00
## 12/FA.McCann_R 8 1 112.00 NA 112.00 112.00 0.00 112.00
## 12/FA.McCann_W 9 0 NaN NA NA NaN NA Inf
## 13/FA.Col_Eng_Term* 1 219 1.00 0.00 1.00 1.00 0.00 1.00
## 13/FA.College_English_Pass 2 219 0.91 0.29 1.00 1.00 0.00 0.00
## 13/FA.College_English_Grade* 3 219 2.10 1.13 2.00 1.90 1.48 1.00
## 13/FA.EnglishStart 4 219 0.50 0.50 0.00 0.50 0.00 0.00
## 13/FA.HSGPA 5 196 3.10 0.51 3.19 3.12 0.58 1.87
## 13/FA.ACT_Engl 6 15 23.73 4.61 22.00 23.46 2.97 18.00
## 13/FA.ACT_Reading 7 15 24.87 4.05 23.00 24.46 1.48 19.00
## 13/FA.McCann_R 8 1 150.00 NA 150.00 150.00 0.00 150.00
## 13/FA.McCann_W 9 0 NaN NA NA NaN NA Inf
## 14/FA.Col_Eng_Term* 1 254 1.00 0.00 1.00 1.00 0.00 1.00
## 14/FA.College_English_Pass 2 254 0.93 0.26 1.00 1.00 0.00 0.00
## 14/FA.College_English_Grade* 3 254 1.93 1.05 2.00 1.76 1.48 1.00
## 14/FA.EnglishStart 4 254 0.54 0.50 1.00 0.55 0.00 0.00
## 14/FA.HSGPA 5 240 3.04 0.59 3.13 3.06 0.63 1.51
## 14/FA.ACT_Engl 6 136 21.12 3.72 21.00 21.01 2.97 13.00
## 14/FA.ACT_Reading 7 137 21.55 4.09 21.00 21.39 4.45 12.00
## 14/FA.McCann_R 8 0 NaN NA NA NaN NA Inf
## 14/FA.McCann_W 9 0 NaN NA NA NaN NA Inf
## 15/FA.Col_Eng_Term* 1 366 1.00 0.00 1.00 1.00 0.00 1.00
## 15/FA.College_English_Pass 2 366 0.85 0.36 1.00 0.94 0.00 0.00
## 15/FA.College_English_Grade* 3 366 2.25 1.29 2.00 2.06 1.48 1.00
## 15/FA.EnglishStart 4 366 0.60 0.49 1.00 0.63 0.00 0.00
## 15/FA.HSGPA 5 318 2.94 0.56 2.97 2.96 0.63 1.50
## 15/FA.ACT_Engl 6 211 19.76 4.21 20.00 19.66 2.97 11.00
## 15/FA.ACT_Reading 7 211 21.21 4.31 21.00 21.07 4.45 10.00
## 15/FA.McCann_R 8 2 109.50 4.95 109.50 109.50 5.19 106.00
## 15/FA.McCann_W 9 1 4.00 NA 4.00 4.00 0.00 4.00
## 16/FA.Col_Eng_Term* 1 358 1.00 0.00 1.00 1.00 0.00 1.00
## 16/FA.College_English_Pass 2 358 0.86 0.35 1.00 0.94 0.00 0.00
## 16/FA.College_English_Grade* 3 358 2.25 1.30 2.00 2.07 1.48 1.00
## 16/FA.EnglishStart 4 358 0.65 0.48 1.00 0.68 0.00 0.00
## 16/FA.HSGPA 5 315 2.93 0.55 2.97 2.95 0.55 1.00
## 16/FA.ACT_Engl 6 227 18.97 4.19 19.00 18.94 4.45 7.00
## 16/FA.ACT_Reading 7 228 20.41 4.40 20.00 20.27 4.45 11.00
## 16/FA.McCann_R 8 121 101.45 11.46 100.00 100.87 8.90 74.00
## 16/FA.McCann_W 9 121 4.88 0.70 5.00 4.86 0.00 3.00
## 17/FA.Col_Eng_Term* 1 343 1.00 0.00 1.00 1.00 0.00 1.00
## 17/FA.College_English_Pass 2 343 0.87 0.33 1.00 0.96 0.00 0.00
## 17/FA.College_English_Grade* 3 343 2.19 1.24 2.00 1.99 1.48 1.00
## 17/FA.EnglishStart 4 343 0.62 0.49 1.00 0.65 0.00 0.00
## 17/FA.HSGPA 5 321 2.92 0.53 2.98 2.93 0.60 1.34
## 17/FA.ACT_Engl 6 229 18.70 4.65 19.00 18.56 4.45 8.00
## 17/FA.ACT_Reading 7 229 19.96 4.81 20.00 19.70 4.45 10.00
## 17/FA.McCann_R 8 167 99.47 12.91 99.00 99.12 13.34 64.00
## 17/FA.McCann_W 9 161 4.76 0.74 5.00 4.75 0.00 3.00
## 18/FA.Col_Eng_Term* 1 294 1.00 0.00 1.00 1.00 0.00 1.00
## 18/FA.College_English_Pass 2 294 0.88 0.33 1.00 0.97 0.00 0.00
## 18/FA.College_English_Grade* 3 294 2.19 1.24 2.00 1.99 1.48 1.00
## 18/FA.EnglishStart 4 294 0.63 0.48 1.00 0.67 0.00 0.00
## 18/FA.HSGPA 5 272 2.90 0.54 2.92 2.90 0.54 1.44
## 18/FA.ACT_Engl 6 173 18.75 4.66 19.00 18.71 4.45 2.00
## 18/FA.ACT_Reading 7 170 20.72 4.93 21.00 20.47 4.45 9.00
## 18/FA.McCann_R 8 159 101.03 13.91 100.00 100.18 10.38 64.00
## 18/FA.McCann_W 9 159 4.87 0.86 5.00 4.88 1.48 3.00
## 19/FA.Col_Eng_Term* 1 325 1.00 0.00 1.00 1.00 0.00 1.00
## 19/FA.College_English_Pass 2 325 0.89 0.31 1.00 0.99 0.00 0.00
## 19/FA.College_English_Grade* 3 325 2.16 1.17 2.00 1.97 1.48 1.00
## 19/FA.EnglishStart 4 324 0.70 0.46 1.00 0.75 0.00 0.00
## 19/FA.HSGPA 5 308 3.02 0.55 3.04 3.03 0.57 1.42
## 19/FA.ACT_Engl 6 191 18.52 4.39 18.00 18.33 4.45 10.00
## 19/FA.ACT_Reading 7 191 20.45 4.99 20.00 20.06 4.45 12.00
## 19/FA.McCann_R 8 83 98.31 15.12 98.00 97.97 11.86 50.00
## 19/FA.McCann_W 9 34 4.44 1.13 5.00 4.50 1.48 2.00
## 20/FA.Col_Eng_Term* 1 238 1.00 0.00 1.00 1.00 0.00 1.00
## 20/FA.College_English_Pass 2 238 0.82 0.39 1.00 0.89 0.00 0.00
## 20/FA.College_English_Grade* 3 238 2.35 1.39 2.00 2.19 1.48 1.00
## 20/FA.EnglishStart 4 238 0.71 0.45 1.00 0.76 0.00 0.00
## 20/FA.HSGPA 5 228 2.94 0.56 2.97 2.95 0.55 1.50
## 20/FA.ACT_Engl 6 134 18.09 4.14 18.00 17.96 4.45 11.00
## 20/FA.ACT_Reading 7 134 19.64 4.14 20.00 19.55 4.45 10.00
## 20/FA.McCann_R 8 27 100.37 15.78 100.00 100.91 11.86 61.00
## 20/FA.McCann_W 9 16 4.38 0.96 4.50 4.43 0.74 2.00
## max range skew kurtosis se
## 10/FA.Col_Eng_Term* 1.00 0.00 NaN NaN 0.00
## 10/FA.College_English_Pass 1.00 1.00 -3.78 12.34 0.01
## 10/FA.College_English_Grade* 5.00 4.00 1.02 0.62 0.06
## 10/FA.EnglishStart 1.00 1.00 0.28 -1.93 0.03
## 10/FA.HSGPA 4.00 2.43 -0.35 -0.46 0.03
## 10/FA.ACT_Engl 21.00 4.00 -0.29 -2.33 1.20
## 10/FA.ACT_Reading 23.00 6.00 0.21 -2.33 1.76
## 10/FA.McCann_R -Inf -Inf NA NA NA
## 10/FA.McCann_W -Inf -Inf NA NA NA
## 11/FA.Col_Eng_Term* 1.00 0.00 NaN NaN 0.00
## 11/FA.College_English_Pass 1.00 1.00 -3.11 7.70 0.02
## 11/FA.College_English_Grade* 5.00 4.00 1.31 1.37 0.07
## 11/FA.EnglishStart 1.00 1.00 0.34 -1.89 0.03
## 11/FA.HSGPA 4.00 2.34 -0.45 -0.25 0.03
## 11/FA.ACT_Engl 17.00 3.00 0.00 -2.75 1.50
## 11/FA.ACT_Reading 19.00 2.00 0.00 -2.75 1.00
## 11/FA.McCann_R 120.00 8.00 0.38 -2.33 2.67
## 11/FA.McCann_W 6.00 0.00 NA NA NA
## 12/FA.Col_Eng_Term* 1.00 0.00 NaN NaN 0.00
## 12/FA.College_English_Pass 1.00 1.00 -2.61 4.83 0.02
## 12/FA.College_English_Grade* 5.00 4.00 1.10 0.58 0.08
## 12/FA.EnglishStart 1.00 1.00 0.13 -1.99 0.03
## 12/FA.HSGPA 4.00 2.38 -0.35 -0.54 0.04
## 12/FA.ACT_Engl 22.00 5.00 -0.38 -2.33 1.67
## 12/FA.ACT_Reading 23.00 2.00 0.38 -2.33 0.67
## 12/FA.McCann_R 112.00 0.00 NA NA NA
## 12/FA.McCann_W -Inf -Inf NA NA NA
## 13/FA.Col_Eng_Term* 1.00 0.00 NaN NaN 0.00
## 13/FA.College_English_Pass 1.00 1.00 -2.82 5.97 0.02
## 13/FA.College_English_Grade* 5.00 4.00 1.23 1.06 0.08
## 13/FA.EnglishStart 1.00 1.00 0.01 -2.01 0.03
## 13/FA.HSGPA 4.00 2.13 -0.39 -0.71 0.04
## 13/FA.ACT_Engl 33.00 15.00 0.92 -0.43 1.19
## 13/FA.ACT_Reading 36.00 17.00 1.35 1.48 1.05
## 13/FA.McCann_R 150.00 0.00 NA NA NA
## 13/FA.McCann_W -Inf -Inf NA NA NA
## 14/FA.Col_Eng_Term* 1.00 0.00 NaN NaN 0.00
## 14/FA.College_English_Pass 1.00 1.00 -3.33 9.09 0.02
## 14/FA.College_English_Grade* 5.00 4.00 1.29 1.41 0.07
## 14/FA.EnglishStart 1.00 1.00 -0.16 -1.98 0.03
## 14/FA.HSGPA 4.39 2.88 -0.28 -0.66 0.04
## 14/FA.ACT_Engl 35.00 22.00 0.56 1.45 0.32
## 14/FA.ACT_Reading 33.00 21.00 0.45 0.26 0.35
## 14/FA.McCann_R -Inf -Inf NA NA NA
## 14/FA.McCann_W -Inf -Inf NA NA NA
## 15/FA.Col_Eng_Term* 1.00 0.00 NaN NaN 0.00
## 15/FA.College_English_Pass 1.00 1.00 -1.98 1.92 0.02
## 15/FA.College_English_Grade* 6.00 5.00 0.99 0.03 0.07
## 15/FA.EnglishStart 1.00 1.00 -0.42 -1.83 0.03
## 15/FA.HSGPA 4.47 2.97 -0.19 -0.46 0.03
## 15/FA.ACT_Engl 32.00 21.00 0.21 0.03 0.29
## 15/FA.ACT_Reading 35.00 25.00 0.33 0.12 0.30
## 15/FA.McCann_R 113.00 7.00 0.00 -2.75 3.50
## 15/FA.McCann_W 4.00 0.00 NA NA NA
## 16/FA.Col_Eng_Term* 1.00 0.00 NaN NaN 0.00
## 16/FA.College_English_Pass 1.00 1.00 -2.04 2.16 0.02
## 16/FA.College_English_Grade* 5.00 4.00 0.91 -0.17 0.07
## 16/FA.EnglishStart 1.00 1.00 -0.62 -1.62 0.03
## 16/FA.HSGPA 4.00 3.00 -0.29 -0.16 0.03
## 16/FA.ACT_Engl 32.00 25.00 0.07 0.38 0.28
## 16/FA.ACT_Reading 31.00 20.00 0.25 -0.34 0.29
## 16/FA.McCann_R 150.00 76.00 0.78 2.10 1.04
## 16/FA.McCann_W 6.00 3.00 0.03 -0.68 0.06
## 17/FA.Col_Eng_Term* 1.00 0.00 NaN NaN 0.00
## 17/FA.College_English_Pass 1.00 1.00 -2.21 2.91 0.02
## 17/FA.College_English_Grade* 5.00 4.00 1.05 0.24 0.07
## 17/FA.EnglishStart 1.00 1.00 -0.48 -1.77 0.03
## 17/FA.HSGPA 4.00 2.66 -0.11 -0.62 0.03
## 17/FA.ACT_Engl 36.00 28.00 0.41 0.37 0.31
## 17/FA.ACT_Reading 36.00 26.00 0.51 0.14 0.32
## 17/FA.McCann_R 150.00 86.00 0.39 0.69 1.00
## 17/FA.McCann_W 6.00 3.00 -0.23 -0.20 0.06
## 18/FA.Col_Eng_Term* 1.00 0.00 NaN NaN 0.00
## 18/FA.College_English_Pass 1.00 1.00 -2.29 3.26 0.02
## 18/FA.College_English_Grade* 5.00 4.00 1.03 0.23 0.07
## 18/FA.EnglishStart 1.00 1.00 -0.55 -1.71 0.03
## 18/FA.HSGPA 4.00 2.56 -0.08 -0.55 0.03
## 18/FA.ACT_Engl 34.00 32.00 0.09 1.11 0.35
## 18/FA.ACT_Reading 35.00 26.00 0.43 0.01 0.38
## 18/FA.McCann_R 150.00 86.00 0.91 2.54 1.10
## 18/FA.McCann_W 6.00 3.00 -0.11 -0.99 0.07
## 19/FA.Col_Eng_Term* 1.00 0.00 NaN NaN 0.00
## 19/FA.College_English_Pass 1.00 1.00 -2.52 4.36 0.02
## 19/FA.College_English_Grade* 6.00 5.00 1.07 0.60 0.06
## 19/FA.EnglishStart 1.00 1.00 -0.86 -1.27 0.03
## 19/FA.HSGPA 4.01 2.59 -0.25 -0.47 0.03
## 19/FA.ACT_Engl 33.00 23.00 0.48 0.09 0.32
## 19/FA.ACT_Reading 35.00 23.00 0.66 -0.06 0.36
## 19/FA.McCann_R 150.00 100.00 0.47 2.64 1.66
## 19/FA.McCann_W 6.00 4.00 -0.40 -0.69 0.19
## 20/FA.Col_Eng_Term* 1.00 0.00 NaN NaN 0.00
## 20/FA.College_English_Pass 1.00 1.00 -1.61 0.61 0.03
## 20/FA.College_English_Grade* 5.00 4.00 0.84 -0.54 0.09
## 20/FA.EnglishStart 1.00 1.00 -0.92 -1.16 0.03
## 20/FA.HSGPA 4.00 2.50 -0.13 -0.52 0.04
## 20/FA.ACT_Engl 33.00 22.00 0.45 0.17 0.36
## 20/FA.ACT_Reading 32.00 22.00 0.26 -0.03 0.36
## 20/FA.McCann_R 130.00 69.00 -0.44 0.08 3.04
## 20/FA.McCann_W 6.00 4.00 -0.73 0.24 0.24
write.csv(engldescribe, "engdescrible.csv")
math_pass_tbl<- describeBy(MathC_Term, MathC_Term$College_Math_Pass)
math_pass_tbl<- do.call("rbind", math_pass_tbl)
math_pass_tbl
## vars n mean sd median trimmed mad min max
## 0.Col_Math_Term* 1 523 6.50 3.20 7.00 6.56 4.45 1.00 11.00
## 0.College_Math_Pass 2 523 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 0.College_Math_Grade* 3 523 1.69 0.46 2.00 1.73 0.00 1.00 2.00
## 0.MathStart 4 522 0.39 0.49 0.00 0.36 0.00 0.00 1.00
## 0.HSGPA 5 460 2.94 0.50 3.00 2.97 0.47 1.34 4.00
## 0.ACT_Math 6 201 19.93 3.66 19.00 19.83 4.45 12.00 27.00
## 0.ALEKS 7 49 32.08 19.23 27.00 29.95 13.34 5.00 94.00
## 1.Col_Math_Term* 1 2292 6.44 3.02 7.00 6.51 2.97 1.00 11.00
## 1.College_Math_Pass 2 2292 1.00 0.00 1.00 1.00 0.00 1.00 1.00
## 1.College_Math_Grade* 3 2292 1.91 0.81 2.00 1.89 1.48 1.00 3.00
## 1.MathStart 4 2288 0.34 0.47 0.00 0.30 0.00 0.00 1.00
## 1.HSGPA 5 1933 3.19 0.53 3.27 3.23 0.52 0.86 4.81
## 1.ACT_Math 6 807 20.65 3.75 20.00 20.49 4.45 11.00 33.00
## 1.ALEKS 7 193 36.10 20.03 34.00 35.03 23.72 4.00 86.00
## range skew kurtosis se
## 0.Col_Math_Term* 10.00 -0.07 -1.28 0.14
## 0.College_Math_Pass 0.00 NaN NaN 0.00
## 0.College_Math_Grade* 1.00 -0.80 -1.36 0.02
## 0.MathStart 1.00 0.47 -1.78 0.02
## 0.HSGPA 2.66 -0.40 -0.08 0.02
## 0.ACT_Math 15.00 0.16 -1.13 0.26
## 0.ALEKS 89.00 1.21 1.33 2.75
## 1.Col_Math_Term* 10.00 -0.15 -1.00 0.06
## 1.College_Math_Pass 0.00 NaN NaN 0.00
## 1.College_Math_Grade* 2.00 0.16 -1.44 0.02
## 1.MathStart 1.00 0.66 -1.57 0.01
## 1.HSGPA 3.95 -0.72 0.27 0.01
## 1.ACT_Math 22.00 0.32 -0.62 0.13
## 1.ALEKS 82.00 0.36 -0.65 1.44
write.csv(math_pass_tbl, "math_pass_table.csv")
eng_pass_table<- describeBy(EnglC_Term, EnglC_Term$College_English_Pass)
eng_pass_table<- do.call("rbind", eng_pass_table)
eng_pass_table
## vars n mean sd median trimmed mad min
## 0.Col_Eng_Term* 1 360 6.90 2.85 7.00 7.05 2.97 1.00
## 0.College_English_Pass 2 360 0.00 0.00 0.00 0.00 0.00 0.00
## 0.College_English_Grade* 3 360 1.81 0.41 2.00 1.88 0.00 1.00
## 0.EnglishStart 4 360 0.60 0.49 1.00 0.62 0.00 0.00
## 0.HSGPA 5 331 2.67 0.51 2.67 2.67 0.55 1.00
## 0.ACT_Engl 6 171 17.90 4.05 18.00 17.90 4.45 9.00
## 0.ACT_Reading 7 172 19.26 4.48 19.00 19.14 4.45 9.00
## 0.McCann_R 8 82 95.16 12.34 95.00 95.26 10.38 61.00
## 0.McCann_W 9 76 4.63 0.85 5.00 4.63 1.48 2.00
## 1.Col_Eng_Term* 1 2777 6.15 3.06 6.00 6.20 4.45 1.00
## 1.College_English_Pass 2 2777 1.00 0.00 1.00 1.00 0.00 1.00
## 1.College_English_Grade* 3 2777 1.79 0.76 2.00 1.74 1.48 1.00
## 1.EnglishStart 4 2775 0.58 0.49 1.00 0.60 0.00 0.00
## 1.HSGPA 5 2515 3.04 0.54 3.07 3.05 0.57 1.34
## 1.ACT_Engl 6 1153 19.32 4.44 20.00 19.20 4.45 2.00
## 1.ACT_Reading 7 1151 20.79 4.59 20.00 20.57 4.45 10.00
## 1.McCann_R 8 482 101.33 13.55 101.00 100.76 11.86 50.00
## 1.McCann_W 9 417 4.82 0.81 5.00 4.84 1.48 2.00
## max range skew kurtosis se
## 0.Col_Eng_Term* 11.00 10.00 -0.36 -0.76 0.15
## 0.College_English_Pass 0.00 0.00 NaN NaN 0.00
## 0.College_English_Grade* 3.00 2.00 -1.33 0.55 0.02
## 0.EnglishStart 1.00 1.00 -0.39 -1.85 0.03
## 0.HSGPA 3.90 2.90 -0.05 -0.39 0.03
## 0.ACT_Engl 28.00 19.00 -0.01 -0.78 0.31
## 0.ACT_Reading 32.00 23.00 0.26 -0.04 0.34
## 0.McCann_R 130.00 69.00 0.00 0.78 1.36
## 0.McCann_W 6.00 4.00 -0.15 0.00 0.10
## 1.Col_Eng_Term* 11.00 10.00 -0.15 -1.12 0.06
## 1.College_English_Pass 1.00 0.00 NaN NaN 0.00
## 1.College_English_Grade* 3.00 2.00 0.37 -1.19 0.01
## 1.EnglishStart 1.00 1.00 -0.31 -1.91 0.01
## 1.HSGPA 4.47 3.13 -0.29 -0.45 0.01
## 1.ACT_Engl 36.00 34.00 0.28 0.58 0.13
## 1.ACT_Reading 36.00 26.00 0.45 0.08 0.14
## 1.McCann_R 150.00 100.00 0.64 2.21 0.62
## 1.McCann_W 6.00 4.00 -0.33 -0.09 0.04
write.csv(eng_pass_table, "eng_pass_table.csv")
library(gridExtra)
FT_Percent<- read.csv("FT_Percent.csv")
FTTrans<- read.csv("FTTrans.csv")
ft_graph<- read.csv("ft_graph.csv")
ft_graph_transpose<- read.csv("ft_graph_transpose.csv")
mytheme <- gridExtra::ttheme_default(
core = list(fg_params=list(cex = 1)),
colhead = list(fg_params=list(cex = 1)),
rowhead = list(fg_params=list(cex = 1)))
tbl<- tableGrob(FTTrans, theme = mytheme)
tbl2<- tableGrob(ft_graph_transpose, theme = mytheme)
p1<- ggplot(FT_Percent, aes(x=Term, y=Percent, group = Content, color = Content)) +
geom_line()+
geom_point()+
scale_color_brewer(palette = "Dark2")+
geom_text(aes(label= scales::percent(Percent)), size = 4, color = "black", hjust = .5, vjust = -1.2)+
geom_vline(xintercept = 7, lwd=.5,colour="black")+
scale_y_continuous(label=scales::percent, limits = c(0, 1))+
ggtitle("Percent Students Passing College Level Math and English")
p2<- ggplot(ft_graph, aes(x=Term, y=Percent, group = Content, color = Content)) +
geom_line()+
geom_point()+
scale_color_brewer(palette = "Dark2")+
geom_text(aes(label= scales::percent(Percent)), size = 4, color = "black", hjust = .5, vjust = -1.2)+
geom_vline(xintercept = 7, lwd=.8,colour="black")+
scale_y_continuous(label=scales::percent, limits = c(0, .8))+
ggtitle("Percent First Time Students by Content and Term")
grid.arrange(p1, tbl, nrow = 2, heights=c(4,2))
grid.arrange(p2, tbl2, nrow = 2, heights=c(4,2))
library(apaTables)
corrtable<- Placement %>%
select(LCCCGPA, HSGPA, ACT_Reading, ACT_Engl, ACT_Math, HSGradYears, ALEKS, McCann_W, McCann_R)
corrtable$HSGradYears<- as.numeric(corrtable$HSGradYears)
apa.cor.table(corrtable, filename = "correlations.doc")
##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3 4
## 1. LCCCGPA 2.68 0.98
##
## 2. HSGPA 2.97 0.57 .33**
## [.31, .35]
##
## 3. ACT_Reading 20.49 4.96 .23** .39**
## [.19, .26] [.36, .42]
##
## 4. ACT_Engl 19.09 4.71 .26** .44** .75**
## [.23, .30] [.41, .47] [.73, .76]
##
## 5. ACT_Math 19.16 3.79 .22** .42** .53** .63**
## [.19, .26] [.38, .45] [.51, .56] [.61, .65]
##
## 6. HSGradYears 3.06 6.46 .12** -.21** .00 -.01
## [.10, .14] [-.23, -.19] [-.03, .04] [-.04, .03]
##
## 7. ALEKS 25.59 15.58 .16** .33** .33** .38**
## [.11, .21] [.27, .37] [.26, .40] [.31, .44]
##
## 8. McCann_W 4.63 0.97 .17** .23** .34** .39**
## [.11, .23] [.16, .29] [.26, .42] [.32, .47]
##
## 9. McCann_R 99.40 14.69 .28** .22** .48** .52**
## [.23, .33] [.16, .28] [.41, .54] [.45, .58]
##
## 5 6 7 8
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## -.03
## [-.06, .01]
##
## .55** -.01
## [.50, .61] [-.07, .04]
##
## .27** -.04 .19**
## [.19, .35] [-.11, .02] [.12, .26]
##
## .35** .16** .25** .33**
## [.28, .42] [.11, .22] [.19, .31] [.28, .39]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
##
##Point biserial with English
with(Placement, cor.test(LCCCGPA, College_English_Pass))
##
## Pearson's product-moment correlation
##
## data: LCCCGPA and College_English_Pass
## t = 54.783, df = 4997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5949416 0.6295914
## sample estimates:
## cor
## 0.6125607
with(Placement, cor.test(HSGPA, College_English_Pass))
##
## Pearson's product-moment correlation
##
## data: HSGPA and College_English_Pass
## t = 13.421, df = 4477, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1683540 0.2246647
## sample estimates:
## cor
## 0.1966715
with(Placement, cor.test(ACT_Engl, College_English_Pass))
##
## Pearson's product-moment correlation
##
## data: ACT_Engl and College_English_Pass
## t = 5.6924, df = 1954, p-value = 1.442e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.08387532 0.17107467
## sample estimates:
## cor
## 0.1277218
with(Placement, cor.test(ACT_Reading, College_English_Pass))
##
## Pearson's product-moment correlation
##
## data: ACT_Reading and College_English_Pass
## t = 4.788, df = 1953, p-value = 1.811e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.06368487 0.15132340
## sample estimates:
## cor
## 0.1077134
with(Placement, cor.test(McCann_R, College_English_Pass))
##
## Pearson's product-moment correlation
##
## data: McCann_R and College_English_Pass
## t = 4.6837, df = 897, p-value = 3.254e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09003074 0.21769083
## sample estimates:
## cor
## 0.1545056
with(Placement, cor.test(McCann_W, College_English_Pass))
