Intro Stuff
setwd( "C:/Users/Jerome/Documents/Data_Science_110/Datasets")
library(tidyverse)
## -- Attaching packages ----------------------------------------------------------------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.1 v dplyr 1.0.0
## v tidyr 1.1.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## -- Conflicts -------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(mosaic)
## Warning: package 'mosaic' was built under R version 4.0.2
## Loading required package: lattice
## Loading required package: ggformula
## Warning: package 'ggformula' was built under R version 4.0.2
## Loading required package: ggstance
## Warning: package 'ggstance' was built under R version 4.0.2
##
## Attaching package: 'ggstance'
## The following objects are masked from 'package:ggplot2':
##
## geom_errorbarh, GeomErrorbarh
##
## New to ggformula? Try the tutorials:
## learnr::run_tutorial("introduction", package = "ggformula")
## learnr::run_tutorial("refining", package = "ggformula")
## Loading required package: mosaicData
## Warning: package 'mosaicData' was built under R version 4.0.2
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
## Registered S3 method overwritten by 'mosaic':
## method from
## fortify.SpatialPolygonsDataFrame ggplot2
##
## The 'mosaic' package masks several functions from core packages in order to add
## additional features. The original behavior of these functions should not be affected by this.
##
## Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.
##
## Have you tried the ggformula package for your plots?
##
## Attaching package: 'mosaic'
## The following object is masked from 'package:Matrix':
##
## mean
## The following objects are masked from 'package:dplyr':
##
## count, do, tally
## The following object is masked from 'package:purrr':
##
## cross
## The following object is masked from 'package:ggplot2':
##
## stat
## The following objects are masked from 'package:stats':
##
## binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
## quantile, sd, t.test, var
## The following objects are masked from 'package:base':
##
## max, mean, min, prod, range, sample, sum
lcn_sample_2 <- read.csv("lcn_sample_2.csv")
Run Regression
fit3 <- lm(MEASURE ~ as.factor(UrbRur) +as.factor(Educ) +as.factor(Wealth), data = lcn_sample_2)
summary(fit3)
##
## Call:
## lm(formula = MEASURE ~ as.factor(UrbRur) + as.factor(Educ) +
## as.factor(Wealth), data = lcn_sample_2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -47.064 -18.295 8.012 14.900 40.980
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.011 5.004 7.996 2.13e-13 ***
## as.factor(UrbRur)2 7.051 4.232 1.666 0.0975 .
## as.factor(Educ)1 -2.634 3.593 -0.733 0.4645
## as.factor(Educ)2 -5.875 9.821 -0.598 0.5505
## as.factor(Educ)3 1.903 5.926 0.321 0.7485
## as.factor(Educ)4 -4.718 21.722 -0.217 0.8283
## as.factor(Wealth)2 -5.002 4.181 -1.197 0.2332
## as.factor(Wealth)3 -2.004 4.885 -0.410 0.6821
## as.factor(Wealth)4 7.064 5.641 1.252 0.2123
## as.factor(Wealth)5 8.396 10.456 0.803 0.4231
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.3 on 165 degrees of freedom
## Multiple R-squared: 0.04773, Adjusted R-squared: -0.004216
## F-statistic: 0.9188 on 9 and 165 DF, p-value: 0.5103
print(fit3)
##
## Call:
## lm(formula = MEASURE ~ as.factor(UrbRur) + as.factor(Educ) +
## as.factor(Wealth), data = lcn_sample_2)
##
## Coefficients:
## (Intercept) as.factor(UrbRur)2 as.factor(Educ)1 as.factor(Educ)2
## 40.011 7.051 -2.634 -5.875
## as.factor(Educ)3 as.factor(Educ)4 as.factor(Wealth)2 as.factor(Wealth)3
## 1.903 -4.718 -5.002 -2.004
## as.factor(Wealth)4 as.factor(Wealth)5
## 7.064 8.396