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library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.2
## ✔ ggplot2 3.5.2 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(readxl)
library(dplyr)
library(pastecs)##
## Attaching package: 'pastecs'
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## The following objects are masked from 'package:dplyr':
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## first, last
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## The following object is masked from 'package:tidyr':
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## extractdistrict<-read_excel("district.xls")model1<- lm(DA0912DR21R~DDA00A001S22R+PROPWLTH+DDE00A001S22R, data=district)
summary(model1)##
## Call:
## lm(formula = DA0912DR21R ~ DDA00A001S22R + PROPWLTH + DDE00A001S22R,
## data = district)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.722 -0.973 -0.110 0.660 39.477
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.146392 0.752444 12.156 < 2e-16 ***
## DDA00A001S22R -0.084529 0.017220 -4.909 1.06e-06 ***
## PROPWLTH$1,892,208 and over 0.313910 0.582796 0.539 0.590
## PROPWLTH$164,606 to < $234,712 -0.032790 0.500695 -0.065 0.948
## PROPWLTH$234,712 to < $298,152 -0.254902 0.462105 -0.552 0.581
## PROPWLTH$298,152 to < $340,843 -0.111611 0.504490 -0.221 0.825
## PROPWLTH$340,843 to < $359,962 0.090908 0.605503 0.150 0.881
## PROPWLTH$359,962 to < $411,857 -0.448565 0.457677 -0.980 0.327
## PROPWLTH$411,857 to < $427,868 0.082275 0.629944 0.131 0.896
## PROPWLTH$427,868 to < $456,750 -0.090155 0.539586 -0.167 0.867
## PROPWLTH$456,750 to < $479,670 -0.094413 0.574538 -0.164 0.870
## PROPWLTH$479,670 to < $526,224 -0.174903 0.521662 -0.335 0.737
## PROPWLTH$526,224 to < $539,089 0.104998 0.872696 0.120 0.904
## PROPWLTH$539,089 to < $573,876 -0.396455 0.624508 -0.635 0.526
## PROPWLTH$573,876 to < $602,435 0.091625 0.644454 0.142 0.887
## PROPWLTH$602,435 to < $674,066 -0.132866 0.560522 -0.237 0.813
## PROPWLTH$674,066 to < $710,768 -0.196519 0.597997 -0.329 0.743
## PROPWLTH$710,768 to < $836,577 -0.197682 0.527288 -0.375 0.708
## PROPWLTH$836,577 to < $949,515 -0.006754 0.568720 -0.012 0.991
## PROPWLTH$949,515 to < $1,039,446 0.310709 0.725872 0.428 0.669
## PROPWLTHNon-taxing entities 2.190254 0.438502 4.995 6.87e-07 ***
## PROPWLTHUnder $164,606 0.159319 0.587300 0.271 0.786
## DDE00A001S22R -0.024671 0.017281 -1.428 0.154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.014 on 1071 degrees of freedom
## (113 observations deleted due to missingness)
## Multiple R-squared: 0.1937, Adjusted R-squared: 0.1772
## F-statistic: 11.7 on 22 and 1071 DF, p-value: < 2.2e-16model2<- lm(DA0912DR21R~DDA00A001S22R+DDE00A001S22R, data=district)
summary(model2)##
## Call:
## lm(formula = DA0912DR21R ~ DDA00A001S22R + DDE00A001S22R, data = district)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.145 -1.038 -0.263 0.504 40.876
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.99672 0.65146 15.345 < 2e-16 ***
## DDA00A001S22R -0.09809 0.01715 -5.721 1.37e-08 ***
## DDE00A001S22R -0.02006 0.01743 -1.151 0.25
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.075 on 1091 degrees of freedom
## (113 observations deleted due to missingness)
## Multiple R-squared: 0.1451, Adjusted R-squared: 0.1435
## F-statistic: 92.58 on 2 and 1091 DF, p-value: < 2.2e-16model3<- lm(DA0912DR21R~DDA00A001S22R+DPFEAINSK, data=district)
summary(model3)##
## Call:
## lm(formula = DA0912DR21R ~ DDA00A001S22R + DPFEAINSK, data = district)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.226 -1.047 -0.280 0.569 39.828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.552e+00 7.790e-01 10.98 < 2e-16 ***
## DDA00A001S22R -1.131e-01 8.513e-03 -13.29 < 2e-16 ***
## DPFEAINSK 1.660e-04 5.338e-05 3.11 0.00192 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.059 on 1090 degrees of freedom
## (114 observations deleted due to missingness)
## Multiple R-squared: 0.1532, Adjusted R-squared: 0.1517
## F-statistic: 98.61 on 2 and 1090 DF, p-value: < 2.2e-16I ran multiple models with different Independent variables the first one was “DDA00A001S22R (STAAR: % ALL SUBJECTS AT APPROACHES GRADE LEVEL STANDARD OR ABOVE) +PROPWLTH (PROPERTY WEALTH) +DDE00A001S22R (ECONOMICALLY DISADVANTAGED AT APPROACHES GRADE LEVEL STANDARD OR ABOVE)”, to see the effect of those three on the dependent variable which was dropout rate. it got an “Adjusted R-squared: 0.1772” which means that they explain 17.7% of the dropout rate. But, it was formatted weird so I used a different set of variables “DDA00A001S22R (STAAR: % ALL SUBJECTS AT APPROACHES GRADE LEVEL STANDARD OR ABOVE)+DDE00A001S22R (ECONOMICALLY DISADVANTAGED AT APPROACHES GRADE LEVEL STANDARD OR ABOVE)” and that was shown to be significant together but, it seemed that ALL SUBJECTS AT APPROACHES GRADE LEVEL STANDARD OR ABOVE had a stronger pull. So i did a third model and called it (model3) where it took ALL SUBJECTS AT APPROACHES GRADE LEVEL STANDARD OR ABOVE and TOTAL ACTUAL INSTRUCTIONAL EXPENDITURES PER PUPIL and those both seemed to have a strong correlation together and separately. now with the two coefficients I have are DDA00A001S22R -1.131e-01 and DPFEAINSK 1.660e-04. So, 0.1131 and 0.0001660 respectively. So that means for every one dropout we’ll see that much in changes
model3<- lm(DA0912DR21R~DDA00A001S22R+DPFEAINSK, data=district)
plot(model3, which = 1)
the line is mostly straight,