##
## Pearson's product-moment correlation
##
## data: McCann_W and College_English_Pass
## t = 2.1583, df = 780, p-value = 0.03121
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.006979748 0.146366990
## sample estimates:
## cor
## 0.07704984
with(Placement, cor.test(ACT_Math, College_English_Pass))
##
## Pearson's product-moment correlation
##
## data: ACT_Math and College_English_Pass
## t = 4.7708, df = 1962, p-value = 1.971e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.06315452 0.15060386
## sample estimates:
## cor
## 0.1070863
with(Placement, cor.test(ALEKS, College_English_Pass))
##
## Pearson's product-moment correlation
##
## data: ALEKS and College_English_Pass
## t = 1.05, df = 990, p-value = 0.294
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02895080 0.09539659
## sample estimates:
## cor
## 0.03335196
#Point biserial with Math
with(Placement, cor.test(LCCCGPA, College_Math_Pass))
##
## Pearson's product-moment correlation
##
## data: LCCCGPA and College_Math_Pass
## t = 52.269, df = 5469, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5592294 0.5945806
## sample estimates:
## cor
## 0.5771754
with(Placement, cor.test(HSGPA, College_Math_Pass))
##
## Pearson's product-moment correlation
##
## data: HSGPA and College_Math_Pass
## t = 12.103, df = 4611, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1473533 0.2032952
## sample estimates:
## cor
## 0.1754659
with(Placement, cor.test(ACT_Math, College_Math_Pass))
##
## Pearson's product-moment correlation
##
## data: ACT_Math and College_Math_Pass
## t = 4.2217, df = 1817, p-value = 2.544e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05283695 0.14386679
## sample estimates:
## cor
## 0.09855804
with(Placement, cor.test(ALEKS, College_Math_Pass))
##
## Pearson's product-moment correlation
##
## data: ALEKS and College_Math_Pass
## t = 1.8316, df = 459, p-value = 0.06766
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.006193846 0.175147593
## sample estimates:
## cor
## 0.08518225
with(Placement, cor.test(ACT_Engl, College_Math_Pass))
##
## Pearson's product-moment correlation
##
## data: ACT_Engl and College_Math_Pass
## t = 5.1843, df = 1813, p-value = 2.41e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07527148 0.16595214
## sample estimates:
## cor
## 0.120864
with(Placement, cor.test(ACT_Reading, College_Math_Pass))
##
## Pearson's product-moment correlation
##
## data: ACT_Reading and College_Math_Pass
## t = 3.6429, df = 1810, p-value = 0.0002772
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03941929 0.13084865
## sample estimates:
## cor
## 0.08531357
with(Placement, cor.test(McCann_R, College_Math_Pass))
##
## Pearson's product-moment correlation
##
## data: McCann_R and College_Math_Pass
## t = 2.0911, df = 380, p-value = 0.03718
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.006391295 0.204806664
## sample estimates:
## cor
## 0.1066607
with(Placement, cor.test(McCann_W, College_Math_Pass))
##
## Pearson's product-moment correlation
##
## data: McCann_W and College_Math_Pass
## t = 1.6083, df = 312, p-value = 0.1088
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02021185 0.19935822
## sample estimates:
## cor
## 0.09067502
with(Placement, cor.test(College_English_Pass, College_Math_Pass))
##
## Pearson's product-moment correlation
##
## data: College_English_Pass and College_Math_Pass
## t = 20.669, df = 2812, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3306463 0.3948123
## sample estimates:
## cor
## 0.3631598
library(bannerCommenter)
attach(corrtable)
banner("GPA")
##
## #################################################################
## ## GPA ##
## #################################################################
cor.test(HSGPA, LCCCGPA)
##
## Pearson's product-moment correlation
##
## data: HSGPA and LCCCGPA
## t = 31.183, df = 7878, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3116662 0.3509763
## sample estimates:
## cor
## 0.3314651
banner("ACT Reading")
##
## #################################################################
## ## ACT Reading ##
## #################################################################
cor.test(ACT_Reading, LCCCGPA)
##
## Pearson's product-moment correlation
##
## data: ACT_Reading and LCCCGPA
## t = 12.831, df = 2997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1939940 0.2618556
## sample estimates:
## cor
## 0.2282019
banner("ACT English")
##
## #################################################################
## ## ACT English ##
## #################################################################
cor.test(ACT_Engl, LCCCGPA)
##
## Pearson's product-moment correlation
##
## data: ACT_Engl and LCCCGPA
## t = 14.92, df = 3002, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2291400 0.2957333
## sample estimates:
## cor
## 0.2627495
banner("ACT Math")
##
## ##################################################################
## ## ACT Math ##
## ##################################################################
cor.test(ACT_Math, LCCCGPA)
##
## Pearson's product-moment correlation
##
## data: ACT_Math and LCCCGPA
## t = 12.546, df = 3013, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1886181 0.2564726
## sample estimates:
## cor
## 0.2228152
banner("Years")
##
## #################################################################
## ## Years ##
## #################################################################
cor.test(HSGradYears, LCCCGPA)
##
## Pearson's product-moment correlation
##
## data: HSGradYears and LCCCGPA
## t = 11.973, df = 9404, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1025843 0.1423967
## sample estimates:
## cor
## 0.1225398
detach(corrtable)
library(apaTables)
lmoutput1<- lm(HSGPA~Col_Eng_Term, data = EnglC_Term)
apa.aov.table(lmoutput1, filename = "GPAEnglish.doc")
##
##
## ANOVA results using HSGPA as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 2118.97 1 2118.97 7200.50 .000
## Col_Eng_Term 16.03 10 1.60 5.45 .000 .02 [.01, .02]
## Error 834.29 2835 0.29
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
EnglGPApair<- TukeyHSD(aov(HSGPA~Col_Eng_Term, data = EnglC_Term))
EnglGPApair<- do.call("rbind", EnglGPApair)
EnglGPApair
## diff lwr upr p adj
## 11/FA-10/FA 0.0847830644 -0.07834824 0.247914366 0.8488406846
## 12/FA-10/FA 0.0478719993 -0.12386035 0.219604350 0.9982879293
## 13/FA-10/FA 0.0560843953 -0.11395960 0.226128393 0.9931734087
## 14/FA-10/FA 0.0009381368 -0.16048868 0.162364952 1.0000000000
## 15/FA-10/FA -0.1015681525 -0.25302014 0.049883836 0.5336282905
## 16/FA-10/FA -0.1074634505 -0.25921707 0.044290166 0.4471919773
## 17/FA-10/FA -0.1188079691 -0.26996338 0.032347444 0.2862218516
## 18/FA-10/FA -0.1437849024 -0.30050666 0.012936853 0.1067222175
## 19/FA-10/FA -0.0234049368 -0.17588275 0.129072879 0.9999924972
## 20/FA-10/FA -0.1032723895 -0.26676027 0.060215487 0.6243247751
## 12/FA-11/FA -0.0369110651 -0.20847453 0.134652400 0.9998251415
## 13/FA-11/FA -0.0286986690 -0.19857210 0.141174764 0.9999813882
## 14/FA-11/FA -0.0838449275 -0.24509206 0.077402208 0.8484381619
## 15/FA-11/FA -0.1863512168 -0.33761168 -0.035090756 0.0035811514
## 16/FA-11/FA -0.1922465148 -0.34380898 -0.040684045 0.0022246975
## 17/FA-11/FA -0.2035910335 -0.35455454 -0.052627524 0.0007388222
## 18/FA-11/FA -0.2285679668 -0.38510464 -0.072031291 0.0001412922
## 19/FA-11/FA -0.1081880011 -0.26047558 0.044099577 0.4420690647
## 20/FA-11/FA -0.1880554539 -0.35136592 -0.024744989 0.0096740327
## 13/FA-12/FA 0.0082123961 -0.16993687 0.186361665 0.9999999999
## 14/FA-12/FA -0.0469338624 -0.21687743 0.123009709 0.9984183637
## 15/FA-12/FA -0.1494401517 -0.30993908 0.011058776 0.0948200673
## 16/FA-12/FA -0.1553354497 -0.31611903 0.005448135 0.0690993406
## 17/FA-12/FA -0.1666799684 -0.32689907 -0.006460868 0.0332980366
## 18/FA-12/FA -0.1916569016 -0.35713775 -0.026176058 0.0089375584
## 19/FA-12/FA -0.0712769360 -0.23274422 0.090190353 0.9431214661
## 20/FA-12/FA -0.1511443887 -0.32304694 0.020758162 0.1463383772
## 14/FA-13/FA -0.0551462585 -0.22338353 0.113091009 0.9935007099
## 15/FA-13/FA -0.1576525478 -0.31634365 0.001038556 0.0532933166
## 16/FA-13/FA -0.1635478458 -0.32252684 -0.004568848 0.0375117665
## 17/FA-13/FA -0.1748923644 -0.33330045 -0.016484282 0.0167280919
## 18/FA-13/FA -0.1998692977 -0.36359734 -0.036141260 0.0041346983
## 19/FA-13/FA -0.0794893321 -0.23915976 0.080181097 0.8811045133
## 20/FA-13/FA -0.1593567848 -0.32957267 0.010859100 0.0906486762
## 15/FA-14/FA -0.1025062893 -0.25192691 0.046914332 0.4978081270
## 16/FA-14/FA -0.1084015873 -0.25812793 0.041324754 0.4118983913
## 17/FA-14/FA -0.1197461059 -0.26886611 0.029373900 0.2560020389
## 18/FA-14/FA -0.1447230392 -0.29948261 0.010036535 0.0914992839
## 19/FA-14/FA -0.0243430736 -0.17480337 0.126117225 0.9999876320
## 20/FA-14/FA -0.1042105263 -0.26581839 0.057397342 0.5938697180
## 16/FA-15/FA -0.0058952980 -0.14480918 0.133018580 1.0000000000
## 17/FA-15/FA -0.0172398166 -0.15549995 0.121020317 0.9999989819
## 18/FA-15/FA -0.0422167499 -0.18654143 0.102107933 0.9974320938
## 19/FA-15/FA 0.0781632157 -0.06154144 0.217867867 0.7790586407
## 20/FA-15/FA -0.0017042370 -0.15334919 0.149940714 1.0000000000
## 17/FA-16/FA -0.0113445186 -0.14993499 0.127245956 0.9999999834
## 18/FA-16/FA -0.0363214519 -0.18096263 0.108319723 0.9993047868
## 19/FA-16/FA 0.0840585137 -0.05597307 0.224090099 0.6949504571
## 20/FA-16/FA 0.0041910610 -0.14775514 0.156137257 1.0000000000
## 18/FA-17/FA -0.0249769333 -0.16899036 0.119036498 0.9999761730
## 19/FA-17/FA 0.0954030323 -0.04398005 0.234786115 0.5014188388
## 20/FA-17/FA 0.0155355796 -0.13581317 0.166884334 0.9999998469
## 19/FA-18/FA 0.1203799656 -0.02502084 0.265780769 0.2155273130
## 20/FA-18/FA 0.0405125129 -0.11639572 0.197420751 0.9991139071
## 20/FA-19/FA -0.0798674527 -0.23253693 0.072802029 0.8433955947
write.csv(EnglGPApair, "enggpapair.csv")
lmoutput2<- lm(ACT_Engl~Col_Eng_Term, data = EnglC_Term)
apa.aov.table(lmoutput2, filename = "ACTEng.doc")
##
##
## ANOVA results using ACT_Engl as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 1121.33 1 1121.33 60.11 .000
## Col_Eng_Term 1258.16 10 125.82 6.74 .000 .05 [.03, .06]
## Error 24494.99 1313 18.66
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
EnglACTEnpair<- TukeyHSD(aov(ACT_Engl~Col_Eng_Term, data = EnglC_Term))
EnglACTEnpair<- do.call("rbind", EnglACTEnpair)
EnglACTEnpair
## diff lwr upr p adj
## 11/FA-10/FA -3.83333333 -16.546738 8.8800713 9.966549e-01
## 12/FA-10/FA 1.00000000 -10.371215 12.3712148 1.000000e+00
## 13/FA-10/FA 4.40000000 -4.408105 13.2081051 8.779176e-01
## 14/FA-10/FA 1.78431373 -6.344549 9.9131768 9.997871e-01
## 15/FA-10/FA 0.42496051 -7.672662 8.5225829 1.000000e+00
## 16/FA-10/FA -0.35976505 -8.453386 7.7338558 1.000000e+00
## 17/FA-10/FA -0.63464338 -8.727803 7.4585164 1.000000e+00
## 18/FA-10/FA -0.58766859 -8.697749 7.5224116 1.000000e+00
## 19/FA-10/FA -0.80977312 -8.913337 7.2937905 9.999999e-01
## 20/FA-10/FA -1.24378109 -9.373953 6.8863912 9.999926e-01
## 12/FA-11/FA 4.83333333 -7.880071 17.5467379 9.795879e-01
## 13/FA-11/FA 8.23333333 -2.250409 18.7170753 2.866277e-01
## 14/FA-11/FA 5.61764706 -4.302260 15.5375537 7.646033e-01
## 15/FA-11/FA 4.25829384 -5.636029 14.1526165 9.514415e-01
## 16/FA-11/FA 3.47356828 -6.417480 13.3646162 9.887321e-01
## 17/FA-11/FA 3.19868996 -6.691981 13.0893607 9.940914e-01
## 18/FA-11/FA 3.24566474 -6.658856 13.1501855 9.934370e-01
## 19/FA-11/FA 3.02356021 -6.875625 12.9227458 9.962807e-01
## 20/FA-11/FA 2.58955224 -7.331427 12.5105317 9.990125e-01
## 13/FA-12/FA 3.40000000 -5.408105 12.2081051 9.771993e-01
## 14/FA-12/FA 0.78431373 -7.344549 8.9131768 9.999999e-01
## 15/FA-12/FA -0.57503949 -8.672662 7.5225829 1.000000e+00
## 16/FA-12/FA -1.35976505 -9.453386 6.7338558 9.999821e-01
## 17/FA-12/FA -1.63464338 -9.727803 6.4585164 9.999003e-01
## 18/FA-12/FA -1.58766859 -9.697749 6.5224116 9.999252e-01
## 19/FA-12/FA -1.80977312 -9.913337 6.2937905 9.997512e-01
## 20/FA-12/FA -2.24378109 -10.373953 5.8863912 9.984063e-01
## 14/FA-13/FA -2.61568627 -6.404697 1.1733249 4.866014e-01
## 15/FA-13/FA -3.97503949 -7.696555 -0.2535238 2.488220e-02
## 16/FA-13/FA -4.75976505 -8.472566 -1.0469645 1.881807e-03
## 17/FA-13/FA -5.03464338 -8.746439 -1.3228479 6.745436e-04
## 18/FA-13/FA -4.98766859 -8.736214 -1.2391235 9.699720e-04
## 19/FA-13/FA -5.20977312 -8.944199 -1.4753477 3.862016e-04
## 20/FA-13/FA -5.64378109 -9.435600 -1.8519621 9.407649e-05
## 15/FA-14/FA -1.35935322 -2.890816 0.1721099 1.365371e-01
## 16/FA-14/FA -2.14407878 -3.654240 -0.6339172 2.681862e-04
## 17/FA-14/FA -2.41895710 -3.926646 -0.9112682 1.433461e-05
## 18/FA-14/FA -2.37198232 -3.968006 -0.7759590 9.740488e-05
## 19/FA-14/FA -2.59408685 -4.156660 -1.0315142 5.532468e-06
## 20/FA-14/FA -3.02809482 -4.723262 -1.3329277 5.725467e-07
## 16/FA-15/FA -0.78472556 -2.116514 0.5470633 7.174983e-01
## 17/FA-15/FA -1.05960388 -2.388588 0.2693804 2.651896e-01
## 18/FA-15/FA -1.01262910 -2.441042 0.4157840 4.442642e-01
## 19/FA-15/FA -1.23473363 -2.625671 0.1562038 1.364471e-01
## 20/FA-15/FA -1.66874160 -3.207139 -0.1303446 2.078978e-02
## 17/FA-16/FA -0.27487833 -1.579259 1.0295020 9.998530e-01
## 18/FA-16/FA -0.22790354 -1.633454 1.1776471 9.999872e-01
## 19/FA-16/FA -0.45000807 -1.817457 0.9174406 9.932126e-01
## 20/FA-16/FA -0.88401604 -2.401209 0.6331767 7.313248e-01
## 18/FA-17/FA 0.04697478 -1.355919 1.4498684 1.000000e+00
## 19/FA-17/FA -0.17512975 -1.539847 1.1895876 9.999986e-01
## 20/FA-17/FA -0.60913772 -2.123869 0.9055938 9.694703e-01
## 19/FA-18/FA -0.22210453 -1.683822 1.2396130 9.999931e-01
## 20/FA-18/FA -0.65611250 -2.258790 0.9465653 9.654218e-01
## 20/FA-19/FA -0.43400797 -2.003377 1.1353611 9.983782e-01
write.csv(EnglACTEnpair, "engactengpair.csv")
lmoutput3<- lm(ACT_Reading~Col_Eng_Term, data = EnglC_Term)
apa.aov.table(lmoutput3, filename = "ACTRdg.doc")
##
##
## ANOVA results using ACT_Reading as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 1160.33 1 1160.33 55.86 .000
## Col_Eng_Term 727.84 10 72.78 3.50 .000 .03 [.01, .03]
## Error 27251.57 1312 20.77
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
EnglACTRdpair<- TukeyHSD(aov(ACT_Reading~Col_Eng_Term, data = EnglC_Term))
EnglACTRdpair<- do.call("rbind", EnglACTRdpair)
EnglACTRdpair
## diff lwr upr p adj
## 11/FA-10/FA -1.66666667 -15.0814934 11.748160020 0.999999000
## 12/FA-10/FA 2.00000000 -9.9985858 13.998585751 0.999983339
## 13/FA-10/FA 5.20000000 -4.0940646 14.494064558 0.777877989
## 14/FA-10/FA 1.88077859 -6.6958933 10.457450481 0.999788998
## 15/FA-10/FA 1.54660348 -6.9977798 10.090986743 0.999963872
## 16/FA-10/FA 0.74122807 -7.7986885 9.281144647 0.999999970
## 17/FA-10/FA 0.28966521 -8.2500092 8.829339628 1.000000000
## 18/FA-10/FA 1.05098039 -7.5078349 9.609795659 0.999999107
## 19/FA-10/FA 0.78359511 -7.7670571 9.334247341 0.999999949
## 20/FA-10/FA -0.02487562 -8.6036046 8.553853327 1.000000000
## 12/FA-11/FA 3.66666667 -9.7481600 17.081493353 0.998532214
## 13/FA-11/FA 6.86666667 -4.1954828 17.928816142 0.648018399
## 14/FA-11/FA 3.54744526 -6.9192073 14.014097813 0.991440477
## 15/FA-11/FA 3.21327014 -7.2269406 13.653480888 0.996040596
## 16/FA-11/FA 2.40789474 -8.0286607 12.844450210 0.999666839
## 17/FA-11/FA 1.95633188 -8.4800254 12.392689199 0.999949950
## 18/FA-11/FA 2.71764706 -7.7343783 13.169672402 0.999044978
## 19/FA-11/FA 2.45026178 -7.9950802 12.895603734 0.999613638
## 20/FA-11/FA 1.64179104 -8.8265472 12.110129279 0.999990643
## 13/FA-12/FA 3.20000000 -6.0940646 12.494064558 0.990325131
## 14/FA-12/FA -0.11922141 -8.6958933 8.457450481 1.000000000
## 15/FA-12/FA -0.45339652 -8.9977798 8.090986743 1.000000000
## 16/FA-12/FA -1.25877193 -9.7986885 7.281144647 0.999994820
## 17/FA-12/FA -1.71033479 -10.2500092 6.829339628 0.999907710
## 18/FA-12/FA -0.94901961 -9.5078349 7.609795659 0.999999669
## 19/FA-12/FA -1.21640489 -9.7670571 7.334247341 0.999996312
## 20/FA-12/FA -2.02487562 -10.6036046 6.553853327 0.999591777
## 14/FA-13/FA -3.31922141 -7.3158296 0.677386742 0.211020008
## 15/FA-13/FA -3.65339652 -7.5802351 0.273442077 0.095286791
## 16/FA-13/FA -4.45877193 -8.3758820 -0.541661881 0.011361369
## 17/FA-13/FA -4.91033479 -8.8269169 -0.993752717 0.002732788
## 18/FA-13/FA -4.14901961 -8.1071625 -0.190876765 0.030711258
## 19/FA-13/FA -4.41640489 -8.3568655 -0.475944316 0.013897727
## 20/FA-13/FA -5.22487562 -9.2258963 -1.223854955 0.001362815
## 15/FA-14/FA -0.33417511 -1.9465417 1.278191512 0.999873677
## 16/FA-14/FA -1.13955052 -2.7280768 0.448975799 0.425400253
## 17/FA-14/FA -1.59111338 -3.1783373 -0.003889431 0.048815307
## 18/FA-14/FA -0.82979820 -2.5169732 0.857376808 0.888362154
## 19/FA-14/FA -1.09718348 -2.7424477 0.548080710 0.541092793
## 20/FA-14/FA -1.90565421 -3.6911040 -0.120204378 0.025096133
## 16/FA-15/FA -0.80537541 -2.2091562 0.598405395 0.749741696
## 17/FA-15/FA -1.25693826 -2.6592451 0.145368596 0.127171285
## 18/FA-15/FA -0.49562308 -2.0101343 1.018888114 0.993507478
## 19/FA-15/FA -0.76300836 -2.2306864 0.704669706 0.847641562
## 20/FA-15/FA -1.57147910 -3.1947523 0.051794070 0.067786114
## 17/FA-16/FA -0.45156286 -1.8263917 0.923265952 0.993315836
## 18/FA-16/FA 0.30975232 -1.1793527 1.798857388 0.999869410
## 19/FA-16/FA 0.04236704 -1.3990797 1.483813761 1.000000000
## 20/FA-16/FA -0.76610369 -2.3656991 0.833491727 0.904535940
## 18/FA-17/FA 0.76131518 -0.7264005 2.249030843 0.860232644
## 19/FA-17/FA 0.49392990 -0.9460814 1.933941234 0.990605234
## 20/FA-17/FA -0.31454083 -1.9128429 1.283761235 0.999921482
## 19/FA-18/FA -0.26738528 -1.8168730 1.282102467 0.999976909
## 20/FA-18/FA -1.07585601 -2.7734570 0.621744981 0.618217617
## 20/FA-19/FA -0.80847074 -2.4644248 0.847483335 0.893048965
write.csv(EnglACTRdpair, "engactrdpair.csv")
lmoutput4<- lm(McCann_R~Col_Eng_Term, data = EnglC_Term)
apa.aov.table(lmoutput4, filename = "McR.doc")
##
##
## ANOVA results using McCann_R as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 39445.33 1 39445.33 220.52 .000
## Col_Eng_Term 4074.97 8 509.37 2.85 .004 .04 [.01, .06]
## Error 99275.73 555 178.88
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
EnglMRpair<- TukeyHSD(aov(McCann_R~Col_Eng_Term, data = EnglC_Term))
EnglMRpair<- do.call("rbind", EnglMRpair)
EnglMRpair
## diff lwr upr p adj
## 12/FA-11/FA -2.6666667 -50.758717 45.425384 0.999999976
## 13/FA-11/FA 35.3333333 -12.758717 83.425384 0.351204925
## 15/FA-11/FA -5.1666667 -43.186771 32.853438 0.999972062
## 16/FA-11/FA -13.2121212 -37.554413 11.130170 0.752488250
## 17/FA-11/FA -15.1996008 -39.460647 9.061445 0.578430068
## 18/FA-11/FA -13.6352201 -37.907035 10.636594 0.715400694
## 19/FA-11/FA -16.3534137 -40.830148 8.123321 0.487740606
## 20/FA-11/FA -14.2962963 -39.643033 11.050440 0.710870346
## 13/FA-12/FA 38.0000000 -20.900492 96.900492 0.537642797
## 15/FA-12/FA -2.5000000 -53.509323 48.509323 0.999999991
## 16/FA-12/FA -10.5454545 -52.366141 31.275232 0.997237933
## 17/FA-12/FA -12.5329341 -54.306383 29.240515 0.990927466
## 18/FA-12/FA -10.9685535 -52.748257 30.811150 0.996339495
## 19/FA-12/FA -13.6867470 -55.585831 28.212337 0.984190211
## 20/FA-12/FA -11.6296296 -54.042832 30.783572 0.995058816
## 15/FA-13/FA -40.5000000 -91.509323 10.509323 0.247739667
## 16/FA-13/FA -48.5454545 -90.366141 -6.724768 0.009865287
## 17/FA-13/FA -50.5329341 -92.306383 -8.759485 0.005682167
## 18/FA-13/FA -48.9685535 -90.748257 -7.188850 0.008707687
## 19/FA-13/FA -51.6867470 -93.585831 -9.787663 0.004298110
## 20/FA-13/FA -49.6296296 -92.042832 -7.216428 0.008895764
## 16/FA-15/FA -8.0454545 -37.738094 21.647185 0.995446199
## 17/FA-15/FA -10.0329341 -39.659004 19.593136 0.980119052
## 18/FA-15/FA -8.4685535 -38.103443 21.166336 0.993453859
## 19/FA-15/FA -11.1867470 -40.989703 18.616209 0.962643493
## 20/FA-15/FA -9.1296296 -39.651142 21.391883 0.991106905
## 17/FA-16/FA -1.9874796 -6.959687 2.984728 0.946104114
## 18/FA-16/FA -0.4230989 -5.447586 4.601388 0.999999335
## 19/FA-16/FA -3.1412924 -9.077202 2.794617 0.777649581
## 20/FA-16/FA -1.0841751 -9.948797 7.780447 0.999987635
## 18/FA-17/FA 1.5643807 -3.050452 6.179213 0.979993932
## 19/FA-17/FA -1.1538129 -6.747223 4.439597 0.999348150
## 20/FA-17/FA 0.9033045 -7.735719 9.542328 0.999996356
## 19/FA-18/FA -2.7181935 -8.358127 2.921740 0.855064681
## 20/FA-18/FA -0.6610762 -9.330295 8.008142 0.999999696
## 20/FA-19/FA 2.0571174 -7.170283 11.284517 0.998852392
write.csv(EnglMRpair, "engmrpair.csv")
lmoutput5<- lm(McCann_W~Col_Eng_Term, data = EnglC_Term)
apa.aov.table(lmoutput5, filename = "McW.doc")
##
##
## ANOVA results using McCann_W as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 36.00 1 36.00 54.84 .000
## Col_Eng_Term 11.01 6 1.83 2.79 .011 .03 [.00, .05]
## Error 319.05 486 0.66
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
EnglMWpair<- TukeyHSD(aov(McCann_W~Col_Eng_Term, data = EnglC_Term))
EnglMWpair<- do.call("rbind", EnglMWpair)
EnglMWpair
## diff lwr upr p adj
## 15/FA-11/FA -2.00000000 -5.3925621 1.39256211 0.58571961
## 16/FA-11/FA -1.12396694 -3.5327630 1.28482916 0.81161168
## 17/FA-11/FA -1.24223602 -3.6485782 1.16410613 0.72757360
## 18/FA-11/FA -1.13207547 -3.5385110 1.27436010 0.80562670
## 19/FA-11/FA -1.55882353 -3.9927495 0.87510249 0.48378482
## 20/FA-11/FA -1.62500000 -4.0977333 0.84773331 0.45105608
## 16/FA-15/FA 0.87603306 -1.5327630 3.28482916 0.93471429
## 17/FA-15/FA 0.75776398 -1.6485782 3.16410613 0.96721028
## 18/FA-15/FA 0.86792453 -1.5385110 3.27436010 0.93717768
## 19/FA-15/FA 0.44117647 -1.9927495 2.87510249 0.99828564
## 20/FA-15/FA 0.37500000 -2.0977333 2.84773331 0.99937857
## 17/FA-16/FA -0.11826908 -0.4068926 0.17035445 0.88895041
## 18/FA-16/FA -0.00810853 -0.2975099 0.28129284 0.99999997
## 19/FA-16/FA -0.43485659 -0.9004925 0.03077938 0.08506519
## 20/FA-16/FA -0.50103306 -1.1391796 0.13711348 0.23432464
## 18/FA-17/FA 0.11016055 -0.1580503 0.37837138 0.88784001
## 19/FA-17/FA -0.31658750 -0.7693576 0.13618257 0.37217198
## 20/FA-17/FA -0.38276398 -1.0115842 0.24605627 0.54718459
## 19/FA-18/FA -0.42674806 -0.8800144 0.02651826 0.08022196
## 20/FA-18/FA -0.49292453 -1.1221022 0.13625311 0.23668490
## 20/FA-19/FA -0.06617647 -0.7934510 0.66109803 0.99996849
write.csv(EnglMWpair, "engmwpair.csv")
lmoutput6<- lm(HSGPA~Col_Math_Term, data = MathC_Term)
apa.aov.table(lmoutput6, filename = "GPAMath.doc")
##
##
## ANOVA results using HSGPA as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2
## (Intercept) 1283.92 1 1283.92 4565.49 .000
## Col_Math_Term 16.76 10 1.68 5.96 .000 .02
## Error 669.87 2382 0.28
## CI_90_partial_eta2
##
## [.01, .03]
##
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
MathGPApair<- TukeyHSD(aov(HSGPA~Col_Math_Term, data = MathC_Term))
MathGPApair<- do.call("rbind", MathGPApair)
MathGPApair
## diff lwr upr p adj
## 11/FA-10/FA 0.046060440 -0.15743347 0.249554346 9.997227e-01
## 12/FA-10/FA -0.029431624 -0.23937813 0.180514882 9.999968e-01
## 13/FA-10/FA -0.038350364 -0.23064562 0.153944891 9.999124e-01
## 14/FA-10/FA 0.065236154 -0.12724782 0.257720125 9.915260e-01
## 15/FA-10/FA -0.140571996 -0.32179793 0.040653940 3.053883e-01
## 16/FA-10/FA 0.048741758 -0.13332212 0.230805640 9.987852e-01
## 17/FA-10/FA 0.072238716 -0.11354874 0.258026171 9.761753e-01
## 18/FA-10/FA 0.089670355 -0.09949572 0.278836431 9.108408e-01
## 19/FA-10/FA 0.078474359 -0.10841964 0.265368360 9.591774e-01
## 20/FA-10/FA -0.117839031 -0.29522041 0.059542345 5.481435e-01
## 12/FA-11/FA -0.075492063 -0.27693217 0.125948047 9.817902e-01
## 13/FA-11/FA -0.084410803 -0.26738088 0.098559270 9.245485e-01
## 14/FA-11/FA 0.019175714 -0.16399268 0.202344113 9.999998e-01
## 15/FA-11/FA -0.186632435 -0.35793165 -0.015333216 1.967919e-02
## 16/FA-11/FA 0.002681319 -0.16950416 0.174866800 1.000000e+00
## 17/FA-11/FA 0.026178276 -0.14993976 0.202296309 9.999945e-01
## 18/FA-11/FA 0.043609915 -0.13606865 0.223288481 9.994835e-01
## 19/FA-11/FA 0.032413919 -0.14487102 0.209698861 9.999608e-01
## 20/FA-11/FA -0.163899471 -0.33112607 0.003327128 6.057831e-02
## 13/FA-12/FA -0.008918740 -0.19903926 0.181201784 1.000000e+00
## 14/FA-12/FA 0.094667778 -0.09564362 0.284979175 8.815129e-01
## 15/FA-12/FA -0.111140372 -0.29005708 0.067776335 6.481133e-01
## 16/FA-12/FA 0.078173382 -0.10159204 0.257938800 9.483727e-01
## 17/FA-12/FA 0.101670340 -0.08186529 0.285205968 7.893853e-01
## 18/FA-12/FA 0.119101979 -0.06785297 0.306056927 6.116887e-01
## 19/FA-12/FA 0.107905983 -0.07674969 0.292561652 7.288242e-01
## 20/FA-12/FA -0.088407407 -0.26342884 0.086614023 8.707260e-01
## 14/FA-13/FA 0.103586517 -0.06705450 0.274227536 6.798603e-01
## 15/FA-13/FA -0.102221632 -0.26005419 0.055610930 5.870768e-01
## 16/FA-13/FA 0.087092122 -0.07170188 0.245886124 7.995835e-01
## 17/FA-13/FA 0.110589079 -0.05246078 0.273638938 5.156412e-01
## 18/FA-13/FA 0.128020718 -0.03886871 0.294910149 3.219446e-01
## 19/FA-13/FA 0.116824723 -0.04748488 0.281134326 4.405629e-01
## 20/FA-13/FA -0.079488668 -0.23289151 0.073914170 8.511247e-01
## 15/FA-14/FA -0.205808149 -0.36387058 -0.047745719 1.417294e-03
## 16/FA-14/FA -0.016494396 -0.17551688 0.142528085 9.999998e-01
## 17/FA-14/FA 0.007002562 -0.15626982 0.170274944 1.000000e+00
## 18/FA-14/FA 0.024434201 -0.14267264 0.191541043 9.999953e-01
## 19/FA-14/FA 0.013238205 -0.15129222 0.177768628 1.000000e+00
## 20/FA-14/FA -0.183075185 -0.33671452 -0.029435851 5.965138e-03
## 16/FA-15/FA 0.189313754 0.04412109 0.334506418 1.382634e-03
## 17/FA-15/FA 0.212810711 0.06297536 0.362646058 2.603420e-04
## 18/FA-15/FA 0.230242350 0.07623762 0.384247080 8.238783e-05
## 19/FA-15/FA 0.219046355 0.06784113 0.370251580 1.694074e-04
## 20/FA-15/FA 0.022732964 -0.11654304 0.162008968 9.999865e-01
## 17/FA-16/FA 0.023496958 -0.12735081 0.174344724 9.999913e-01
## 18/FA-16/FA 0.040928597 -0.11406132 0.195918513 9.989205e-01
## 19/FA-16/FA 0.029732601 -0.12247593 0.181941135 9.999276e-01
## 20/FA-16/FA -0.166580790 -0.30694540 -0.026216183 6.329697e-03
## 18/FA-17/FA 0.017431639 -0.14191577 0.176779045 9.999997e-01
## 19/FA-17/FA 0.006235643 -0.15040777 0.162879057 1.000000e+00
## 20/FA-17/FA -0.190077747 -0.33523953 -0.044915961 1.281465e-03
## 19/FA-18/FA -0.011195996 -0.17183218 0.149440192 1.000000e+00
## 20/FA-18/FA -0.207509386 -0.35697099 -0.058047786 4.177413e-04
## 20/FA-19/FA -0.196313390 -0.34288874 -0.049738040 8.450533e-04
write.csv(MathGPApair, "mathgpapair.csv")
lmoutput7<- lm(ACT_Math~Col_Math_Term, data = MathC_Term)
apa.aov.table(lmoutput7, filename = "ACTMath.doc")
##
##
## ANOVA results using ACT_Math as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 1404.50 1 1404.50 107.37 .000
## Col_Math_Term 1053.72 9 117.08 8.95 .000 .07 [.04, .09]
## Error 13054.24 998 13.08
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
MathACTpair<- TukeyHSD(aov(ACT_Math~Col_Math_Term, data = MathC_Term))
MathACTpair<- do.call("rbind", MathACTpair)
MathACTpair
## diff lwr upr p adj
## 12/FA-10/FA -0.50000000 -14.5455243 13.54552434 1.000000e+00
## 13/FA-10/FA -5.00000000 -16.4681226 6.46812260 9.325463e-01
## 14/FA-10/FA -3.16197183 -11.3845798 5.06063614 9.692997e-01
## 15/FA-10/FA -5.86111111 -14.0450390 2.32281678 4.090582e-01
## 16/FA-10/FA -6.40643275 -14.5629043 1.75003880 2.746987e-01
## 17/FA-10/FA -5.60734463 -13.7622178 2.54752855 4.710902e-01
## 18/FA-10/FA -5.96853147 -14.1342294 2.19716649 3.782797e-01
## 19/FA-10/FA -5.91666667 -14.0819735 2.24864021 3.913355e-01
## 20/FA-10/FA -7.29894180 -15.4509219 0.85303830 1.249297e-01
## 13/FA-12/FA -4.50000000 -18.5455243 9.54552434 9.913258e-01
## 14/FA-12/FA -2.66197183 -14.2105735 8.88662981 9.993123e-01
## 15/FA-12/FA -5.36111111 -16.8822045 6.15998232 9.017015e-01
## 16/FA-12/FA -5.90643275 -17.4080390 5.59517349 8.341595e-01
## 17/FA-12/FA -5.10734463 -16.6078174 6.39312817 9.247869e-01
## 18/FA-12/FA -5.46853147 -16.9766825 6.03961961 8.895960e-01
## 19/FA-12/FA -5.41666667 -16.9245402 6.09120691 8.952264e-01
## 20/FA-12/FA -6.79894180 -18.2973633 4.69947973 6.861701e-01
## 14/FA-13/FA 1.83802817 -6.3845798 10.06063614 9.994635e-01
## 15/FA-13/FA -0.86111111 -9.0450390 7.32281678 9.999991e-01
## 16/FA-13/FA -1.40643275 -9.5629043 6.75003880 9.999379e-01
## 17/FA-13/FA -0.60734463 -8.7622178 7.54752855 1.000000e+00
## 18/FA-13/FA -0.96853147 -9.1342294 7.19716649 9.999975e-01
## 19/FA-13/FA -0.91666667 -9.0819735 7.24864021 9.999985e-01
## 20/FA-13/FA -2.29894180 -10.4509219 5.85303830 9.966451e-01
## 15/FA-14/FA -2.69913928 -4.4513155 -0.94696303 5.269576e-05
## 16/FA-14/FA -3.24446092 -4.8635576 -1.62536424 1.429423e-08
## 17/FA-14/FA -2.44537280 -4.0563981 -0.83434750 7.465919e-05
## 18/FA-14/FA -2.80655964 -4.4715128 -1.14160647 4.972378e-06
## 19/FA-14/FA -2.75469484 -4.4177289 -1.09166080 8.127933e-06
## 20/FA-14/FA -4.13696997 -5.7332862 -2.54065375 3.108624e-15
## 16/FA-15/FA -0.54532164 -1.9548847 0.86424138 9.680626e-01
## 17/FA-15/FA 0.25376648 -1.1465179 1.65405086 9.999055e-01
## 18/FA-15/FA -0.10742036 -1.5694268 1.35458610 1.000000e+00
## 19/FA-15/FA -0.05555556 -1.5153761 1.40426500 1.000000e+00
## 20/FA-15/FA -1.43783069 -2.8211670 -0.05449439 3.406649e-02
## 17/FA-16/FA 0.79908812 -0.4306065 2.02878270 5.560685e-01
## 18/FA-16/FA 0.43790128 -0.8616430 1.73744555 9.875464e-01
## 19/FA-16/FA 0.48976608 -0.8073185 1.78685068 9.727849e-01
## 20/FA-16/FA -0.89250905 -2.1028692 0.31785111 3.651217e-01
## 18/FA-17/FA -0.36118684 -1.6506611 0.92828739 9.968144e-01
## 19/FA-17/FA -0.30932203 -1.5963173 0.97767328 9.990380e-01
## 20/FA-17/FA -1.69159717 -2.8911388 -0.49205551 3.676231e-04
## 19/FA-18/FA 0.05186480 -1.3020272 1.40575677 1.000000e+00
## 20/FA-18/FA -1.33041033 -2.6014598 -0.05936088 3.165438e-02
## 20/FA-19/FA -1.38227513 -2.6508097 -0.11374060 2.027401e-02
write.csv(MathGPApair, "mathactpair.csv")
lmoutput8<- lm(ALEKS~Col_Math_Term, data = MathC_Term)
apa.aov.table(lmoutput8, filename = "ALEKS.doc")
##
##
## ANOVA results using ALEKS as the dependent variable
##
##
## Predictor SS df MS F p partial_eta2 CI_90_partial_eta2
## (Intercept) 784.00 1 784.00 2.95 .087
## Col_Math_Term 33463.70 8 4182.96 15.73 .000 .35 [.25, .40]
## Error 61947.62 233 265.87
##
## Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared
MathALEKSpair<- TukeyHSD(aov(ALEKS~Col_Math_Term, data = MathC_Term))
MathALEKSpair<- do.call("rbind", MathALEKSpair)
MathALEKSpair
## diff lwr upr p adj
## 12/FA-11/FA 8.0000000 -64.20394 80.2039402 9.999939e-01
## 13/FA-11/FA 11.0000000 -61.20394 83.2039402 9.999281e-01
## 15/FA-11/FA 10.3333333 -48.62094 69.2876036 9.997924e-01
## 16/FA-11/FA 20.9791667 -30.60582 72.5641532 9.380808e-01
## 17/FA-11/FA 18.5454545 -33.08736 70.1782714 9.699906e-01
## 18/FA-11/FA 13.7037037 -38.28908 65.6964830 9.960145e-01
## 19/FA-11/FA 2.1951220 -49.47966 53.8698996 1.000000e+00
## 20/FA-11/FA -7.5000000 -58.89069 43.8906921 9.999482e-01
## 13/FA-12/FA 3.0000000 -69.20394 75.2039402 1.000000e+00
## 15/FA-12/FA 2.3333333 -56.62094 61.2876036 1.000000e+00
## 16/FA-12/FA 12.9791667 -38.60582 64.5641532 9.971148e-01
## 17/FA-12/FA 10.5454545 -41.08736 62.1782714 9.993543e-01
## 18/FA-12/FA 5.7037037 -46.28908 57.6964830 9.999943e-01
## 19/FA-12/FA -5.8048780 -57.47966 45.8698996 9.999932e-01
## 20/FA-12/FA -15.5000000 -66.89069 35.8906921 9.900874e-01
## 15/FA-13/FA -0.6666667 -59.62094 58.2876036 1.000000e+00
## 16/FA-13/FA 9.9791667 -41.60582 61.5641532 9.995670e-01
## 17/FA-13/FA 7.5454545 -44.08736 59.1782714 9.999477e-01
## 18/FA-13/FA 2.7037037 -49.28908 54.6964830 1.000000e+00
## 19/FA-13/FA -8.8048780 -60.47966 42.8698996 9.998322e-01
## 20/FA-13/FA -18.5000000 -69.89069 32.8906921 9.695793e-01
## 16/FA-15/FA 10.6458333 -19.73850 41.0301688 9.742112e-01
## 17/FA-15/FA 8.2121212 -22.25335 38.6775899 9.953480e-01
## 18/FA-15/FA 3.3703704 -27.70126 34.4419990 9.999948e-01
## 19/FA-15/FA -8.1382114 -38.67474 22.3983185 9.956977e-01
## 20/FA-15/FA -17.8333333 -47.88662 12.2199567 6.432236e-01
## 17/FA-16/FA -2.4337121 -13.08968 8.2222546 9.985475e-01
## 18/FA-16/FA -7.2754630 -19.55760 5.0066767 6.454157e-01
## 19/FA-16/FA -18.7840447 -29.64151 -7.9265818 5.329371e-06
## 20/FA-16/FA -28.4791667 -37.89219 -19.0661392 3.037570e-13
## 18/FA-17/FA -4.8417508 -17.32325 7.6397513 9.526629e-01
## 19/FA-17/FA -16.3503326 -27.43282 -5.2678490 2.170312e-04
## 20/FA-17/FA -26.0454545 -35.71717 -16.3737418 4.404255e-13
## 19/FA-18/FA -11.5085818 -24.16254 1.1453808 1.074050e-01
## 20/FA-18/FA -21.2037037 -32.64238 -9.7650257 7.488033e-07
## 20/FA-19/FA -9.6951220 -19.58840 0.1981536 5.973064e-02
write.csv(MathALEKSpair, "mathactpair.csv")
gpatermenglish<- lm(HSGPA ~ Col_Eng_Term, data = EnglC_Term)
actreading<- lm(ACT_Reading ~ Col_Eng_Term, data = EnglC_Term)
actenglish<- lm(ACT_Engl ~ Col_Eng_Term, data = EnglC_Term)
mccannw<- lm(McCann_W ~ Col_Eng_Term, data = EnglC_Term)
mccannr<- lm(McCann_R ~ Col_Eng_Term, data = EnglC_Term)
summary(gpatermenglish)
##
## Call:
## lm(formula = HSGPA ~ Col_Eng_Term, data = EnglC_Term)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.93443 -0.37827 0.04251 0.39518 1.52967
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.0418952 0.0358478 84.856 < 2e-16 ***
## Col_Eng_Term11/FA 0.0847831 0.0506414 1.674 0.09420 .
## Col_Eng_Term12/FA 0.0478720 0.0533114 0.898 0.36928
## Col_Eng_Term13/FA 0.0560844 0.0527873 1.062 0.28812
## Col_Eng_Term14/FA 0.0009381 0.0501122 0.019 0.98507
## Col_Eng_Term15/FA -0.1015682 0.0470157 -2.160 0.03083 *
## Col_Eng_Term16/FA -0.1074635 0.0471094 -2.281 0.02261 *
## Col_Eng_Term17/FA -0.1188080 0.0469237 -2.532 0.01140 *
## Col_Eng_Term18/FA -0.1437849 0.0486516 -2.955 0.00315 **
## Col_Eng_Term19/FA -0.0234049 0.0473342 -0.494 0.62102
## Col_Eng_Term20/FA -0.1032724 0.0507521 -2.035 0.04196 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5425 on 2835 degrees of freedom
## (291 observations deleted due to missingness)
## Multiple R-squared: 0.01885, Adjusted R-squared: 0.01539
## F-statistic: 5.447 on 10 and 2835 DF, p-value: 4.784e-08
summary(actreading)
##
## Call:
## lm(formula = ACT_Reading ~ Col_Eng_Term, data = EnglC_Term)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11.7176 -3.0848 -0.4079 2.5497 16.0437
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.66667 2.63129 7.474 1.41e-13 ***
## Col_Eng_Term11/FA -1.66667 4.16043 -0.401 0.6888
## Col_Eng_Term12/FA 2.00000 3.72120 0.537 0.5910
## Col_Eng_Term13/FA 5.20000 2.88243 1.804 0.0715 .
## Col_Eng_Term14/FA 1.88078 2.65994 0.707 0.4796
## Col_Eng_Term15/FA 1.54660 2.64993 0.584 0.5596
## Col_Eng_Term16/FA 0.74123 2.64854 0.280 0.7796
## Col_Eng_Term17/FA 0.28967 2.64847 0.109 0.9129
## Col_Eng_Term18/FA 1.05098 2.65440 0.396 0.6922
## Col_Eng_Term19/FA 0.78360 2.65187 0.295 0.7677
## Col_Eng_Term20/FA -0.02488 2.66058 -0.009 0.9925
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.558 on 1312 degrees of freedom
## (1814 observations deleted due to missingness)
## Multiple R-squared: 0.02601, Adjusted R-squared: 0.01859
## F-statistic: 3.504 on 10 and 1312 DF, p-value: 0.0001412
summary(actenglish)
##
## Call:
## lm(formula = ACT_Engl ~ Col_Eng_Term, data = EnglC_Term)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.7457 -2.9736 0.0264 2.4764 17.3013
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.3333 2.4937 7.753 1.79e-14 ***
## Col_Eng_Term11/FA -3.8333 3.9429 -0.972 0.331
## Col_Eng_Term12/FA 1.0000 3.5266 0.284 0.777
## Col_Eng_Term13/FA 4.4000 2.7317 1.611 0.107
## Col_Eng_Term14/FA 1.7843 2.5211 0.708 0.479
## Col_Eng_Term15/FA 0.4250 2.5114 0.169 0.866
## Col_Eng_Term16/FA -0.3598 2.5101 -0.143 0.886
## Col_Eng_Term17/FA -0.6346 2.5100 -0.253 0.800
## Col_Eng_Term18/FA -0.5877 2.5152 -0.234 0.815
## Col_Eng_Term19/FA -0.8098 2.5132 -0.322 0.747
## Col_Eng_Term20/FA -1.2438 2.5215 -0.493 0.622
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.319 on 1313 degrees of freedom
## (1813 observations deleted due to missingness)
## Multiple R-squared: 0.04885, Adjusted R-squared: 0.04161
## F-statistic: 6.744 on 10 and 1313 DF, p-value: 2.648e-10
summary(mccannw)
##
## Call:
## lm(formula = McCann_W ~ Col_Eng_Term, data = EnglC_Term)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4412 -0.7578 0.1321 0.2422 1.6250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.0000 0.8102 7.405 5.82e-13 ***
## Col_Eng_Term15/FA -2.0000 1.1458 -1.745 0.0815 .
## Col_Eng_Term16/FA -1.1240 0.8136 -1.382 0.1678
## Col_Eng_Term17/FA -1.2422 0.8128 -1.528 0.1271
## Col_Eng_Term18/FA -1.1321 0.8128 -1.393 0.1643
## Col_Eng_Term19/FA -1.5588 0.8221 -1.896 0.0585 .
## Col_Eng_Term20/FA -1.6250 0.8352 -1.946 0.0523 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8102 on 486 degrees of freedom
## (2644 observations deleted due to missingness)
## Multiple R-squared: 0.03335, Adjusted R-squared: 0.02142
## F-statistic: 2.795 on 6 and 486 DF, p-value: 0.01107
summary(mccannr)
##
## Call:
## lm(formula = McCann_R ~ Col_Eng_Term, data = EnglC_Term)
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.313 -8.031 -0.467 6.969 51.687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 114.667 7.722 14.850 <2e-16 ***
## Col_Eng_Term12/FA -2.667 15.443 -0.173 0.8630
## Col_Eng_Term13/FA 35.333 15.443 2.288 0.0225 *
## Col_Eng_Term15/FA -5.167 12.209 -0.423 0.6723
## Col_Eng_Term16/FA -13.212 7.817 -1.690 0.0915 .
## Col_Eng_Term17/FA -15.200 7.791 -1.951 0.0516 .
## Col_Eng_Term18/FA -13.635 7.794 -1.749 0.0808 .
## Col_Eng_Term19/FA -16.353 7.860 -2.081 0.0379 *
## Col_Eng_Term20/FA -14.296 8.139 -1.756 0.0796 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.37 on 555 degrees of freedom
## (2573 observations deleted due to missingness)
## Multiple R-squared: 0.03943, Adjusted R-squared: 0.02558
## F-statistic: 2.848 on 8 and 555 DF, p-value: 0.004153
apa.reg.table(mccannr, filename = "table1.doc")
##
##
## Regression results using McCann_R as the criterion
##
##
## Predictor b b_95%_CI sr2 sr2_95%_CI Fit
## (Intercept) 114.67** [99.50, 129.83]
## Col_Eng_Term12/FA -2.67 [-33.00, 27.67] .00 [-.00, .00]
## Col_Eng_Term13/FA 35.33* [5.00, 65.67] .01 [-.01, .02]
## Col_Eng_Term15/FA -5.17 [-29.15, 18.82] .00 [-.00, .00]
## Col_Eng_Term16/FA -13.21 [-28.57, 2.14] .00 [-.01, .02]
## Col_Eng_Term17/FA -15.20 [-30.50, 0.10] .01 [-.01, .02]
## Col_Eng_Term18/FA -13.64 [-28.95, 1.67] .01 [-.01, .02]
## Col_Eng_Term19/FA -16.35* [-31.79, -0.91] .01 [-.01, .02]
## Col_Eng_Term20/FA -14.30 [-30.28, 1.69] .01 [-.01, .02]
## R2 = .039**
## 95% CI[.00,.06]
##
##
## Note. A significant b-weight indicates the semi-partial correlation is also significant.
## b represents unstandardized regression weights.
## sr2 represents the semi-partial correlation squared.
## Square brackets are used to enclose the lower and upper limits of a confidence interval.
## * indicates p < .05. ** indicates p < .01.
##
gpatermmath<- lm(HSGPA ~ Col_Math_Term, data = PlacementMath)
acttermmath<- lm(ACT_Math ~ Col_Math_Term, data = PlacementMath)
aleksmath<- lm(ALEKS ~ Col_Math_Term, data = PlacementMath)
summary(gpatermmath)
##
## Call:
## lm(formula = HSGPA ~ Col_Math_Term, data = PlacementMath)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2856 -0.3257 0.0557 0.3883 1.8079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.14265 0.04651 67.568 <2e-16 ***
## Col_Math_Term11/FA 0.04606 0.06316 0.729 0.4659
## Col_Math_Term12/FA -0.02943 0.06516 -0.452 0.6516
## Col_Math_Term13/FA -0.03835 0.05969 -0.643 0.5206
## Col_Math_Term14/FA 0.06524 0.05974 1.092 0.2750
## Col_Math_Term15/FA -0.14057 0.05625 -2.499 0.0125 *
## Col_Math_Term16/FA 0.04874 0.05651 0.863 0.3885
## Col_Math_Term17/FA 0.07224 0.05767 1.253 0.2104
## Col_Math_Term18/FA 0.08967 0.05871 1.527 0.1268
## Col_Math_Term19/FA 0.07847 0.05801 1.353 0.1762
## Col_Math_Term20/FA -0.11784 0.05506 -2.140 0.0324 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5303 on 2382 degrees of freedom
## (422 observations deleted due to missingness)
## Multiple R-squared: 0.0244, Adjusted R-squared: 0.02031
## F-statistic: 5.958 on 10 and 2382 DF, p-value: 5.731e-09
summary(acttermmath)
##
## Call:
## lm(formula = ACT_Math ~ Col_Math_Term, data = PlacementMath)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.3380 -3.0936 -0.2011 2.6620 12.7989
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 26.500 2.557 10.362 < 2e-16 ***
## Col_Math_Term12/FA -0.500 4.429 -0.113 0.91015
## Col_Math_Term13/FA -5.000 3.617 -1.382 0.16713
## Col_Math_Term14/FA -3.162 2.593 -1.219 0.22300
## Col_Math_Term15/FA -5.861 2.581 -2.271 0.02336 *
## Col_Math_Term16/FA -6.406 2.572 -2.491 0.01292 *
## Col_Math_Term17/FA -5.607 2.572 -2.180 0.02947 *
## Col_Math_Term18/FA -5.968 2.575 -2.318 0.02067 *
## Col_Math_Term19/FA -5.917 2.575 -2.298 0.02179 *
## Col_Math_Term20/FA -7.299 2.571 -2.839 0.00462 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.617 on 998 degrees of freedom
## (1807 observations deleted due to missingness)
## Multiple R-squared: 0.07469, Adjusted R-squared: 0.06635
## F-statistic: 8.951 on 9 and 998 DF, p-value: 4.537e-13
summary(aleksmath)
##
## Call:
## lm(formula = ALEKS ~ Col_Math_Term, data = PlacementMath)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.704 -10.859 -0.979 7.729 65.500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.000 16.306 1.717 0.0873 .
## Col_Math_Term12/FA 8.000 23.059 0.347 0.7290
## Col_Math_Term13/FA 11.000 23.059 0.477 0.6338
## Col_Math_Term15/FA 10.333 18.828 0.549 0.5836
## Col_Math_Term16/FA 20.979 16.474 1.273 0.2041
## Col_Math_Term17/FA 18.545 16.490 1.125 0.2619
## Col_Math_Term18/FA 13.704 16.605 0.825 0.4101
## Col_Math_Term19/FA 2.195 16.503 0.133 0.8943
## Col_Math_Term20/FA -7.500 16.412 -0.457 0.6481
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.31 on 233 degrees of freedom
## (2573 observations deleted due to missingness)
## Multiple R-squared: 0.3507, Adjusted R-squared: 0.3284
## F-statistic: 15.73 on 8 and 233 DF, p-value: < 2.2e-16
apa.reg.table(aleksmath, filename = "table1.doc")
##
##
## Regression results using ALEKS as the criterion
##
##
## Predictor b b_95%_CI sr2 sr2_95%_CI Fit
## (Intercept) 28.00 [-4.13, 60.13]
## Col_Math_Term12/FA 8.00 [-37.43, 53.43] .00 [-.00, .00]
## Col_Math_Term13/FA 11.00 [-34.43, 56.43] .00 [-.00, .01]
## Col_Math_Term15/FA 10.33 [-26.76, 47.43] .00 [-.01, .01]
## Col_Math_Term16/FA 20.98 [-11.48, 53.44] .00 [-.01, .02]
## Col_Math_Term17/FA 18.55 [-13.94, 51.03] .00 [-.01, .02]
## Col_Math_Term18/FA 13.70 [-19.01, 46.42] .00 [-.01, .01]
## Col_Math_Term19/FA 2.20 [-30.32, 34.71] .00 [-.00, .00]
## Col_Math_Term20/FA -7.50 [-39.84, 24.84] .00 [-.00, .01]
## R2 = .351**
## 95% CI[.24,.42]
##
##
## Note. A significant b-weight indicates the semi-partial correlation is also significant.
## b represents unstandardized regression weights.
## sr2 represents the semi-partial correlation squared.
## Square brackets are used to enclose the lower and upper limits of a confidence interval.
## * indicates p < .05. ** indicates p < .01.
##
library(questionr)
Placement2<- Placement %>%
subset(Col_Eng_Term %in% c("10/FA", "11/FA", "12/FA", "13/FA", "14/FA", "15/FA", "16/FA", "17/FA", "18/FA", "19/FA", "20/FA"))%>%
mutate(gpahave = if_else(!is.na (HSGPA), 1, 0))
Placement3<- Placement %>%
subset(Col_Math_Term %in% c("10/FA", "11/FA", "12/FA", "13/FA", "14/FA", "15/FA", "16/FA", "17/FA", "18/FA", "19/FA", "20/FA"))%>%
mutate(gpahave = if_else(!is.na (HSGPA), 1, 0))
EnglC_Term<- EnglC_Term %>%
mutate(gpahave = if_else(!is.na (HSGPA), 1, 0)) %>%
subset(Col_Eng_Term %in% c("14/FA", "15/FA", "16/FA", "17/FA", "18/FA", "19/FA", "20/FA"))
PlacementMath<- PlacementMath %>%
mutate(gpahave = if_else(!is.na (HSGPA), 1, 0)) %>%
subset(Col_Math_Term %in% c("14/FA", "15/FA", "16/FA", "17/FA", "18/FA", "19/FA", "20/FA"))
statistics<- statistics %>%
mutate(gpahave = if_else(!is.na (HSGPA), 1, 0)) %>%
subset(Col_Math_Term %in% c("14/FA", "15/FA", "16/FA", "17/FA", "18/FA", "19/FA", "20/FA"))
math1400<- math1400 %>%
mutate(gpahave = if_else(!is.na (HSGPA), 1, 0)) %>%
subset(Col_Math_Term %in% c("14/FA", "15/FA", "16/FA", "17/FA", "18/FA", "19/FA", "20/FA"))
chienglish<- CrossTable(Placement2$College_English_Pass, Placement2$gpahave, chisq = TRUE, prop.r = FALSE, prop.c = TRUE, expected = FALSE, prop.t = FALSE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 3137
##
##
## | Placement2$gpahave
## Placement2$College_English_Pass | 0 | 1 | Row Total |
## --------------------------------|-----------|-----------|-----------|
## 0 | 29 | 331 | 360 |
## | 0.578 | 0.059 | |
## | 0.100 | 0.116 | |
## --------------------------------|-----------|-----------|-----------|
## 1 | 262 | 2515 | 2777 |
## | 0.075 | 0.008 | |
## | 0.900 | 0.884 | |
## --------------------------------|-----------|-----------|-----------|
## Column Total | 291 | 2846 | 3137 |
## | 0.093 | 0.907 | |
## --------------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 0.7201912 d.f. = 1 p = 0.3960812
##
## Pearson's Chi-squared test with Yates' continuity correction
## ------------------------------------------------------------
## Chi^2 = 0.5656451 d.f. = 1 p = 0.4519946
##
##
chimath<- CrossTable(Placement3$College_Math_Pass, Placement3$gpahave, chisq = TRUE, prop.r = FALSE, prop.c = TRUE, expected = FALSE, prop.t = FALSE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 2815
##
##
## | Placement3$gpahave
## Placement3$College_Math_Pass | 0 | 1 | Row Total |
## -----------------------------|-----------|-----------|-----------|
## 0 | 63 | 460 | 523 |
## | 3.026 | 0.534 | |
## | 0.149 | 0.192 | |
## -----------------------------|-----------|-----------|-----------|
## 1 | 359 | 1933 | 2292 |
## | 0.691 | 0.122 | |
## | 0.851 | 0.808 | |
## -----------------------------|-----------|-----------|-----------|
## Column Total | 422 | 2393 | 2815 |
## | 0.150 | 0.850 | |
## -----------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 4.372255 d.f. = 1 p = 0.03652861
##
## Pearson's Chi-squared test with Yates' continuity correction
## ------------------------------------------------------------
## Chi^2 = 4.093015 d.f. = 1 p = 0.04306076
##
##
chistat<- CrossTable(statistics$College_Math_Pass, statistics$gpahave, chisq = TRUE, prop.r = FALSE, prop.c = TRUE, expected = FALSE, prop.t = FALSE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 525
##
##
## | statistics$gpahave
## statistics$College_Math_Pass | 0 | 1 | Row Total |
## -----------------------------|-----------|-----------|-----------|
## 0 | 7 | 59 | 66 |
## | 0.683 | 0.116 | |
## | 0.092 | 0.131 | |
## -----------------------------|-----------|-----------|-----------|
## 1 | 69 | 390 | 459 |
## | 0.098 | 0.017 | |
## | 0.908 | 0.869 | |
## -----------------------------|-----------|-----------|-----------|
## Column Total | 76 | 449 | 525 |
## | 0.145 | 0.855 | |
## -----------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 0.9132723 d.f. = 1 p = 0.3392477
##
## Pearson's Chi-squared test with Yates' continuity correction
## ------------------------------------------------------------
## Chi^2 = 0.5907219 d.f. = 1 p = 0.4421401
##
##
chi14<- CrossTable(math1400$College_Math_Pass, math1400$gpahave, chisq = TRUE, prop.r = FALSE, prop.c = TRUE, expected = FALSE, prop.t = FALSE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Col Total |
## |-------------------------|
##
##
## Total Observations in Table: 1359
##
##
## | math1400$gpahave
## math1400$College_Math_Pass | 0 | 1 | Row Total |
## ---------------------------|-----------|-----------|-----------|
## 0 | 16 | 240 | 256 |
## | 3.161 | 0.340 | |
## | 0.121 | 0.196 | |
## ---------------------------|-----------|-----------|-----------|
## 1 | 116 | 987 | 1103 |
## | 0.734 | 0.079 | |
## | 0.879 | 0.804 | |
## ---------------------------|-----------|-----------|-----------|
## Column Total | 132 | 1227 | 1359 |
## | 0.097 | 0.903 | |
## ---------------------------|-----------|-----------|-----------|
##
##
## Statistics for All Table Factors
##
##
## Pearson's Chi-squared test
## ------------------------------------------------------------
## Chi^2 = 4.313357 d.f. = 1 p = 0.03781427
##
## Pearson's Chi-squared test with Yates' continuity correction
## ------------------------------------------------------------
## Chi^2 = 3.840536 d.f. = 1 p = 0.05002753
##
##
chienglish
## $t
## y
## x 0 1
## 0 29 331
## 1 262 2515
##
## $prop.row
## y
## x 0 1
## 0 0.08055556 0.91944444
## 1 0.09434642 0.90565358
##
## $prop.col
## y
## x 0 1
## 0 0.09965636 0.11630358
## 1 0.90034364 0.88369642
##
## $prop.tbl
## y
## x 0 1
## 0 0.009244501 0.105514823
## 1 0.083519286 0.801721390
##
## $chisq
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.72019, df = 1, p-value = 0.3961
##
##
## $chisq.corr
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 0.56565, df = 1, p-value = 0.452
chimath
## $t
## y
## x 0 1
## 0 63 460
## 1 359 1933
##
## $prop.row
## y
## x 0 1
## 0 0.1204589 0.8795411
## 1 0.1566318 0.8433682
##
## $prop.col
## y
## x 0 1
## 0 0.1492891 0.1922273
## 1 0.8507109 0.8077727
##
## $prop.tbl
## y
## x 0 1
## 0 0.02238011 0.16341030
## 1 0.12753108 0.68667851
##
## $chisq
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 4.3723, df = 1, p-value = 0.03653
##
##
## $chisq.corr
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 4.093, df = 1, p-value = 0.04306
chistat
## $t
## y
## x 0 1
## 0 7 59
## 1 69 390
##
## $prop.row
## y
## x 0 1
## 0 0.1060606 0.8939394
## 1 0.1503268 0.8496732
##
## $prop.col
## y
## x 0 1
## 0 0.09210526 0.13140312
## 1 0.90789474 0.86859688
##
## $prop.tbl
## y
## x 0 1
## 0 0.01333333 0.11238095
## 1 0.13142857 0.74285714
##
## $chisq
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 0.91327, df = 1, p-value = 0.3392
##
##
## $chisq.corr
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 0.59072, df = 1, p-value = 0.4421
chi14
## $t
## y
## x 0 1
## 0 16 240
## 1 116 987
##
## $prop.row
## y
## x 0 1
## 0 0.0625000 0.9375000
## 1 0.1051677 0.8948323
##
## $prop.col
## y
## x 0 1
## 0 0.1212121 0.1955990
## 1 0.8787879 0.8044010
##
## $prop.tbl
## y
## x 0 1
## 0 0.01177336 0.17660044
## 1 0.08535688 0.72626932
##
## $chisq
##
## Pearson's Chi-squared test
##
## data: t
## X-squared = 4.3134, df = 1, p-value = 0.03781
##
##
## $chisq.corr
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: t
## X-squared = 3.8405, df = 1, p-value = 0.05003
Enggpa<- glm(formula = College_English_Pass ~ Col_Eng_Term + gpahave + ACT_Engl + ACT_Reading + McCann_R + McCann_W, family = "binomial", data = EnglC_Term)
summary(Enggpa)
##
## Call:
## glm(formula = College_English_Pass ~ Col_Eng_Term + gpahave +
## ACT_Engl + ACT_Reading + McCann_R + McCann_W, family = "binomial",
## data = EnglC_Term)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2985 0.4179 0.5709 0.6579 1.2734
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.76710 1.84514 -1.500 0.134
## Col_Eng_Term17/FA 0.08835 0.45887 0.193 0.847
## Col_Eng_Term18/FA 0.32949 0.48424 0.680 0.496
## Col_Eng_Term19/FA 0.17479 0.69801 0.250 0.802
## Col_Eng_Term20/FA 0.60680 1.13808 0.533 0.594
## gpahave 0.67761 0.72106 0.940 0.347
## ACT_Engl 0.08763 0.06092 1.439 0.150
## ACT_Reading -0.03872 0.05989 -0.646 0.518
## McCann_R 0.02401 0.01725 1.392 0.164
## McCann_W 0.09801 0.21433 0.457 0.647
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 231.47 on 246 degrees of freedom
## Residual deviance: 222.38 on 237 degrees of freedom
## (1931 observations deleted due to missingness)
## AIC: 242.38
##
## Number of Fisher Scoring iterations: 4
enggpaoddrat<- odds.ratio(Enggpa)
enggpaoddrat
## OR 2.5 % 97.5 % p
## (Intercept) 0.062844 0.001537 2.2522 0.1337
## Col_Eng_Term17/FA 1.092371 0.432271 2.6532 0.8473
## Col_Eng_Term18/FA 1.390262 0.529258 3.5991 0.4962
## Col_Eng_Term19/FA 1.190999 0.319489 5.2158 0.8023
## Col_Eng_Term20/FA 1.834557 0.270531 36.8383 0.5939
## gpahave 1.969159 0.403562 7.4790 0.3474
## ACT_Engl 1.091584 0.970979 1.2341 0.1503
## ACT_Reading 0.962022 0.855134 1.0826 0.5180
## McCann_R 1.024298 0.990725 1.0604 0.1639
## McCann_W 1.102975 0.722109 1.6807 0.6475
VIF(Enggpa)
## GVIF Df GVIF^(1/(2*Df))
## Col_Eng_Term 1.123352 4 1.014646
## gpahave 1.069883 1 1.034352
## ACT_Engl 1.731680 1 1.315933
## ACT_Reading 1.627449 1 1.275715
## McCann_R 1.376891 1 1.173410
## McCann_W 1.104385 1 1.050897
PseudoR2(Enggpa)
## McFadden
## 0.03926004
EngMod<- glm(formula = College_English_Pass ~ Col_Eng_Term + HSGPA + ACT_Engl + ACT_Reading + McCann_R + McCann_W, family = "binomial", data = EnglC_Term)
summary(EngMod)
##
## Call:
## glm(formula = College_English_Pass ~ Col_Eng_Term + HSGPA + ACT_Engl +
## ACT_Reading + McCann_R + McCann_W, family = "binomial", data = EnglC_Term)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4856 0.3108 0.5105 0.6572 1.5517
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.728047 1.963976 -2.407 0.01607 *
## Col_Eng_Term17/FA 0.035306 0.482152 0.073 0.94163
## Col_Eng_Term18/FA 0.341419 0.525484 0.650 0.51587
## Col_Eng_Term19/FA 0.006747 0.731056 0.009 0.99264
## Col_Eng_Term20/FA 0.375553 1.216830 0.309 0.75760
## HSGPA 1.394153 0.482860 2.887 0.00389 **
## ACT_Engl 0.028320 0.066913 0.423 0.67213
## ACT_Reading -0.080679 0.068226 -1.183 0.23700
## McCann_R 0.032547 0.018267 1.782 0.07479 .
## McCann_W 0.090782 0.230161 0.394 0.69326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 217.18 on 233 degrees of freedom
## Residual deviance: 199.43 on 224 degrees of freedom
## (1944 observations deleted due to missingness)
## AIC: 219.43
##
## Number of Fisher Scoring iterations: 5
engoddrat<- odds.ratio(EngMod)
engoddrat
## OR 2.5 % 97.5 % p
## (Intercept) 0.00884372 0.00015641 0.3681 0.016067 *
## Col_Eng_Term17/FA 1.03593655 0.38960235 2.6245 0.941627
## Col_Eng_Term18/FA 1.40694256 0.49369615 3.9609 0.515871
## Col_Eng_Term19/FA 1.00676939 0.25075291 4.6681 0.992637
## Col_Eng_Term20/FA 1.45579602 0.17932599 32.1151 0.757601
## HSGPA 4.03155729 1.60660668 10.7506 0.003886 **
## ACT_Engl 1.02872455 0.90294616 1.1752 0.672128
## ACT_Reading 0.92248967 0.80600698 1.0546 0.236995
## McCann_R 1.03308222 0.99751916 1.0720 0.074795 .
## McCann_W 1.09503078 0.69590177 1.7244 0.693263
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VIF(EngMod)
## GVIF Df GVIF^(1/(2*Df))
## Col_Eng_Term 1.143190 4 1.016868
## HSGPA 1.223051 1 1.105916
## ACT_Engl 1.582757 1 1.258077
## ACT_Reading 1.633985 1 1.278274
## McCann_R 1.355942 1 1.164449
## McCann_W 1.164407 1 1.079077
PseudoR2(EngMod)
## McFadden
## 0.08171396
EngMod2<- glm(formula = College_English_Pass ~ Col_Eng_Term + HSGPA + ACT_Engl + ACT_Reading, family = "binomial", data = EnglC_Term)
summary(EngMod2)
##
## Call:
## glm(formula = College_English_Pass ~ Col_Eng_Term + HSGPA + ACT_Engl +
## ACT_Reading, family = "binomial", data = EnglC_Term)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6679 0.2560 0.3814 0.5468 1.4893
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.77889 0.69333 -4.008 6.12e-05 ***
## Col_Eng_Term15/FA -0.61323 0.40633 -1.509 0.1312
## Col_Eng_Term16/FA -0.05321 0.41755 -0.127 0.8986
## Col_Eng_Term17/FA -0.32160 0.40213 -0.800 0.4238
## Col_Eng_Term18/FA -0.17850 0.42757 -0.417 0.6763
## Col_Eng_Term19/FA -0.33630 0.41781 -0.805 0.4209
## Col_Eng_Term20/FA -0.87053 0.41699 -2.088 0.0368 *
## HSGPA 1.75777 0.18747 9.376 < 2e-16 ***
## ACT_Engl -0.01552 0.03032 -0.512 0.6087
## ACT_Reading 0.01400 0.02862 0.489 0.6247
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 982.02 on 1259 degrees of freedom
## Residual deviance: 856.67 on 1250 degrees of freedom
## (918 observations deleted due to missingness)
## AIC: 876.67
##
## Number of Fisher Scoring iterations: 5
engoddrat2<- odds.ratio(EngMod2)
engoddrat2
## OR 2.5 % 97.5 % p
## (Intercept) 0.062108 0.015875 0.2422 6.123e-05 ***
## Col_Eng_Term15/FA 0.541599 0.234159 1.1679 0.13125
## Col_Eng_Term16/FA 0.948184 0.402991 2.1023 0.89860
## Col_Eng_Term17/FA 0.724985 0.315799 1.5493 0.42385
## Col_Eng_Term18/FA 0.836520 0.349712 1.8980 0.67632
## Col_Eng_Term19/FA 0.714407 0.303243 1.5828 0.42086
## Col_Eng_Term20/FA 0.418729 0.177728 0.9238 0.03683 *
## HSGPA 5.799485 4.039452 8.4314 < 2.2e-16 ***
## ACT_Engl 0.984598 0.927612 1.0447 0.60866
## ACT_Reading 1.014100 0.959045 1.0730 0.62470
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VIF(EngMod2)
## GVIF Df GVIF^(1/(2*Df))
## Col_Eng_Term 1.062018 6 1.005027
## HSGPA 1.127473 1 1.061825
## ACT_Engl 2.060378 1 1.435402
## ACT_Reading 2.009945 1 1.417725
PseudoR2(EngMod2)
## McFadden
## 0.1276496
EngMod3<- glm(formula = College_English_Pass ~ Col_Eng_Term + HSGPA + ACT_Reading, family = "binomial", data = EnglC_Term)
summary(EngMod3)
##
## Call:
## glm(formula = College_English_Pass ~ Col_Eng_Term + HSGPA + ACT_Reading,
## family = "binomial", data = EnglC_Term)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6711 0.2552 0.3809 0.5513 1.5120
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.864309 0.681706 -4.202 2.65e-05 ***
## Col_Eng_Term15/FA -0.609819 0.405753 -1.503 0.1329
## Col_Eng_Term16/FA -0.037847 0.416388 -0.091 0.9276
## Col_Eng_Term17/FA -0.303864 0.400861 -0.758 0.4484
## Col_Eng_Term18/FA -0.162807 0.425626 -0.383 0.7021
## Col_Eng_Term19/FA -0.305602 0.415365 -0.736 0.4619
## Col_Eng_Term20/FA -0.892241 0.413249 -2.159 0.0308 *
## HSGPA 1.740912 0.185277 9.396 < 2e-16 ***
## ACT_Reading 0.005766 0.021268 0.271 0.7863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 987.21 on 1264 degrees of freedom
## Residual deviance: 860.76 on 1256 degrees of freedom
## (913 observations deleted due to missingness)
## AIC: 878.76
##
## Number of Fisher Scoring iterations: 5
engoddrat3<- odds.ratio(EngMod3)
engoddrat3
## OR 2.5 % 97.5 % p
## (Intercept) 0.057023 0.014927 0.2176 2.649e-05 ***
## Col_Eng_Term15/FA 0.543449 0.235187 1.1705 0.13286
## Col_Eng_Term16/FA 0.962860 0.410035 2.1295 0.92758
## Col_Eng_Term17/FA 0.737961 0.322118 1.5726 0.44843
## Col_Eng_Term18/FA 0.849755 0.356459 1.9203 0.70208
## Col_Eng_Term19/FA 0.736680 0.314026 1.6237 0.46189
## Col_Eng_Term20/FA 0.409737 0.175007 0.8965 0.03084 *
## HSGPA 5.702539 3.989012 8.2546 < 2.2e-16 ***
## ACT_Reading 1.005782 0.965029 1.0490 0.78632
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VIF(EngMod3)
## GVIF Df GVIF^(1/(2*Df))
## Col_Eng_Term 1.044827 6 1.003661
## HSGPA 1.100492 1 1.049043
## ACT_Reading 1.110238 1 1.053678
PseudoR2(EngMod3)
## McFadden
## 0.1280876
EngMod4<- glm(formula = College_English_Pass ~ Col_Eng_Term + HSGPA, family = "binomial", data = Placement2)
summary(EngMod4)
##
## Call:
## glm(formula = College_English_Pass ~ Col_Eng_Term + HSGPA, family = "binomial",
## data = Placement2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6739 0.2969 0.4031 0.5383 1.2404
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7702 0.4182 -1.841 0.065553 .
## Col_Eng_Term11/FA -0.3101 0.3796 -0.817 0.413979
## Col_Eng_Term12/FA -0.6210 0.3732 -1.664 0.096055 .
## Col_Eng_Term13/FA -0.4619 0.3809 -1.213 0.225171
## Col_Eng_Term14/FA -0.1837 0.3752 -0.490 0.624463
## Col_Eng_Term15/FA -0.9562 0.3218 -2.972 0.002961 **
## Col_Eng_Term16/FA -0.8325 0.3253 -2.559 0.010488 *
## Col_Eng_Term17/FA -0.7519 0.3263 -2.304 0.021211 *
## Col_Eng_Term18/FA -0.6018 0.3383 -1.779 0.075217 .
## Col_Eng_Term19/FA -0.5544 0.3387 -1.637 0.101711
## Col_Eng_Term20/FA -1.1910 0.3301 -3.608 0.000308 ***
## HSGPA 1.2095 0.1131 10.698 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2046.2 on 2845 degrees of freedom
## Residual deviance: 1886.8 on 2834 degrees of freedom
## (291 observations deleted due to missingness)
## AIC: 1910.8
##
## Number of Fisher Scoring iterations: 5
engoddrat4<- odds.ratio(EngMod4)
VIF(EngMod4)
## GVIF Df GVIF^(1/(2*Df))
## Col_Eng_Term 1.011344 10 1.000564
## HSGPA 1.011344 1 1.005656
engoddrat4
## OR 2.5 % 97.5 % p
## (Intercept) 0.46293 0.20699 1.0723 0.065553 .
## Col_Eng_Term11/FA 0.73338 0.34366 1.5416 0.413979
## Col_Eng_Term12/FA 0.53738 0.25425 1.1109 0.096055 .
## Col_Eng_Term13/FA 0.63006 0.29454 1.3276 0.225171
## Col_Eng_Term14/FA 0.83220 0.39275 1.7310 0.624463
## Col_Eng_Term15/FA 0.38434 0.19778 0.7043 0.002961 **
## Col_Eng_Term16/FA 0.43494 0.22253 0.8037 0.010488 *
## Col_Eng_Term17/FA 0.47149 0.24085 0.8733 0.021211 *
## Col_Eng_Term18/FA 0.54780 0.27438 1.0433 0.075217 .
## Col_Eng_Term19/FA 0.57444 0.28754 1.0953 0.101711
## Col_Eng_Term20/FA 0.30392 0.15421 0.5673 0.000308 ***
## HSGPA 3.35182 2.69044 4.1919 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
PseudoR2(EngMod4)
## McFadden
## 0.07792522
write.csv(enggpaoddrat, "enggpa.csv")
write.csv(engoddrat, "engmod.csv")
write.csv(engoddrat2, "engmod2.csv")
write.csv(engoddrat3, "engmod3.csv")
write.csv(engoddrat4, "engmod4.csv")
Mathgpa<- glm(formula = College_Math_Pass ~ Col_Math_Term + gpahave + ACT_Math, family = "binomial", data = PlacementMath)
summary(Mathgpa)
##
## Call:
## glm(formula = College_Math_Pass ~ Col_Math_Term + gpahave + ACT_Math,
## family = "binomial", data = PlacementMath)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0572 0.5493 0.6303 0.6955 0.8758
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.84971 0.79322 1.071 0.2841
## Col_Math_Term15/FA -0.15958 0.41431 -0.385 0.7001
## Col_Math_Term16/FA -0.08110 0.38975 -0.208 0.8352
## Col_Math_Term17/FA -0.07779 0.38651 -0.201 0.8405
## Col_Math_Term18/FA 0.08466 0.40537 0.209 0.8346
## Col_Math_Term19/FA -0.39331 0.38829 -1.013 0.3111
## Col_Math_Term20/FA -0.37461 0.38158 -0.982 0.3262
## gpahave -0.21828 0.49882 -0.438 0.6617
## ACT_Math 0.04579 0.02245 2.040 0.0413 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 999.36 on 1002 degrees of freedom
## Residual deviance: 988.62 on 994 degrees of freedom
## (994 observations deleted due to missingness)
## AIC: 1006.6
##
## Number of Fisher Scoring iterations: 4
mathgpaodd<- odds.ratio(Mathgpa)
mathgpaodd
## OR 2.5 % 97.5 % p
## (Intercept) 2.33897 0.51199 11.7006 0.28407
## Col_Math_Term15/FA 0.85250 0.36782 1.8911 0.70012
## Col_Math_Term16/FA 0.92210 0.41434 1.9329 0.83516
## Col_Math_Term17/FA 0.92516 0.41808 1.9261 0.84049
## Col_Math_Term18/FA 1.08835 0.47686 2.3672 0.83456
## Col_Math_Term19/FA 0.67482 0.30389 1.4094 0.31110
## Col_Math_Term20/FA 0.68756 0.31299 1.4135 0.32624
## gpahave 0.80390 0.26763 1.9716 0.66168
## ACT_Math 1.04686 1.00221 1.0945 0.04134 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VIF(Mathgpa)
## GVIF Df GVIF^(1/(2*Df))
## Col_Math_Term 1.077427 6 1.006234
## gpahave 1.006022 1 1.003007
## ACT_Math 1.071883 1 1.035318
PseudoR2(Mathgpa)
## McFadden
## 0.01074682
PlacementMath2<- PlacementMath%>%
select(College_Math_Pass, Col_Math_Term, HSGPA, ACT_Math)
PlacementMath2<- na.omit(PlacementMath2)
MathMod<- glm(formula = College_Math_Pass ~ Col_Math_Term + HSGPA + ACT_Math, family = "binomial", data = PlacementMath2)
summary(MathMod)
##
## Call:
## glm(formula = College_Math_Pass ~ Col_Math_Term + HSGPA + ACT_Math,
## family = "binomial", data = PlacementMath2)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2832 0.4035 0.5374 0.6789 1.5332
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.906429 0.806690 -3.603 0.000315 ***
## Col_Math_Term15/FA -0.056090 0.432771 -0.130 0.896878
## Col_Math_Term16/FA -0.119278 0.406212 -0.294 0.769037
## Col_Math_Term17/FA -0.065133 0.400549 -0.163 0.870825
## Col_Math_Term18/FA 0.147877 0.420890 0.351 0.725331
## Col_Math_Term19/FA -0.432841 0.401770 -1.077 0.281331
## Col_Math_Term20/FA -0.146013 0.400223 -0.365 0.715238
## HSGPA 1.355439 0.185332 7.314 2.6e-13 ***
## ACT_Math 0.004501 0.024097 0.187 0.851824
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 971.67 on 971 degrees of freedom
## Residual deviance: 905.11 on 963 degrees of freedom
## AIC: 923.11
##
## Number of Fisher Scoring iterations: 4
mathodd<- odds.ratio(MathMod)
mathodd
## OR 2.5 % 97.5 % p
## (Intercept) 0.054671 0.011099 0.2637 0.0003147 ***
## Col_Math_Term15/FA 0.945454 0.394586 2.1795 0.8968776
## Col_Math_Term16/FA 0.887561 0.386980 1.9242 0.7690370
## Col_Math_Term17/FA 0.936942 0.412704 2.0075 0.8708252
## Col_Math_Term18/FA 1.159370 0.493805 2.6033 0.7253312
## Col_Math_Term19/FA 0.648664 0.284884 1.3918 0.2813310
## Col_Math_Term20/FA 0.864146 0.380808 1.8493 0.7152380
## HSGPA 3.878464 2.707441 5.6042 2.601e-13 ***
## ACT_Math 1.004511 0.958361 1.0534 0.8518237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VIF(MathMod)
## GVIF Df GVIF^(1/(2*Df))
## Col_Math_Term 1.126722 6 1.009992
## HSGPA 1.093292 1 1.045606
## ACT_Math 1.131525 1 1.063732
PseudoR2(MathMod)
## McFadden
## 0.06849917
hoslem.test(PlacementMath2$College_Math_Pass, fitted(MathMod), g=10)
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: PlacementMath2$College_Math_Pass, fitted(MathMod)
## X-squared = 17.637, df = 8, p-value = 0.02412
PlacementMath3<- Placement3%>%
select(College_Math_Pass, Col_Math_Term, HSGPA)
PlacementMath3<- na.omit(PlacementMath3)
MathMod2<- glm(formula = College_Math_Pass ~ Col_Math_Term + HSGPA, family = "binomial", data = Placement3)
summary(MathMod2)
##
## Call:
## glm(formula = College_Math_Pass ~ Col_Math_Term + HSGPA, family = "binomial",
## data = Placement3)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3266 0.4447 0.5640 0.6757 1.3082
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.77434 0.37804 -2.048 0.0405 *
## Col_Math_Term11/FA -0.58493 0.31517 -1.856 0.0635 .
## Col_Math_Term12/FA -0.62795 0.31979 -1.964 0.0496 *
## Col_Math_Term13/FA -0.48575 0.30127 -1.612 0.1069
## Col_Math_Term14/FA 0.22310 0.33160 0.673 0.5011
## Col_Math_Term15/FA -0.04802 0.29517 -0.163 0.8708
## Col_Math_Term16/FA -0.20209 0.29670 -0.681 0.4958
## Col_Math_Term17/FA -0.14389 0.30492 -0.472 0.6370
## Col_Math_Term18/FA -0.15084 0.31065 -0.486 0.6273
## Col_Math_Term19/FA -0.55056 0.29556 -1.863 0.0625 .
## Col_Math_Term20/FA -0.47969 0.28143 -1.704 0.0883 .
## HSGPA 0.81408 0.09660 8.427 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2342.4 on 2392 degrees of freedom
## Residual deviance: 2245.2 on 2381 degrees of freedom
## (422 observations deleted due to missingness)
## AIC: 2269.2
##
## Number of Fisher Scoring iterations: 4
mathodd2<- odds.ratio(MathMod2)
mathodd2
## OR 2.5 % 97.5 % p
## (Intercept) 0.46101 0.22146 0.9779 0.04053 *
## Col_Math_Term11/FA 0.55715 0.29616 1.0241 0.06346 .
## Col_Math_Term12/FA 0.53369 0.28137 0.9908 0.04957 *
## Col_Math_Term13/FA 0.61524 0.33516 1.0972 0.10689
## Col_Math_Term14/FA 1.24994 0.64741 2.3915 0.50108
## Col_Math_Term15/FA 0.95311 0.52510 1.6787 0.87076
## Col_Math_Term16/FA 0.81702 0.44880 1.4434 0.49580
## Col_Math_Term17/FA 0.86598 0.46903 1.5582 0.63700
## Col_Math_Term18/FA 0.85999 0.46122 1.5673 0.62728
## Col_Math_Term19/FA 0.57663 0.31721 1.0154 0.06249 .
## Col_Math_Term20/FA 0.61898 0.34912 1.0573 0.08830 .
## HSGPA 2.25709 1.86894 2.7299 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
VIF(MathMod2)
## GVIF Df GVIF^(1/(2*Df))
## Col_Math_Term 1.031631 10 1.001558
## HSGPA 1.031631 1 1.015692
PseudoR2(MathMod2)
## McFadden
## 0.04151638
hoslem.test(PlacementMath3$College_Math_Pass, fitted(MathMod2), g=10)
##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: PlacementMath3$College_Math_Pass, fitted(MathMod2)
## X-squared = 23.266, df = 8, p-value = 0.003039
write.csv(mathgpaodd, "mathgpa.csv")
write.csv(mathodd, "mathmod.csv")
write.csv(mathodd2, "mathmod2.csv")
describe.by(math1400, math1400$Col_Math_Term)
##
## Descriptive statistics by group
## group: 14/FA
## vars n mean sd median trimmed mad
## LCCCGPA 1 178 2.92 0.81 3.16 3.02 0.63
## HSGPA 2 152 3.22 0.52 3.26 3.25 0.54
## HighSchoolEndYear 3 178 2009.91 6.47 2013.00 2011.19 1.48
## HSDate* 4 178 13.28 4.99 14.00 13.38 1.48
## StartTerm* 5 178 21.05 5.94 22.00 21.97 4.45
## StartDate* 6 178 19.02 6.85 23.00 20.04 1.48
## HSGradYears* 7 178 16.18 12.10 11.00 14.62 2.97
## CourseName.x* 8 0 NaN NA NA NaN NA
## Developmental_English_Grade* 9 0 NaN NA NA NaN NA
## Location* 10 0 NaN NA NA NaN NA
## Dev_Eng_Term* 11 0 NaN NA NA NaN NA
## Developmental_English_Pass 12 0 NaN NA NA NaN NA
## DevEnglGradeNumber 13 0 NaN NA NA NaN NA
## CourseName.y* 14 98 1.00 0.00 1.00 1.00 0.00
## College_English_Grade* 15 98 1.96 1.16 2.00 1.76 1.48
## Col_Eng_Term* 16 98 6.71 1.95 7.00 6.72 1.48
## College_English_Pass 17 98 0.92 0.28 1.00 1.00 0.00
## ColEnglGradeNumber 18 98 3.04 1.16 3.00 3.24 1.48
## CourseName.x.x* 19 17 1.88 0.33 2.00 1.93 0.00
## Developmental_Math_Grade* 20 17 1.53 0.72 1.00 1.47 0.00
## Dev_Math_Term* 21 17 3.94 1.98 4.00 3.87 1.48
## Developmental_Math_Pass 22 17 1.00 0.00 1.00 1.00 0.00
## DevMathGradeNumber 23 17 3.47 0.72 4.00 3.53 0.00
## CourseName.y.y* 24 178 1.00 0.00 1.00 1.00 0.00
## College_Math_Grade* 25 178 2.38 1.20 2.00 2.25 1.48
## Col_Math_Term* 26 178 1.00 0.00 1.00 1.00 0.00
## College_Math_Pass 27 178 0.86 0.35 1.00 0.94 0.00
## ColMathGradeNumber 28 178 2.62 1.20 3.00 2.75 1.48
## ALEKS 29 0 NaN NA NA NaN NA
## ACT_Math 30 69 23.42 3.03 24.00 23.56 2.97
## ACT_Reading 31 68 23.69 4.21 23.00 23.62 4.45
## ACT_Engl 32 68 23.00 3.38 23.00 22.84 2.97
## McCann_R 33 0 NaN NA NA NaN NA
## McCann_W 34 0 NaN NA NA NaN NA
## AnonId 35 178 4190.68 2369.95 4292.50 4157.11 2749.48
## EnglishStart 36 98 0.43 0.50 0.00 0.41 0.00
## MathStart 37 178 0.34 0.48 0.00 0.31 0.00
## gpahave 38 178 0.85 0.35 1.00 0.94 0.00
## min max range skew kurtosis se
## LCCCGPA 0.00 4.00 4.00 -1.18 1.33 0.06
## HSGPA 1.83 4.09 2.26 -0.46 -0.60 0.04
## HighSchoolEndYear 1982.00 2016.00 34.00 -2.04 4.25 0.48
## HSDate* 1.00 28.00 27.00 0.01 1.59 0.37
## StartTerm* 1.00 32.00 31.00 -1.40 1.64 0.45
## StartDate* 1.00 27.00 26.00 -1.10 -0.32 0.51
## HSGradYears* 1.00 48.00 47.00 1.21 0.31 0.91
## CourseName.x* Inf -Inf -Inf NA NA NA
## Developmental_English_Grade* Inf -Inf -Inf NA NA NA
## Location* Inf -Inf -Inf NA NA NA
## Dev_Eng_Term* Inf -Inf -Inf NA NA NA
## Developmental_English_Pass Inf -Inf -Inf NA NA NA
## DevEnglGradeNumber Inf -Inf -Inf NA NA NA
## CourseName.y* 1.00 1.00 0.00 NaN NaN 0.00
## College_English_Grade* 1.00 5.00 4.00 1.22 0.84 0.12
## Col_Eng_Term* 1.00 12.00 11.00 -0.09 0.51 0.20
## College_English_Pass 0.00 1.00 1.00 -3.01 7.13 0.03
## ColEnglGradeNumber 0.00 4.00 4.00 -1.22 0.84 0.12
## CourseName.x.x* 1.00 2.00 1.00 -2.17 2.88 0.08
## Developmental_Math_Grade* 1.00 3.00 2.00 0.86 -0.69 0.17
## Dev_Math_Term* 1.00 8.00 7.00 0.30 -0.86 0.48
## Developmental_Math_Pass 1.00 1.00 0.00 NaN NaN 0.00
## DevMathGradeNumber 2.00 4.00 2.00 -0.86 -0.69 0.17
## CourseName.y.y* 1.00 1.00 0.00 NaN NaN 0.00
## College_Math_Grade* 1.00 5.00 4.00 0.55 -0.46 0.09
## Col_Math_Term* 1.00 1.00 0.00 NaN NaN 0.00
## College_Math_Pass 0.00 1.00 1.00 -2.05 2.22 0.03
## ColMathGradeNumber 0.00 4.00 4.00 -0.55 -0.46 0.09
## ALEKS Inf -Inf -Inf NA NA NA
## ACT_Math 15.00 31.00 16.00 -0.50 0.32 0.36
## ACT_Reading 14.00 34.00 20.00 0.15 -0.30 0.51
## ACT_Engl 15.00 34.00 19.00 0.62 0.85 0.41
## McCann_R Inf -Inf -Inf NA NA NA
## McCann_W Inf -Inf -Inf NA NA NA
## AnonId 8.00 9210.00 9202.00 0.11 -0.67 177.64
## EnglishStart 0.00 1.00 1.00 0.28 -1.94 0.05
## MathStart 0.00 1.00 1.00 0.66 -1.58 0.04
## gpahave 0.00 1.00 1.00 -1.99 1.96 0.03
## ------------------------------------------------------------
## group: 15/FA
## vars n mean sd median trimmed mad
## LCCCGPA 1 214 2.80 0.75 2.90 2.87 0.79
## HSGPA 2 179 3.04 0.56 3.07 3.06 0.57
## HighSchoolEndYear 3 214 2010.60 5.54 2013.00 2011.54 2.97
## HSDate* 4 214 12.80 4.75 14.00 13.14 2.97
## StartTerm* 5 214 25.56 7.92 28.00 26.96 4.45
## StartDate* 6 214 21.88 7.36 26.00 22.88 2.97
## HSGradYears* 7 214 18.49 11.17 14.00 17.52 2.97
## CourseName.x* 8 6 1.83 0.41 2.00 1.83 0.00
## Developmental_English_Grade* 9 6 1.00 0.00 1.00 1.00 0.00
## Location* 10 6 1.83 0.41 2.00 1.83 0.00
## Dev_Eng_Term* 11 6 1.00 0.00 1.00 1.00 0.00
## Developmental_English_Pass 12 6 1.00 0.00 1.00 1.00 0.00
## DevEnglGradeNumber 13 6 1.00 0.00 1.00 1.00 0.00
## CourseName.y* 14 119 1.00 0.00 1.00 1.00 0.00
## College_English_Grade* 15 119 1.99 1.06 2.00 1.85 1.48
## Col_Eng_Term* 16 119 7.89 2.35 9.00 8.15 1.48
## College_English_Pass 17 119 0.93 0.25 1.00 1.00 0.00
## ColEnglGradeNumber 18 119 3.01 1.06 3.00 3.15 1.48
## CourseName.x.x* 19 16 1.00 0.00 1.00 1.00 0.00
## Developmental_Math_Grade* 20 16 2.19 0.98 2.00 2.14 1.48
## Dev_Math_Term* 21 16 5.88 2.68 7.00 6.00 2.22
## Developmental_Math_Pass 22 16 0.94 0.25 1.00 1.00 0.00
## DevMathGradeNumber 23 16 2.75 1.13 3.00 2.86 1.48
## CourseName.y.y* 24 214 1.03 0.18 1.00 1.00 0.00
## College_Math_Grade* 25 214 2.39 1.26 2.00 2.24 1.48
## Col_Math_Term* 26 214 1.00 0.00 1.00 1.00 0.00
## College_Math_Pass 27 214 0.83 0.37 1.00 0.91 0.00
## ColMathGradeNumber 28 214 2.61 1.26 3.00 2.76 1.48
## ALEKS 29 1 40.00 NA 40.00 40.00 0.00
## ACT_Math 30 82 21.27 3.58 22.00 21.35 2.97
## ACT_Reading 31 82 22.17 4.48 21.00 21.86 4.45
## ACT_Engl 32 83 20.67 4.68 20.00 20.60 4.45
## McCann_R 33 3 110.33 4.16 109.00 110.33 2.97
## McCann_W 34 1 6.00 NA 6.00 6.00 0.00
## AnonId 35 214 4586.47 2325.62 4681.00 4546.44 2301.00
## EnglishStart 36 119 0.37 0.48 0.00 0.34 0.00
## MathStart 37 214 0.29 0.45 0.00 0.23 0.00
## gpahave 38 214 0.84 0.37 1.00 0.92 0.00
## min max range skew kurtosis se
## LCCCGPA 0.20 4 3.80 -0.80 0.55 0.05
## HSGPA 1.44 4 2.56 -0.35 -0.38 0.04
## HighSchoolEndYear 1986.00 2019 33.00 -1.66 2.97 0.38
## HSDate* 1.00 26 25.00 -0.47 0.61 0.32
## StartTerm* 1.00 37 36.00 -1.44 1.22 0.54
## StartDate* 1.00 30 29.00 -0.91 -0.53 0.50
## HSGradYears* 1.00 47 46.00 0.90 0.12 0.76
## CourseName.x* 1.00 2 1.00 -1.36 -0.08 0.17
## Developmental_English_Grade* 1.00 1 0.00 NaN NaN 0.00
## Location* 1.00 2 1.00 -1.36 -0.08 0.17
## Dev_Eng_Term* 1.00 1 0.00 NaN NaN 0.00
## Developmental_English_Pass 1.00 1 0.00 NaN NaN 0.00
## DevEnglGradeNumber 1.00 1 0.00 NaN NaN 0.00
## CourseName.y* 1.00 1 0.00 NaN NaN 0.00
## College_English_Grade* 1.00 5 4.00 1.11 0.93 0.10
## Col_Eng_Term* 1.00 12 11.00 -1.00 0.39 0.22
## College_English_Pass 0.00 1 1.00 -3.41 9.73 0.02
## ColEnglGradeNumber 0.00 4 4.00 -1.11 0.93 0.10
## CourseName.x.x* 1.00 1 0.00 NaN NaN 0.00
## Developmental_Math_Grade* 1.00 4 3.00 0.05 -1.42 0.25
## Dev_Math_Term* 1.00 9 8.00 -0.52 -1.34 0.67
## Developmental_Math_Pass 0.00 1 1.00 -3.28 9.36 0.06
## DevMathGradeNumber 0.00 4 4.00 -0.59 -0.22 0.28
## CourseName.y.y* 1.00 2 1.00 5.22 25.34 0.01
## College_Math_Grade* 1.00 5 4.00 0.76 -0.36 0.09
## Col_Math_Term* 1.00 1 0.00 NaN NaN 0.00
## College_Math_Pass 0.00 1 1.00 -1.76 1.11 0.03
## ColMathGradeNumber 0.00 4 4.00 -0.76 -0.36 0.09
## ALEKS 40.00 40 0.00 NA NA NA
## ACT_Math 13.00 28 15.00 -0.29 -0.88 0.40
## ACT_Reading 14.00 33 19.00 0.56 -0.40 0.49
## ACT_Engl 11.00 32 21.00 0.11 -0.15 0.51
## McCann_R 107.00 115 8.00 0.29 -2.33 2.40
## McCann_W 6.00 6 0.00 NA NA NA
## AnonId 87.00 9262 9175.00 0.02 -0.54 158.98
## EnglishStart 0.00 1 1.00 0.53 -1.73 0.04
## MathStart 0.00 1 1.00 0.95 -1.11 0.03
## gpahave 0.00 1 1.00 -1.81 1.27 0.03
## ------------------------------------------------------------
## group: 16/FA
## vars n mean sd median trimmed mad
## LCCCGPA 1 193 2.81 0.96 3.00 2.95 0.81
## HSGPA 2 168 3.31 0.44 3.39 3.35 0.42
## HighSchoolEndYear 3 193 2013.08 5.81 2016.00 2014.44 0.00
## HSDate* 4 193 14.14 3.68 16.00 14.75 0.00
## StartTerm* 5 193 15.46 3.76 17.00 16.03 0.00
## StartDate* 6 193 16.42 4.69 19.00 17.38 0.00
## HSGradYears* 7 193 12.07 7.06 9.00 10.82 0.00
## CourseName.x* 8 4 1.00 0.00 1.00 1.00 0.00
## Developmental_English_Grade* 9 4 1.75 0.50 2.00 1.75 0.00
## Location* 10 4 1.50 0.58 1.50 1.50 0.74
## Dev_Eng_Term* 11 4 1.25 0.50 1.00 1.25 0.00
## Developmental_English_Pass 12 4 0.25 0.50 0.00 0.25 0.00
## DevEnglGradeNumber 13 4 0.25 0.50 0.00 0.25 0.00
## CourseName.y* 14 103 1.00 0.00 1.00 1.00 0.00
## College_English_Grade* 15 103 2.10 1.29 2.00 1.88 1.48
## Col_Eng_Term* 16 103 6.94 1.80 7.00 6.94 0.00
## College_English_Pass 17 103 0.87 0.33 1.00 0.96 0.00
## ColEnglGradeNumber 18 103 2.90 1.29 3.00 3.12 1.48
## CourseName.x.x* 19 14 2.50 0.94 3.00 2.50 0.00
## Developmental_Math_Grade* 20 14 2.36 0.93 2.50 2.33 0.74
## Dev_Math_Term* 21 14 5.93 3.22 6.00 5.92 4.45
## Developmental_Math_Pass 22 14 0.93 0.27 1.00 1.00 0.00
## DevMathGradeNumber 23 14 2.57 1.09 2.50 2.67 0.74
## CourseName.y.y* 24 193 1.06 0.24 1.00 1.00 0.00
## College_Math_Grade* 25 193 2.37 1.34 2.00 2.22 1.48
## Col_Math_Term* 26 193 1.00 0.00 1.00 1.00 0.00
## College_Math_Pass 27 193 0.83 0.38 1.00 0.91 0.00
## ColMathGradeNumber 28 193 2.63 1.34 3.00 2.78 1.48
## ALEKS 29 31 53.58 17.81 54.00 54.00 19.27
## ACT_Math 30 121 20.52 3.21 21.00 20.45 4.45
## ACT_Reading 31 123 21.61 4.07 21.00 21.45 2.97
## ACT_Engl 32 123 20.44 3.96 20.00 20.27 2.97
## McCann_R 33 16 101.88 9.69 101.00 102.50 3.71
## McCann_W 34 14 5.21 0.70 5.00 5.25 0.74
## AnonId 35 193 5388.44 2122.48 5703.00 5459.33 2054.88
## EnglishStart 36 103 0.61 0.49 1.00 0.64 0.00
## MathStart 37 193 0.59 0.49 1.00 0.61 0.00
## gpahave 38 193 0.87 0.34 1.00 0.96 0.00
## min max range skew kurtosis se
## LCCCGPA 0.00 4 4.00 -1.28 1.25 0.07
## HSGPA 1.64 4 2.36 -1.03 1.53 0.03
## HighSchoolEndYear 1973.00 2018 45.00 -3.40 15.03 0.42
## HSDate* 1.00 24 23.00 -1.41 2.66 0.27
## StartTerm* 1.00 24 23.00 -1.59 2.94 0.27
## StartDate* 1.00 22 21.00 -1.64 1.30 0.34
## HSGradYears* 1.00 34 33.00 1.58 1.73 0.51
## CourseName.x* 1.00 1 0.00 NaN NaN 0.00
## Developmental_English_Grade* 1.00 2 1.00 -0.75 -1.69 0.25
## Location* 1.00 2 1.00 0.00 -2.44 0.29
## Dev_Eng_Term* 1.00 2 1.00 0.75 -1.69 0.25
## Developmental_English_Pass 0.00 1 1.00 0.75 -1.69 0.25
## DevEnglGradeNumber 0.00 1 1.00 0.75 -1.69 0.25
## CourseName.y* 1.00 1 0.00 NaN NaN 0.00
## College_English_Grade* 1.00 5 4.00 1.08 0.13 0.13
## Col_Eng_Term* 1.00 13 12.00 -0.05 2.58 0.18
## College_English_Pass 0.00 1 1.00 -2.22 2.95 0.03
## ColEnglGradeNumber 0.00 4 4.00 -1.08 0.13 0.13
## CourseName.x.x* 1.00 4 3.00 -0.52 -1.09 0.25
## Developmental_Math_Grade* 1.00 4 3.00 -0.15 -1.22 0.25
## Dev_Math_Term* 1.00 11 10.00 -0.02 -1.48 0.86
## Developmental_Math_Pass 0.00 1 1.00 -2.98 7.41 0.07
## DevMathGradeNumber 0.00 4 4.00 -0.50 -0.11 0.29
## CourseName.y.y* 1.00 2 1.00 3.60 11.00 0.02
## College_Math_Grade* 1.00 5 4.00 0.80 -0.47 0.10
## Col_Math_Term* 1.00 1 0.00 NaN NaN 0.00
## College_Math_Pass 0.00 1 1.00 -1.73 1.01 0.03
## ColMathGradeNumber 0.00 4 4.00 -0.80 -0.47 0.10
## ALEKS 19.00 81 62.00 -0.14 -1.07 3.20
## ACT_Math 15.00 30 15.00 0.19 -0.75 0.29
## ACT_Reading 13.00 33 20.00 0.39 -0.02 0.37
## ACT_Engl 10.00 35 25.00 0.52 1.20 0.36
## McCann_R 75.00 120 45.00 -0.70 1.80 2.42
## McCann_W 4.00 6 2.00 -0.26 -1.13 0.19
## AnonId 72.00 9306 9234.00 -0.31 -0.35 152.78
## EnglishStart 0.00 1 1.00 -0.45 -1.81 0.05
## MathStart 0.00 1 1.00 -0.34 -1.89 0.04
## gpahave 0.00 1 1.00 -2.19 2.81 0.02
## ------------------------------------------------------------
## group: 17/FA
## vars n mean sd median trimmed mad
## LCCCGPA 1 191 2.80 0.94 3.00 2.91 0.81
## HSGPA 2 177 3.24 0.44 3.32 3.27 0.42
## HighSchoolEndYear 3 191 2014.78 5.07 2017.00 2015.94 1.48
## HSDate* 4 191 13.46 3.16 15.00 14.10 1.48
## StartTerm* 5 191 13.91 3.05 15.00 14.35 0.00
## StartDate* 6 191 13.85 3.99 16.00 14.67 0.00
## HSGradYears* 7 191 13.43 6.25 11.00 12.45 0.00
## CourseName.x* 8 5 1.00 0.00 1.00 1.00 0.00
## Developmental_English_Grade* 9 5 1.00 0.00 1.00 1.00 0.00
## Location* 10 5 1.00 0.00 1.00 1.00 0.00
## Dev_Eng_Term* 11 5 2.60 1.14 3.00 2.60 1.48
## Developmental_English_Pass 12 5 1.00 0.00 1.00 1.00 0.00
## DevEnglGradeNumber 13 5 1.00 0.00 1.00 1.00 0.00
## CourseName.y* 14 109 1.00 0.00 1.00 1.00 0.00
## College_English_Grade* 15 109 2.03 1.11 2.00 1.85 1.48
## Col_Eng_Term* 16 109 5.92 1.42 6.00 6.02 0.00
## College_English_Pass 17 109 0.91 0.29 1.00 1.00 0.00
## ColEnglGradeNumber 18 109 2.97 1.11 3.00 3.15 1.48
## CourseName.x.x* 19 5 2.00 1.00 2.00 2.00 1.48
## Developmental_Math_Grade* 20 5 1.80 0.84 2.00 1.80 1.48
## Dev_Math_Term* 21 5 2.40 1.14 2.00 2.40 1.48
## Developmental_Math_Pass 22 5 0.80 0.45 1.00 0.80 0.00
## DevMathGradeNumber 23 5 3.00 1.22 3.00 3.00 1.48
## CourseName.y.y* 24 191 1.00 0.00 1.00 1.00 0.00
## College_Math_Grade* 25 191 2.31 1.30 2.00 2.14 1.48
## Col_Math_Term* 26 191 1.00 0.00 1.00 1.00 0.00
## College_Math_Pass 27 191 0.84 0.37 1.00 0.92 0.00
## ColMathGradeNumber 28 191 2.69 1.30 3.00 2.86 1.48
## ALEKS 29 28 52.82 14.98 50.00 51.71 11.12
## ACT_Math 30 135 21.17 3.16 21.00 21.11 4.45
## ACT_Reading 31 135 22.34 4.68 22.00 22.19 4.45
## ACT_Engl 32 134 20.75 3.61 21.00 20.98 2.97
## McCann_R 33 22 100.64 10.38 99.50 100.72 11.86
## McCann_W 34 21 4.57 0.75 5.00 4.53 1.48
## AnonId 35 191 6255.17 2123.55 6787.00 6452.42 2127.53
## EnglishStart 36 109 0.64 0.48 1.00 0.67 0.00
## MathStart 37 191 0.60 0.49 1.00 0.62 0.00
## gpahave 38 191 0.93 0.26 1.00 1.00 0.00
## min max range skew kurtosis se
## LCCCGPA 0.0 4.00 4.00 -1.03 0.51 0.07
## HSGPA 1.7 4.02 2.32 -0.68 0.59 0.03
## HighSchoolEndYear 1973.0 2018.00 45.00 -4.35 26.13 0.37
## HSDate* 1.0 20.00 19.00 -2.09 4.52 0.23
## StartTerm* 1.0 22.00 21.00 -1.64 3.93 0.22
## StartDate* 1.0 19.00 18.00 -1.63 1.16 0.29
## HSGradYears* 1.0 35.00 34.00 1.53 2.38 0.45
## CourseName.x* 1.0 1.00 0.00 NaN NaN 0.00
## Developmental_English_Grade* 1.0 1.00 0.00 NaN NaN 0.00
## Location* 1.0 1.00 0.00 NaN NaN 0.00
## Dev_Eng_Term* 1.0 4.00 3.00 -0.19 -1.75 0.51
## Developmental_English_Pass 1.0 1.00 0.00 NaN NaN 0.00
## DevEnglGradeNumber 1.0 1.00 0.00 NaN NaN 0.00
## CourseName.y* 1.0 1.00 0.00 NaN NaN 0.00
## College_English_Grade* 1.0 5.00 4.00 1.08 0.60 0.11
## Col_Eng_Term* 1.0 9.00 8.00 -0.91 1.82 0.14
## College_English_Pass 0.0 1.00 1.00 -2.79 5.84 0.03
## ColEnglGradeNumber 0.0 4.00 4.00 -1.08 0.60 0.11
## CourseName.x.x* 1.0 3.00 2.00 0.00 -2.20 0.45
## Developmental_Math_Grade* 1.0 3.00 2.00 0.25 -1.82 0.37
## Dev_Math_Term* 1.0 4.00 3.00 0.19 -1.75 0.51
## Developmental_Math_Pass 0.0 1.00 1.00 -1.07 -0.92 0.20
## DevMathGradeNumber 1.0 4.00 3.00 -0.65 -1.40 0.55
## CourseName.y.y* 1.0 1.00 0.00 NaN NaN 0.00
## College_Math_Grade* 1.0 5.00 4.00 0.85 -0.30 0.09
## Col_Math_Term* 1.0 1.00 0.00 NaN NaN 0.00
## College_Math_Pass 0.0 1.00 1.00 -1.82 1.31 0.03
## ColMathGradeNumber 0.0 4.00 4.00 -0.85 -0.30 0.09
## ALEKS 28.0 94.00 66.00 0.92 0.55 2.83
## ACT_Math 15.0 29.00 14.00 0.09 -0.87 0.27
## ACT_Reading 12.0 36.00 24.00 0.27 -0.01 0.40
## ACT_Engl 9.0 28.00 19.00 -0.62 0.30 0.31
## McCann_R 82.0 118.00 36.00 -0.06 -1.15 2.21
## McCann_W 3.0 6.00 3.00 0.11 -0.59 0.16
## AnonId 250.0 9336.00 9086.00 -0.76 -0.23 153.65
## EnglishStart 0.0 1.00 1.00 -0.59 -1.67 0.05
## MathStart 0.0 1.00 1.00 -0.39 -1.86 0.04
## gpahave 0.0 1.00 1.00 -3.25 8.60 0.02
## ------------------------------------------------------------
## group: 18/FA
## vars n mean sd median trimmed mad
## LCCCGPA 1 148 2.78 0.91 2.98 2.88 0.79
## HSGPA 2 140 3.26 0.43 3.29 3.28 0.39
## HighSchoolEndYear 3 148 2016.42 3.69 2018.00 2017.29 0.00
## HSDate* 4 148 11.87 2.45 13.00 12.37 0.00
## StartTerm* 5 148 11.34 2.35 12.00 11.72 0.00
## StartDate* 6 148 12.25 3.45 14.00 12.98 0.00
## HSGradYears* 7 148 9.58 4.69 8.00 8.91 0.00
## CourseName.x* 8 3 1.67 0.58 2.00 1.67 0.00
## Developmental_English_Grade* 9 3 1.00 0.00 1.00 1.00 0.00
## Location* 10 3 1.00 0.00 1.00 1.00 0.00
## Dev_Eng_Term* 11 3 1.00 0.00 1.00 1.00 0.00
## Developmental_English_Pass 12 3 1.00 0.00 1.00 1.00 0.00
## DevEnglGradeNumber 13 3 1.00 0.00 1.00 1.00 0.00
## CourseName.y* 14 82 1.00 0.00 1.00 1.00 0.00
## College_English_Grade* 15 82 2.18 1.36 2.00 1.98 1.48
## Col_Eng_Term* 16 82 6.33 1.32 6.00 6.42 0.00
## College_English_Pass 17 82 0.84 0.37 1.00 0.92 0.00
## ColEnglGradeNumber 18 82 2.82 1.36 3.00 3.02 1.48
## CourseName.x.x* 19 2 1.50 0.71 1.50 1.50 0.74
## Developmental_Math_Grade* 20 2 1.50 0.71 1.50 1.50 0.74
## Dev_Math_Term* 21 2 1.00 0.00 1.00 1.00 0.00
## Developmental_Math_Pass 22 2 0.50 0.71 0.50 0.50 0.74
## DevMathGradeNumber 23 2 1.50 2.12 1.50 1.50 2.22
## CourseName.y.y* 24 148 1.00 0.00 1.00 1.00 0.00
## College_Math_Grade* 25 148 2.36 1.28 2.00 2.21 1.48
## Col_Math_Term* 26 148 1.00 0.00 1.00 1.00 0.00
## College_Math_Pass 27 148 0.81 0.39 1.00 0.88 0.00
## ColMathGradeNumber 28 148 2.64 1.28 3.00 2.79 1.48
## ALEKS 29 13 49.00 11.37 47.00 49.27 13.34
## ACT_Math 30 95 20.64 3.83 21.00 20.55 5.93
## ACT_Reading 31 94 21.98 5.08 22.00 21.71 4.45
## ACT_Engl 32 95 20.67 4.99 21.00 20.73 2.97
## McCann_R 33 18 109.00 16.40 108.00 108.56 10.38
## McCann_W 34 17 5.00 0.87 5.00 5.07 1.48
## AnonId 35 148 6692.77 2178.67 7355.00 7041.37 1337.31
## EnglishStart 36 82 0.60 0.49 1.00 0.62 0.00
## MathStart 37 148 0.68 0.47 1.00 0.72 0.00
## gpahave 38 148 0.95 0.23 1.00 1.00 0.00
## min max range skew kurtosis se
## LCCCGPA 0.00 4 4.00 -1.03 0.71 0.08
## HSGPA 1.66 4 2.34 -0.87 1.49 0.04
## HighSchoolEndYear 1996.00 2020 24.00 -3.35 12.34 0.30
## HSDate* 1.00 17 16.00 -2.16 5.24 0.20
## StartTerm* 1.00 17 16.00 -2.00 5.12 0.19
## StartDate* 1.00 16 15.00 -1.77 1.66 0.28
## HSGradYears* 1.00 25 24.00 1.55 2.26 0.39
## CourseName.x* 1.00 2 1.00 -0.38 -2.33 0.33
## Developmental_English_Grade* 1.00 1 0.00 NaN NaN 0.00
## Location* 1.00 1 0.00 NaN NaN 0.00
## Dev_Eng_Term* 1.00 1 0.00 NaN NaN 0.00
## Developmental_English_Pass 1.00 1 0.00 NaN NaN 0.00
## DevEnglGradeNumber 1.00 1 0.00 NaN NaN 0.00
## CourseName.y* 1.00 1 0.00 NaN NaN 0.00
## College_English_Grade* 1.00 5 4.00 1.00 -0.21 0.15
## Col_Eng_Term* 1.00 9 8.00 -0.96 3.08 0.15
## College_English_Pass 0.00 1 1.00 -1.84 1.39 0.04
## ColEnglGradeNumber 0.00 4 4.00 -1.00 -0.21 0.15
## CourseName.x.x* 1.00 2 1.00 0.00 -2.75 0.50
## Developmental_Math_Grade* 1.00 2 1.00 0.00 -2.75 0.50
## Dev_Math_Term* 1.00 1 0.00 NaN NaN 0.00
## Developmental_Math_Pass 0.00 1 1.00 0.00 -2.75 0.50
## DevMathGradeNumber 0.00 3 3.00 0.00 -2.75 1.50
## CourseName.y.y* 1.00 1 0.00 NaN NaN 0.00
## College_Math_Grade* 1.00 5 4.00 0.66 -0.62 0.11
## Col_Math_Term* 1.00 1 0.00 NaN NaN 0.00
## College_Math_Pass 0.00 1 1.00 -1.57 0.47 0.03
## ColMathGradeNumber 0.00 4 4.00 -0.66 -0.62 0.11
## ALEKS 27.00 68 41.00 -0.25 -0.89 3.15
## ACT_Math 14.00 28 14.00 0.11 -1.27 0.39
## ACT_Reading 12.00 35 23.00 0.44 -0.02 0.52
## ACT_Engl 2.00 32 30.00 -0.37 1.53 0.51
## McCann_R 75.00 150 75.00 0.46 0.56 3.86
## McCann_W 3.00 6 3.00 -0.54 -0.49 0.21
## AnonId 143.00 9362 9219.00 -1.45 1.52 179.09
## EnglishStart 0.00 1 1.00 -0.39 -1.87 0.05
## MathStart 0.00 1 1.00 -0.78 -1.41 0.04
## gpahave 0.00 1 1.00 -3.90 13.33 0.02
## ------------------------------------------------------------
## group: 19/FA
## vars n mean sd median trimmed mad
## LCCCGPA 1 161 2.84 0.93 3.06 2.96 0.86
## HSGPA 2 157 3.30 0.42 3.33 3.32 0.35
## HighSchoolEndYear 3 161 2016.48 5.68 2019.00 2017.95 0.00
## HSDate* 4 161 17.43 3.87 19.00 18.26 0.00
## StartTerm* 5 161 16.86 3.40 18.00 17.62 0.00
## StartDate* 6 161 15.28 4.03 17.00 16.31 0.00
## HSGradYears* 7 161 7.00 4.94 5.00 5.83 0.00
## CourseName.x* 8 0 NaN NA NA NaN NA
## Developmental_English_Grade* 9 0 NaN NA NA NaN NA
## Location* 10 0 NaN NA NA NaN NA
## Dev_Eng_Term* 11 0 NaN NA NA NaN NA
## Developmental_English_Pass 12 0 NaN NA NA NaN NA
## DevEnglGradeNumber 13 0 NaN NA NA NaN NA
## CourseName.y* 14 111 1.00 0.00 1.00 1.00 0.00
## College_English_Grade* 15 111 1.99 1.07 2.00 1.83 1.48
## Col_Eng_Term* 16 111 6.26 1.47 6.00 6.24 0.00
## College_English_Pass 17 111 0.93 0.26 1.00 1.00 0.00
## ColEnglGradeNumber 18 111 3.01 1.07 3.00 3.17 1.48
## CourseName.x.x* 19 5 1.00 0.00 1.00 1.00 0.00
## Developmental_Math_Grade* 20 5 3.00 1.58 3.00 3.00 1.48
## Dev_Math_Term* 21 5 3.00 1.58 3.00 3.00 1.48
## Developmental_Math_Pass 22 5 0.60 0.55 1.00 0.60 0.00
## DevMathGradeNumber 23 5 2.00 1.58 2.00 2.00 1.48
## CourseName.y.y* 24 161 1.00 0.00 1.00 1.00 0.00
## College_Math_Grade* 25 161 2.55 1.36 2.00 2.43 1.48
## Col_Math_Term* 26 161 1.00 0.00 1.00 1.00 0.00
## College_Math_Pass 27 161 0.77 0.42 1.00 0.84 0.00
## ColMathGradeNumber 28 161 2.45 1.36 3.00 2.57 1.48
## ALEKS 29 16 37.31 15.45 34.00 37.21 15.57
## ACT_Math 30 101 20.39 3.84 20.00 20.21 4.45
## ACT_Reading 31 101 22.41 4.95 22.00 21.99 4.45
## ACT_Engl 32 101 20.46 4.14 21.00 20.35 2.97
## McCann_R 33 15 103.33 14.79 103.00 103.69 11.86
## McCann_W 34 12 5.08 0.79 5.00 5.10 1.48
## AnonId 35 161 6766.45 2200.91 7374.00 7139.67 1595.28
## EnglishStart 36 111 0.68 0.47 1.00 0.72 0.00
## MathStart 37 161 0.73 0.44 1.00 0.79 0.00
## gpahave 38 161 0.98 0.16 1.00 1.00 0.00
## min max range skew kurtosis se
## LCCCGPA 0.00 4.00 4.00 -1.08 0.80 0.07
## HSGPA 1.64 4.01 2.37 -0.92 2.07 0.03
## HighSchoolEndYear 1983.00 2021.00 38.00 -3.07 10.52 0.45
## HSDate* 1.00 25.00 24.00 -2.24 5.40 0.30
## StartTerm* 1.00 23.00 22.00 -2.75 8.11 0.27
## StartDate* 1.00 20.00 19.00 -2.09 2.99 0.32
## HSGradYears* 1.00 26.00 25.00 2.17 3.88 0.39
## CourseName.x* Inf -Inf -Inf NA NA NA
## Developmental_English_Grade* Inf -Inf -Inf NA NA NA
## Location* Inf -Inf -Inf NA NA NA
## Dev_Eng_Term* Inf -Inf -Inf NA NA NA
## Developmental_English_Pass Inf -Inf -Inf NA NA NA
## DevEnglGradeNumber Inf -Inf -Inf NA NA NA
## CourseName.y* 1.00 1.00 0.00 NaN NaN 0.00
## College_English_Grade* 1.00 5.00 4.00 1.18 1.09 0.10
## Col_Eng_Term* 1.00 10.00 9.00 0.16 2.00 0.14
## College_English_Pass 0.00 1.00 1.00 -3.26 8.74 0.02
## ColEnglGradeNumber 0.00 4.00 4.00 -1.18 1.09 0.10
## CourseName.x.x* 1.00 1.00 0.00 NaN NaN 0.00
## Developmental_Math_Grade* 1.00 5.00 4.00 0.00 -1.91 0.71
## Dev_Math_Term* 1.00 5.00 4.00 0.00 -1.91 0.71
## Developmental_Math_Pass 0.00 1.00 1.00 -0.29 -2.25 0.24
## DevMathGradeNumber 0.00 4.00 4.00 0.00 -1.91 0.71
## CourseName.y.y* 1.00 1.00 0.00 NaN NaN 0.00
## College_Math_Grade* 1.00 5.00 4.00 0.60 -0.84 0.11
## Col_Math_Term* 1.00 1.00 0.00 NaN NaN 0.00
## College_Math_Pass 0.00 1.00 1.00 -1.27 -0.38 0.03
## ColMathGradeNumber 0.00 4.00 4.00 -0.60 -0.84 0.11
## ALEKS 9.00 67.00 58.00 0.13 -0.86 3.86
## ACT_Math 13.00 29.00 16.00 0.28 -1.02 0.38
## ACT_Reading 13.00 35.00 22.00 0.71 0.06 0.49
## ACT_Engl 9.00 34.00 25.00 0.41 1.29 0.41
## McCann_R 72.00 130.00 58.00 -0.12 -0.31 3.82
## McCann_W 4.00 6.00 2.00 -0.12 -1.53 0.23
## AnonId 75.00 9392.00 9317.00 -1.48 1.71 173.46
## EnglishStart 0.00 1.00 1.00 -0.74 -1.46 0.04
## MathStart 0.00 1.00 1.00 -1.04 -0.92 0.03
## gpahave 0.00 1.00 1.00 -6.05 34.80 0.01
## ------------------------------------------------------------
## group: 20/FA
## vars n mean sd median trimmed mad
## LCCCGPA 1 274 2.61 1.05 2.75 2.71 1.05
## HSGPA 2 254 3.01 0.61 3.02 3.02 0.71
## HighSchoolEndYear 3 274 2015.97 6.75 2019.00 2017.48 1.48
## HSDate* 4 274 17.41 4.98 19.00 18.06 1.48
## StartTerm* 5 274 24.13 5.88 27.00 25.35 0.00
## StartDate* 6 274 23.34 6.62 27.00 24.74 0.00
## HSGradYears* 7 274 13.99 12.72 7.00 11.28 0.00
## CourseName.x* 8 40 1.75 0.44 2.00 1.81 0.00
## Developmental_English_Grade* 9 40 1.15 0.36 1.00 1.06 0.00
## Location* 10 40 1.93 0.27 2.00 2.00 0.00
## Dev_Eng_Term* 11 40 4.62 1.25 5.00 4.69 1.48
## Developmental_English_Pass 12 40 0.85 0.36 1.00 0.94 0.00
## DevEnglGradeNumber 13 40 0.85 0.36 1.00 0.94 0.00
## CourseName.y* 14 176 1.00 0.00 1.00 1.00 0.00
## College_English_Grade* 15 176 2.22 1.33 2.00 2.04 1.48
## Col_Eng_Term* 16 176 10.59 2.38 11.00 10.85 1.48
## College_English_Pass 17 176 0.84 0.37 1.00 0.92 0.00
## ColEnglGradeNumber 18 176 2.78 1.33 3.00 2.96 1.48
## CourseName.x.x* 19 49 2.12 0.39 2.00 2.07 0.00
## Developmental_Math_Grade* 20 49 2.61 1.37 2.00 2.49 1.48
## Dev_Math_Term* 21 49 7.61 2.07 9.00 7.95 0.00
## Developmental_Math_Pass 22 49 0.80 0.41 1.00 0.85 0.00
## DevMathGradeNumber 23 49 2.43 1.27 3.00 2.51 1.48
## CourseName.y.y* 24 274 1.00 0.00 1.00 1.00 0.00
## College_Math_Grade* 25 274 2.55 1.47 2.00 2.45 1.48
## Col_Math_Term* 26 274 1.00 0.00 1.00 1.00 0.00
## College_Math_Pass 27 274 0.76 0.43 1.00 0.82 0.00
## ColMathGradeNumber 28 274 2.45 1.47 3.00 2.55 1.48
## ALEKS 29 57 19.44 13.74 15.00 17.51 10.38
## ACT_Math 30 141 18.79 3.74 18.00 18.44 2.97
## ACT_Reading 31 140 20.26 4.65 20.00 20.04 4.45
## ACT_Engl 32 141 18.58 4.59 19.00 18.42 4.45
## McCann_R 33 48 97.85 15.87 99.00 97.17 10.38
## McCann_W 34 25 4.76 0.72 5.00 4.76 0.00
## AnonId 35 274 6778.21 2106.77 7382.50 7103.92 1716.85
## EnglishStart 36 176 0.62 0.49 1.00 0.65 0.00
## MathStart 37 274 0.51 0.50 1.00 0.51 0.00
## gpahave 38 274 0.93 0.26 1.00 1.00 0.00
## min max range skew kurtosis se
## LCCCGPA 0.00 4.0 4.00 -0.74 -0.15 0.06
## HSGPA 1.28 4.2 2.92 -0.24 -0.72 0.04
## HighSchoolEndYear 1985.00 2022.0 37.00 -2.29 5.43 0.41
## HSDate* 1.00 31.0 30.00 -1.06 1.81 0.30
## StartTerm* 1.00 34.0 33.00 -1.96 3.37 0.35
## StartDate* 1.00 31.0 30.00 -1.70 1.52 0.40
## HSGradYears* 1.00 52.0 51.00 1.62 1.30 0.77
## CourseName.x* 1.00 2.0 1.00 -1.11 -0.78 0.07
## Developmental_English_Grade* 1.00 2.0 1.00 1.89 1.60 0.06
## Location* 1.00 2.0 1.00 -3.11 7.85 0.04
## Dev_Eng_Term* 1.00 7.0 6.00 -0.50 0.90 0.20
## Developmental_English_Pass 0.00 1.0 1.00 -1.89 1.60 0.06
## DevEnglGradeNumber 0.00 1.0 1.00 -1.89 1.60 0.06
## CourseName.y* 1.00 1.0 0.00 NaN NaN 0.00
## College_English_Grade* 1.00 5.0 4.00 0.98 -0.19 0.10
## Col_Eng_Term* 1.00 14.0 13.00 -1.65 4.00 0.18
## College_English_Pass 0.00 1.0 1.00 -1.79 1.22 0.03
## ColEnglGradeNumber 0.00 4.0 4.00 -0.98 -0.19 0.10
## CourseName.x.x* 1.00 3.0 2.00 1.12 2.12 0.06
## Developmental_Math_Grade* 1.00 6.0 5.00 0.90 -0.07 0.20
## Dev_Math_Term* 1.00 10.0 9.00 -1.41 1.30 0.30
## Developmental_Math_Pass 0.00 1.0 1.00 -1.42 0.03 0.06
## DevMathGradeNumber 0.00 4.0 4.00 -0.70 -0.58 0.18
## CourseName.y.y* 1.00 1.0 0.00 NaN NaN 0.00
## College_Math_Grade* 1.00 5.0 4.00 0.54 -1.07 0.09
## Col_Math_Term* 1.00 1.0 0.00 NaN NaN 0.00
## College_Math_Pass 0.00 1.0 1.00 -1.21 -0.55 0.03
## ColMathGradeNumber 0.00 4.0 4.00 -0.54 -1.07 0.09
## ALEKS 4.00 71.0 67.00 1.57 2.75 1.82
## ACT_Math 11.00 32.0 21.00 0.91 0.88 0.31
## ACT_Reading 8.00 35.0 27.00 0.45 0.26 0.39
## ACT_Engl 7.00 35.0 28.00 0.46 0.54 0.39
## McCann_R 66.00 150.0 84.00 0.57 1.27 2.29
## McCann_W 3.00 6.0 3.00 -0.28 -0.21 0.14
## AnonId 51.00 9408.0 9357.00 -1.34 1.39 127.27
## EnglishStart 0.00 1.0 1.00 -0.49 -1.77 0.04
## MathStart 0.00 1.0 1.00 -0.04 -2.01 0.03
## gpahave 0.00 1.0 1.00 -3.27 8.69 0.02
model1<- lm(LCCCGPA ~ HSGPA + ACT_Math + ACT_Engl + ACT_Reading + ALEKS + McCann_R + McCann_W, data = Placement)
summary(model1)
##
## Call:
## lm(formula = LCCCGPA ~ HSGPA + ACT_Math + ACT_Engl + ACT_Reading +
## ALEKS + McCann_R + McCann_W, data = Placement)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7853 -0.5343 0.1364 0.6315 1.8617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.226514 0.499232 -2.457 0.0145 *
## HSGPA 0.822516 0.124669 6.598 1.65e-10 ***
## ACT_Math 0.012848 0.025481 0.504 0.6145
## ACT_Engl 0.027336 0.017365 1.574 0.1164
## ACT_Reading -0.021486 0.017909 -1.200 0.2311
## ALEKS -0.005442 0.004591 -1.185 0.2368
## McCann_R 0.013740 0.004432 3.100 0.0021 **
## McCann_W -0.040519 0.059012 -0.687 0.4928
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9132 on 332 degrees of freedom
## (9082 observations deleted due to missingness)
## Multiple R-squared: 0.1993, Adjusted R-squared: 0.1824
## F-statistic: 11.81 on 7 and 332 DF, p-value: 1.952e-13
VIF(model1)
## HSGPA ACT_Math ACT_Engl ACT_Reading ALEKS McCann_R
## 1.275524 1.645069 1.936999 1.772942 1.361161 1.443325
## McCann_W
## 1.231992
mediationmodel <- with(Placement,
"
LCCCGPA ~ a*HSGPA
ACT_Math ~ b*LCCCGPA
ACT_Math ~ c*HSGPA
Mediation := a * b
Total := c + a*b
")
mediationmodel <- sem(mediationmodel, data = Placement)
summary(mediationmodel, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE)
## lavaan 0.6-12 ended normally after 1 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 5
##
## Used Total
## Number of observations 2917 9422
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 1237.383
## Degrees of freedom 3
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -11646.695
## Loglikelihood unrestricted model (H1) -11646.695
##
## Akaike (AIC) 23303.391
## Bayesian (BIC) 23333.282
## Sample-size adjusted Bayesian (BIC) 23317.395
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LCCCGPA ~
## HSGPA (a) 0.870 0.031 27.687 0.000 0.870 0.456
## ACT_Math ~
## LCCCGPA (b) 0.138 0.067 2.049 0.040 0.138 0.039
## HSGPA (c) 2.697 0.128 21.031 0.000 2.697 0.398
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .LCCCGPA 0.874 0.023 38.190 0.000 0.874 0.792
## .ACT_Math 11.518 0.302 38.190 0.000 11.518 0.826
##
## R-Square:
## Estimate
## LCCCGPA 0.208
## ACT_Math 0.174
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Mediation 0.120 0.059 2.043 0.041 0.120 0.018
## Total 2.817 0.114 24.666 0.000 2.817 0.415
stdsolution<-standardizedsolution(mediationmodel, type = "std.all")
stdsolution
## lhs op rhs label est.std se z pvalue ci.lower ci.upper
## 1 LCCCGPA ~ HSGPA a 0.456 0.014 32.870 0.000 0.429 0.483
## 2 ACT_Math ~ LCCCGPA b 0.039 0.019 2.050 0.040 0.002 0.076
## 3 ACT_Math ~ HSGPA c 0.398 0.017 23.182 0.000 0.364 0.431
## 4 LCCCGPA ~~ LCCCGPA 0.792 0.013 62.540 0.000 0.767 0.817
## 5 ACT_Math ~~ ACT_Math 0.826 0.012 67.751 0.000 0.802 0.850
## 6 HSGPA ~~ HSGPA 1.000 0.000 NA NA 1.000 1.000
## 7 Mediation := a*b Mediation 0.018 0.009 2.045 0.041 0.001 0.035
## 8 Total := c+a*b Total 0.415 0.015 28.368 0.000 0.387 0.444
write.csv(stdsolution, "150Grad.csv")
semPaths(mediationmodel,
whatLabels = "std.all", structural = FALSE,
edge.label.color = "black", label.prop=0.9, edge.label.cex = 1.5,
equalizeManifests = FALSE, optimizeLatRes = FALSE, node.width = 1.5,
edge.width = 0.5, shapeMan = "rectangle", shapeLat = "ellipse",
shapeInt = "square", sizeMan = 6, sizeInt = 3, sizeLat = 4,
unCol = "#070b8c", title = TRUE, intercepts = FALSE, residuals = FALSE)
EnglDis<- Placement %>%
subset(College_English_Grade %in% c("A", "B", "C", "D", "F")) %>%
dplyr::select(College_English_Grade, ACT_Engl, HSGPA)
EnglDis<- na.omit(EnglDis)
MathDis<- Placement %>%
subset(College_Math_Grade %in% c("A", "B", "C", "D", "F")) %>%
dplyr::select(College_Math_Grade, ACT_Math, HSGPA)
MathDis<- na.omit(MathDis)
library(tidyverse)
library(caret)
library(MASS)
theme_set(theme_classic())
set.seed(123)
training.samples <- EnglDis$College_English_Grade %>%
createDataPartition(p = .08, list = FALSE)
train.data<- EnglDis[training.samples, ]
test.data<- EnglDis[-training.samples, ]
#Estimate preprocessing parameters
preproc.param <- train.data %>%
preProcess(method = c("center", "scale"))
# Transform the data using the estimated parameters
train.transformed <- preproc.param %>% predict(train.data)
test.transformed <- preproc.param %>% predict(test.data)
# Fit the model
model <- lda(College_English_Grade ~., data = train.transformed)
# Make predictions
predictions <- model %>% predict(test.transformed)
# Model accuracy
mean(predictions$class==test.transformed$College_English_Grade)
## [1] 0.4017192
model2 <- lda(College_English_Grade~., data = train.transformed)
model2
## Call:
## lda(College_English_Grade ~ ., data = train.transformed)
##
## Prior probabilities of groups:
## A B C D F
## 0.31168831 0.33116883 0.20129870 0.03246753 0.12337662
##
## Group means:
## ACT_Engl HSGPA
## A 0.23208565 0.3688045
## B -0.08910980 -0.0125984
## C 0.07726752 -0.1284682
## D 0.04366259 -0.4278793
## F -0.48469040 -0.5756941
##
## Coefficients of linear discriminants:
## LD1 LD2
## ACT_Engl -0.365226 1.0043104
## HSGPA -0.854303 -0.6830999
##
## Proportion of trace:
## LD1 LD2
## 0.8985 0.1015
plot(model2)
predictions <- model %>% predict(test.transformed)
names(predictions)
## [1] "class" "posterior" "x"
# Predicted classes
head(predictions$class, 6)
## [1] B B B B A B
## Levels: A B C D F
# Predicted probabilities of class memebership.
head(predictions$posterior, 6)
## A B C D F
## 28 0.2644964 0.3409906 0.2309615 0.03964563 0.12390596
## 58 0.1599813 0.3055811 0.2617766 0.06731772 0.20534328
## 72 0.3172949 0.3537712 0.2014083 0.02770938 0.09981627
## 74 0.2700130 0.3144775 0.2602087 0.04993657 0.10536422
## 87 0.3803339 0.3655753 0.1600655 0.01637007 0.07765525
## 94 0.2400080 0.3707129 0.1975472 0.03061668 0.16111532
# Linear discriminants
head(predictions$x, 3)
## LD1 LD2
## 28 0.3268585 0.4407275
## 58 1.3258382 1.2395107
## 72 -0.0905183 -0.1999316
lda.data <- cbind(train.transformed, predict(model2)$x)
ggplot(lda.data, aes(LD1, LD2)) +
geom_point(aes(color = College_English_Grade))
mean(predictions$class==test.transformed$College_English_Grade)
## [1] 0.4017192
library(tidyverse)
library(caret)
library(MASS)
theme_set(theme_classic())
set.seed(123)
training.samples <- MathDis$College_Math_Grade %>%
createDataPartition(p = .08, list = FALSE)
train.data<- MathDis[training.samples, ]
test.data<- MathDis[-training.samples, ]
#Estimate preprocessing parameters
preproc.param <- train.data %>%
preProcess(method = c("center", "scale"))
# Transform the data using the estimated parameters
train.transformed <- preproc.param %>% predict(train.data)
test.transformed <- preproc.param %>% predict(test.data)
# Fit the model
model <- lda(College_Math_Grade ~., data = train.transformed)
# Make predictions
predictions <- model %>% predict(test.transformed)
# Model accuracy
mean(predictions$class==test.transformed$College_Math_Grade)
## [1] 0.3453193
model2 <- lda(College_Math_Grade~., data = train.transformed)
model2
## Call:
## lda(College_Math_Grade ~ ., data = train.transformed)
##
## Prior probabilities of groups:
## A B C D F
## 0.30555556 0.27083333 0.21527778 0.05555556 0.15277778
##
## Group means:
## ACT_Math HSGPA
## A 0.49733961 0.4875320
## B -0.12881802 0.1339826
## C -0.05746451 -0.2939056
## D -0.72560896 -0.2651124
## F -0.42148948 -0.7020345
##
## Coefficients of linear discriminants:
## LD1 LD2
## ACT_Math -0.5940894 0.9031001
## HSGPA -0.7942199 -0.7695481
##
## Proportion of trace:
## LD1 LD2
## 0.885 0.115
plot(model2)
predictions <- model %>% predict(test.transformed)
names(predictions)
## [1] "class" "posterior" "x"
# Predicted classes
head(predictions$class, 6)
## [1] C F B A B B
## Levels: A B C D F
# Predicted probabilities of class memebership.
head(predictions$posterior, 6)
## A B C D F
## 28 0.1589255 0.2409906 0.3022905 0.05442938 0.24336397
## 73 0.1003498 0.2595153 0.2674542 0.09575122 0.27692943
## 87 0.2428463 0.3644484 0.2042224 0.07322699 0.11525592
## 88 0.5809703 0.2617939 0.1142613 0.01587443 0.02710012
## 95 0.2340268 0.2936699 0.2599024 0.05272202 0.15967885
## 116 0.2365119 0.2950751 0.2583754 0.05260370 0.15743390
# Linear discriminants
head(predictions$x, 3)
## LD1 LD2
## 28 0.78381501 0.6337491
## 73 1.22682933 -0.2000195
## 87 0.08523922 -0.8851400
lda.data <- cbind(train.transformed, predict(model2)$x)
ggplot(lda.data, aes(LD1, LD2)) +
geom_point(aes(color = College_Math_Grade))
mean(predictions$class==test.transformed$College_Math_Grade)
## [1] 0.3453193
corrtable1<- Placement %>%
dplyr:: select(LCCCGPA, HSGPA, ACT_Reading, ACT_Engl, ACT_Math, HSGradYears)
corrtable1$HSGradYears<- as.numeric(corrtable1$HSGradYears)
apa.cor.table(corrtable1)
##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3 4
## 1. LCCCGPA 2.68 0.98
##
## 2. HSGPA 2.97 0.57 .33**
## [.31, .35]
##
## 3. ACT_Reading 20.49 4.96 .23** .39**
## [.19, .26] [.36, .42]
##
## 4. ACT_Engl 19.09 4.71 .26** .44** .75**
## [.23, .30] [.41, .47] [.73, .76]
##
## 5. ACT_Math 19.16 3.79 .22** .42** .53** .63**
## [.19, .26] [.38, .45] [.51, .56] [.61, .65]
##
## 6. HSGradYears 3.06 6.46 .12** -.21** .00 -.01
## [.10, .14] [-.23, -.19] [-.03, .04] [-.04, .03]
##
## 5
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## -.03
## [-.06, .01]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
##
gpamodel<- lm(LCCCGPA ~ HSGPA + ACT_Reading + ACT_Engl + ACT_Math + HSGradYears, data = corrtable1)
summary(gpamodel)
##
## Call:
## lm(formula = LCCCGPA ~ HSGPA + ACT_Reading + ACT_Engl + ACT_Math +
## HSGradYears, data = corrtable1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3582 -0.4833 0.2111 0.6411 2.7973
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3216354 0.1136789 -2.829 0.004697 **
## HSGPA 0.8197027 0.0361187 22.695 < 2e-16 ***
## ACT_Reading 0.0009008 0.0053312 0.169 0.865829
## ACT_Engl 0.0124825 0.0061368 2.034 0.042037 *
## ACT_Math 0.0047272 0.0061797 0.765 0.444356
## HSGradYears 0.0283269 0.0082254 3.444 0.000582 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9303 on 2875 degrees of freedom
## (6541 observations deleted due to missingness)
## Multiple R-squared: 0.2174, Adjusted R-squared: 0.216
## F-statistic: 159.7 on 5 and 2875 DF, p-value: < 2.2e-16
VIF(gpamodel)
## HSGPA ACT_Reading ACT_Engl ACT_Math HSGradYears
## 1.319281 2.288639 2.731945 1.729197 1.010516
placementyears<- Placement %>%
subset(HSGradYears>0) %>%
dplyr::select(LCCCGPA, HSGPA, HSGradYears)%>%
na.omit(placementyears)
placementyears$HSGradYears<- as.numeric(placementyears$HSGradYears)
placementyears<-
mutate(placementyears,HSGPACat=case_when(HSGPA >= 0 & HSGPA < 1 ~ 'F', HSGPA >= 1 & HSGPA < 2 ~ 'D', HSGPA >=2 & HSGPA < 3 ~ 'C', HSGPA >= 3 & HSGPA < 4 ~ 'B', HSGPA >= 4 ~ 'A'))
placementyears<-
mutate(placementyears,HSDegreeCat=case_when(HSGradYears < 5 ~ '0 to 4.9', HSGradYears >=10 ~ '10 or More',HSGradYears >= 5 & HSGradYears <10 ~'5 to 9.9'))
placementyears$new = factor(placementyears$HSDegreeCat, levels=c("0 to 4.9", "5 to 9.9", "10 or More"), labels=c("0 to 4.9 Years", "5 to 9.9 Years", "10 or More Years"))
ggplot(placementyears, aes(x= HSGPACat, y=LCCCGPA))+
geom_boxplot(fill = "dodgerblue4")+
facet_grid(.~new)+
ggtitle("Effects of HS Grades on College GPA by Years since HS Graduation")+
scale_x_discrete(name="Years Since HS, and High School GPA Letter Equivalent")+
scale_y_continuous(name="College Grade Point Average")
ggplot(placementyears, aes(HSGradYears,LCCCGPA))+
geom_point()+
scale_y_continuous(name = "College GPA")+
scale_x_continuous(name = "High School Degree Age")+
ggtitle("The Correlation Between High School Degree Age and College Grade Point Average")+
stat_smooth(method = "lm", col = "dodgerblue4")
ppym<- Placement %>%
subset(HSGradYears>0) %>%
dplyr::select(College_Math_Pass, HSGPA, ACT_Math, HSGradYears)%>%
na.omit(ppym)
ppym$HSGradYears<- as.numeric(ppym$HSGradYears)
mathgrady<- glm(formula = College_Math_Pass ~ HSGPA + HSGradYears + ACT_Math, family = "binomial", data = ppym)
summary(mathgrady)
##
## Call:
## glm(formula = College_Math_Pass ~ HSGPA + HSGradYears + ACT_Math,
## family = "binomial", data = ppym)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3477 0.4392 0.5755 0.7086 1.3787
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.62764 0.47661 -5.513 3.52e-08 ***
## HSGPA 1.00941 0.13048 7.736 1.02e-14 ***
## HSGradYears 0.09954 0.04196 2.372 0.0177 *
## ACT_Math 0.03450 0.01791 1.927 0.0540 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1630.1 on 1585 degrees of freedom
## Residual deviance: 1549.9 on 1582 degrees of freedom
## AIC: 1557.9
##
## Number of Fisher Scoring iterations: 4
VIF(mathgrady)
## HSGPA HSGradYears ACT_Math
## 1.098365 1.034598 1.063070
OddsRatio(mathgrady)
##
## Call:
## glm(formula = College_Math_Pass ~ HSGPA + HSGradYears + ACT_Math,
## family = "binomial", data = ppym)
##
## Odds Ratios:
## or or.lci or.uci Pr(>|z|)
## (Intercept) 0.072 0.028 0.183 3.52e-08 ***
## HSGPA 2.744 2.127 3.549 1.02e-14 ***
## HSGradYears 1.105 1.025 1.209 0.0177 *
## ACT_Math 1.035 1.000 1.072 0.0540 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Brier Score: 0.158 Nagelkerke R2: 0.077
###This Gives Goodness of Fit
performance::performance_hosmer(mathgrady, n_bins = 10)
## # Hosmer-Lemeshow Goodness-of-Fit Test
##
## Chi-squared: 16.226
## df: 8
## p-value: 0.039
###This gives omnibus test
lmtest::lrtest(mathgrady)
## Likelihood ratio test
##
## Model 1: College_Math_Pass ~ HSGPA + HSGradYears + ACT_Math
## Model 2: College_Math_Pass ~ 1
## #Df LogLik Df Chisq Pr(>Chisq)
## 1 4 -774.97
## 2 1 -815.06 -3 80.167 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Placement<- read.csv("Placement.csv")
epym<- Placement %>%
subset(HSGradYears>0) %>%
dplyr::select(College_English_Pass, HSGPA, ACT_Engl, ACT_Reading, HSGradYears)%>%
na.omit(epym)
epym$HSGradYears<- as.numeric(epym$HSGradYears)
enggrady<- glm(formula = College_English_Pass ~ HSGPA + HSGradYears + ACT_Engl + ACT_Reading, family = "binomial", data = epym)
summary(enggrady)
##
## Call:
## glm(formula = College_English_Pass ~ HSGPA + HSGradYears + ACT_Engl +
## ACT_Reading, family = "binomial", data = epym)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6545 0.3278 0.4647 0.6288 1.4548
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.705084 0.438024 -6.176 6.59e-10 ***
## HSGPA 1.297208 0.144566 8.973 < 2e-16 ***
## HSGradYears 0.104138 0.054372 1.915 0.0555 .
## ACT_Engl 0.022994 0.022648 1.015 0.3100
## ACT_Reading 0.009956 0.021419 0.465 0.6421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1481.6 on 1709 degrees of freedom
## Residual deviance: 1363.4 on 1705 degrees of freedom
## AIC: 1373.4
##
## Number of Fisher Scoring iterations: 5
VIF(enggrady)
## HSGPA HSGradYears ACT_Engl ACT_Reading
## 1.131974 1.022223 2.012831 1.994732
OddsRatio(enggrady)
##
## Call:
## glm(formula = College_English_Pass ~ HSGPA + HSGradYears + ACT_Engl +
## ACT_Reading, family = "binomial", data = epym)
##
## Odds Ratios:
## or or.lci or.uci Pr(>|z|)
## (Intercept) 0.067 0.028 0.157 6.59e-10 ***
## HSGPA 3.659 2.764 4.873 < 2.2e-16 ***
## HSGradYears 1.110 1.006 1.246 0.0555 .
## ACT_Engl 1.023 0.979 1.070 0.3100
## ACT_Reading 1.010 0.969 1.054 0.6421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Brier Score: 0.123 Nagelkerke R2: 0.115
##This gives goodness of fit
performance::performance_hosmer(enggrady, n_bins = 10)
## # Hosmer-Lemeshow Goodness-of-Fit Test
##
## Chi-squared: 8.370
## df: 8
## p-value: 0.398
##This gives omnibus test
lmtest::lrtest(enggrady)
## Likelihood ratio test
##
## Model 1: College_English_Pass ~ HSGPA + HSGradYears + ACT_Engl + ACT_Reading
## Model 2: College_English_Pass ~ 1
## #Df LogLik Df Chisq Pr(>Chisq)
## 1 5 -681.71
## 2 1 -740.80 -4 118.16 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
yearstable<-
table(placementyears$new)
yearstable
##
## 0 to 4.9 Years 5 to 9.9 Years 10 or More Years
## 5546 602 626
prop.table(yearstable)
##
## 0 to 4.9 Years 5 to 9.9 Years 10 or More Years
## 0.81871863 0.08886921 0.09241216
library(lavaan)
library(semPlot)
data<- Placement%>%
subset(HSGradYears>0)%>%
subset(HSGPA>0)
data <- mutate(data, MathGrade = case_when(College_Math_Grade == "A" ~ 5,
College_Math_Grade == "B" ~ 4,
College_Math_Grade == "C"~ 3,
College_Math_Grade == "D" ~ 2,
College_Math_Grade == "F" ~ 1))
data <- mutate(data, EngGrade = case_when(College_English_Grade == "A" ~ 5,
College_English_Grade == "B" ~ 4,
College_English_Grade == "C"~ 3,
College_English_Grade == "D" ~ 2,
College_English_Grade == "F" ~ 1))
data$EngGrade<- as.numeric(data$EngGrade)
data$MathGrade<- as.numeric(data$MathGrade)
data$HSGradYears<-as.numeric(data$HSGradYears)
table(data$Col_Math_Term, data$MathGrade)
##
## 1 2 3 4 5
## 10/FA 10 3 22 32 25
## 11/FA 21 8 22 26 29
## 11/SP 16 7 27 33 27
## 12/FA 12 14 25 26 17
## 12/SP 10 12 23 24 23
## 13/FA 25 11 41 39 34
## 13/SP 23 11 31 45 40
## 14/FA 11 9 49 36 56
## 14/SP 17 23 40 48 40
## 15/FA 27 12 52 77 67
## 15/SP 14 2 30 52 43
## 16/FA 33 11 57 68 79
## 16/SP 25 10 39 54 49
## 17/FA 27 11 46 66 78
## 17/SP 25 8 26 57 51
## 18/FA 18 15 43 60 69
## 18/SP 16 8 42 33 39
## 19/FA 37 12 37 60 77
## 19/SP 18 7 22 41 42
## 20/FA 61 15 74 70 87
## 20/SP 7 2 21 34 40
## 21/FA 54 13 63 76 78
## 21/SP 36 8 46 43 64
table(data$Col_Eng_Term, data$EngGrade)
##
## 1 2 3 4 5
## 10/FA 6 3 32 37 61
## 11/FA 8 5 22 60 61
## 11/SP 5 4 20 31 46
## 12/FA 9 5 26 42 53
## 12/SP 13 3 18 28 42
## 13/FA 10 2 27 72 45
## 13/SP 5 1 20 30 41
## 14/FA 10 3 32 73 88
## 14/SP 10 3 23 25 35
## 15/FA 34 10 48 99 97
## 15/SP 6 3 23 47 45
## 16/FA 31 8 61 87 105
## 16/SP 23 1 30 47 37
## 17/FA 34 8 52 103 96
## 17/SP 24 5 20 33 44
## 18/FA 24 4 52 86 84
## 18/SP 16 4 25 43 42
## 19/FA 21 8 56 109 95
## 19/SP 26 2 21 30 44
## 20/FA 36 6 36 69 72
## 20/SP 11 4 15 35 40
## 21/FA 44 3 31 48 47
## 21/SP 16 8 18 31 25
table(data$MathGrade)
##
## 1 2 3 4 5
## 543 232 878 1100 1154
prop.table(table(data$MathGrade))
##
## 1 2 3 4 5
## 0.1389813 0.0593806 0.2247249 0.2815459 0.2953673
table(data$EngGrade)
##
## 1 2 3 4 5
## 422 103 708 1265 1345
prop.table(table(data$EngGrade))
##
## 1 2 3 4 5
## 0.10981004 0.02680198 0.18423107 0.32916992 0.34998699
mediation.model <- '
# mediator
HSGradYears ~ a*HSGPA
HSGradYears ~ b*LCCCGPA
# direct effect
HSGPA ~ c*LCCCGPA
# indirect effect (a*b)
ab := a*b
# total effect
total := c + (a*b)
'
fit <- sem(mediation.model, data = data)
summary(fit, fit.measures=T, standardized=T, ci=TRUE, rsquare=T)
## lavaan 0.6-12 ended normally after 1 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 5
##
## Number of observations 6774
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 1292.578
## Degrees of freedom 3
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -26317.457
## Loglikelihood unrestricted model (H1) -26317.457
##
## Akaike (AIC) 52644.913
## Bayesian (BIC) 52679.017
## Sample-size adjusted Bayesian (BIC) 52663.128
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## HSGradYears ~
## HSGPA (a) -2.611 0.117 -22.269 0.000 -2.840 -2.381
## LCCCGPA (b) 1.234 0.067 18.461 0.000 1.103 1.365
## HSGPA ~
## LCCCGPA (c) 0.176 0.007 26.683 0.000 0.163 0.189
## Std.lv Std.all
##
## -2.611 -0.272
## 1.234 0.225
##
## 0.176 0.308
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .HSGradYears 27.495 0.472 58.198 0.000 26.569 28.421
## .HSGPA 0.295 0.005 58.198 0.000 0.285 0.305
## Std.lv Std.all
## 27.495 0.913
## 0.295 0.905
##
## R-Square:
## Estimate
## HSGradYears 0.087
## HSGPA 0.095
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## ab -3.222 0.259 -12.451 0.000 -3.729 -2.715
## total -3.046 0.259 -11.767 0.000 -3.553 -2.539
## Std.lv Std.all
## -3.222 -0.061
## -3.046 0.247
medtable<- standardizedsolution(fit, type = "std.all")
medtable
## lhs op rhs label est.std se z pvalue ci.lower
## 1 HSGradYears ~ HSGPA a -0.272 0.012 -23.014 0 -0.295
## 2 HSGradYears ~ LCCCGPA b 0.225 0.012 19.110 0 0.202
## 3 HSGPA ~ LCCCGPA c 0.308 0.011 28.741 0 0.287
## 4 HSGradYears ~~ HSGradYears 0.913 0.007 139.784 0 0.900
## 5 HSGPA ~~ HSGPA 0.905 0.007 136.731 0 0.892
## 6 LCCCGPA ~~ LCCCGPA 1.000 0.000 NA NA 1.000
## 7 ab := a*b ab -0.061 0.005 -13.211 0 -0.070
## 8 total := c+(a*b) total 0.247 0.012 21.442 0 0.225
## ci.upper
## 1 -0.249
## 2 0.248
## 3 0.329
## 4 0.926
## 5 0.918
## 6 1.000
## 7 -0.052
## 8 0.270
library(diagram)
dataplot <- c(0, "'-.27*'", 0,
0, 0, 0,
"'.23*'", "'.31* (-.06*)'", 0)
M<- matrix (nrow=3, ncol=3, byrow = TRUE, data=dataplot)
plot<- plotmat (M, pos=c(1,2),
name= c( "Years Since HS","High School GPA", "College GPA"),
box.type = "rect", box.size = 0.12, box.prop=0.5, curve=0)
library(lavaan)
library(semPlot)
mediation.model2 <- '
# mediator
HSGPA ~ a*HSGradYears
MathGrade ~ b*HSGradYears
# direct effect
HSGPA ~ c*MathGrade
# indirect effect (a*b)
ab := a*b
# total effect
total := c + (a*b)
'
fit2 <- sem(mediation.model2, data = data, ordered = "MathGrade")
summary(fit2, fit.measures=T, standardized=T, ci=TRUE)
## lavaan 0.6-12 ended normally after 13 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 3907 6774
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 347.058 347.058
## Degrees of freedom 1 1
## P-value 0.000 0.000
## Scaling correction factor 1.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## HSGPA ~
## HSGradYers (a) -0.030 0.001 -22.121 0.000 -0.032 -0.027
## MathGrade ~
## HSGradYers (b) 0.015 0.003 4.593 0.000 0.009 0.022
## HSGPA ~
## MathGrade (c) 0.155 0.008 18.629 0.000 0.139 0.172
## Std.lv Std.all
##
## -0.030 -0.267
##
## 0.015 0.076
##
## 0.155 0.286
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .HSGPA 3.167 0.010 304.819 0.000 3.147 3.187
## .MathGrade 0.000 0.000 0.000
## Std.lv Std.all
## 3.167 5.814
## 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## MathGrade|t1 -1.048 0.026 -39.699 0.000 -1.099 -0.996
## MathGrade|t2 -0.810 0.024 -33.124 0.000 -0.858 -0.762
## MathGrade|t3 -0.155 0.022 -6.998 0.000 -0.198 -0.111
## MathGrade|t4 0.579 0.023 24.918 0.000 0.534 0.625
## Std.lv Std.all
## -1.048 -1.045
## -0.810 -0.807
## -0.155 -0.154
## 0.579 0.577
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .HSGPA 0.255 0.006 42.056 0.000 0.243 0.267
## .MathGrade 1.000 1.000 1.000
## Std.lv Std.all
## 0.255 0.859
## 1.000 0.994
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## MathGrade 1.000 1.000 1.000
## Std.lv Std.all
## 1.000 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## ab -0.000 0.000 -4.280 0.000 -0.001 -0.000
## total 0.155 0.008 18.560 0.000 0.138 0.171
## Std.lv Std.all
## -0.000 -0.020
## 0.155 0.265
medtable2<- standardizedsolution(fit2, type = "std.all")
medtable2
## lhs op rhs label est.std se z pvalue ci.lower
## 1 HSGPA ~ HSGradYears a -0.267 0.011 -24.597 0 -0.288
## 2 MathGrade ~ HSGradYears b 0.076 0.016 4.620 0 0.044
## 3 HSGPA ~ MathGrade c 0.286 0.014 20.142 0 0.258
## 4 MathGrade | t1 -1.045 0.027 -39.033 0 -1.097
## 5 MathGrade | t2 -0.807 0.025 -32.629 0 -0.856
## 6 MathGrade | t3 -0.154 0.022 -6.972 0 -0.198
## 7 MathGrade | t4 0.577 0.023 25.232 0 0.533
## 8 HSGPA ~~ HSGPA 0.859 0.009 92.105 0 0.841
## 9 MathGrade ~~ MathGrade 0.994 0.003 397.582 0 0.989
## 10 HSGradYears ~~ HSGradYears 1.000 0.000 NA NA 1.000
## 11 MathGrade ~*~ MathGrade 1.000 0.000 NA NA 1.000
## 12 HSGPA ~1 5.814 0.066 88.677 0 5.685
## 13 MathGrade ~1 0.000 0.000 NA NA 0.000
## 14 HSGradYears ~1 0.527 0.000 NA NA 0.527
## 15 ab := a*b ab -0.020 0.005 -4.295 0 -0.030
## 16 total := c+(a*b) total 0.265 0.015 17.529 0 0.236
## ci.upper
## 1 -0.245
## 2 0.108
## 3 0.314
## 4 -0.992
## 5 -0.759
## 6 -0.111
## 7 0.622
## 8 0.877
## 9 0.999
## 10 1.000
## 11 1.000
## 12 5.942
## 13 0.000
## 14 0.527
## 15 -0.011
## 16 0.295
library(diagram)
dataplot2 <- c(0, "'-.27*'", 0,
0, 0, 0,
"'.08*'", "'.29* (-.02*)'", 0)
M<- matrix (nrow=3, ncol=3, byrow = TRUE, data=dataplot2)
plot<- plotmat (M, pos=c(1,2),
name= c( "Years Since HS","High School GPA", "Math Grade"),
box.type = "rect", box.size = 0.12, box.prop=0.5, curve=0)
library(lavaan)
library(semPlot)
mediation.model3 <- '
# mediator
HSGPA ~ a*HSGradYears
EngGrade ~ b*HSGradYears
# direct effect
HSGPA ~ c*EngGrade
# indirect effect (a*b)
ab := a*b
# total effect
total := c + (a*b)
'
fit3 <- sem(mediation.model3, data = data, ordered = "EngGrade")
summary(fit3, fit.measures=T, standardized=T, ci=TRUE)
## lavaan 0.6-12 ended normally after 13 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 9
##
## Used Total
## Number of observations 3843 6774
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.000 0.000
## Degrees of freedom 0 0
##
## Model Test Baseline Model:
##
## Test statistic 328.895 328.895
## Degrees of freedom 1 1
## P-value 0.000 0.000
## Scaling correction factor 1.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.000 1.000
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.000 0.000
## P-value RMSEA <= 0.05 NA NA
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000 0.000
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## HSGPA ~
## HSGradYers (a) -0.031 0.002 -18.220 0.000 -0.034 -0.028
## EngGrade ~
## HSGradYers (b) 0.035 0.004 9.710 0.000 0.028 0.042
## HSGPA ~
## EngGrade (c) 0.167 0.009 18.135 0.000 0.149 0.185
## Std.lv Std.all
##
## -0.031 -0.267
##
## 0.035 0.163
##
## 0.167 0.306
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .HSGPA 2.974 0.010 301.638 0.000 2.955 2.994
## .EngGrade 0.000 0.000 0.000
## Std.lv Std.all
## 2.974 5.365
## 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## EngGrade|t1 -1.159 0.028 -40.960 0.000 -1.214 -1.103
## EngGrade|t2 -1.027 0.027 -38.322 0.000 -1.079 -0.974
## EngGrade|t3 -0.392 0.023 -17.199 0.000 -0.437 -0.348
## EngGrade|t4 0.470 0.023 20.468 0.000 0.425 0.515
## Std.lv Std.all
## -1.159 -1.143
## -1.027 -1.013
## -0.392 -0.387
## 0.470 0.464
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .HSGPA 0.265 0.007 38.410 0.000 0.251 0.278
## .EngGrade 1.000 1.000 1.000
## Std.lv Std.all
## 0.265 0.862
## 1.000 0.973
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## EngGrade 1.000 1.000 1.000
## Std.lv Std.all
## 1.000 1.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## ab -0.001 0.000 -7.801 0.000 -0.001 -0.001
## total 0.166 0.009 18.034 0.000 0.148 0.184
## Std.lv Std.all
## -0.001 -0.044
## 0.166 0.262
medtable3<- standardizedsolution(fit3, type = "std.all")
medtable3
## lhs op rhs label est.std se z pvalue ci.lower
## 1 HSGPA ~ HSGradYears a -0.267 0.014 -19.380 0 -0.294
## 2 EngGrade ~ HSGradYears b 0.163 0.016 9.975 0 0.131
## 3 HSGPA ~ EngGrade c 0.306 0.015 19.749 0 0.275
## 4 EngGrade | t1 -1.143 0.029 -39.317 0 -1.200
## 5 EngGrade | t2 -1.013 0.027 -36.862 0 -1.067
## 6 EngGrade | t3 -0.387 0.023 -16.882 0 -0.432
## 7 EngGrade | t4 0.464 0.022 20.916 0 0.420
## 8 HSGPA ~~ HSGPA 0.862 0.010 83.173 0 0.842
## 9 EngGrade ~~ EngGrade 0.973 0.005 182.927 0 0.963
## 10 HSGradYears ~~ HSGradYears 1.000 0.000 NA NA 1.000
## 11 EngGrade ~*~ EngGrade 1.000 0.000 NA NA 1.000
## 12 HSGPA ~1 5.365 0.069 78.021 0 5.230
## 13 EngGrade ~1 0.000 0.000 NA NA 0.000
## 14 HSGradYears ~1 0.500 0.000 NA NA 0.500
## 15 ab := a*b ab -0.044 0.005 -8.008 0 -0.054
## 16 total := c+(a*b) total 0.262 0.016 16.334 0 0.231
## ci.upper
## 1 -0.240
## 2 0.195
## 3 0.336
## 4 -1.086
## 5 -0.959
## 6 -0.342
## 7 0.507
## 8 0.882
## 9 0.984
## 10 1.000
## 11 1.000
## 12 5.500
## 13 0.000
## 14 0.500
## 15 -0.033
## 16 0.293
library(diagram)
dataplot3 <- c(0, "'-.27*'", 0,
0, 0, 0,
"'.16*'", "'.31* (-.04*)'", 0)
M<- matrix (nrow=3, ncol=3, byrow = TRUE, data=dataplot3)
plot<- plotmat (M, pos=c(1,2),
name= c( "Years Since HS","High School GPA", "English Grade"),
box.type = "rect", box.size = 0.12, box.prop=0.5, curve=0)
