Homework 6

library(tidyverse)

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library(ggplot2)

library(readxl)
district <-read_excel("district (2).xls")

My dependent Variable I am selecting is the SAT Average Score (DA0CSA21R) The independent variables I am selecting are: teacher turnover rate, attendance rate, % of economically disadvantaged

district_multiple<-lm(DA0CSA21R~DA0AT21R+DPETECOP,data=district)

summary(district_multiple)

## 
## Call:
## lm(formula = DA0CSA21R ~ DA0AT21R + DPETECOP, data = district)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -984.53   76.88  147.79  203.35  443.09 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 218.0867   501.6097   0.435   0.6638    
## DA0AT21R      8.4676     5.1159   1.655   0.0982 .  
## DPETECOP     -3.3484     0.6494  -5.156 3.08e-07 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 380.1 on 941 degrees of freedom
##   (263 observations deleted due to missingness)
## Multiple R-squared:  0.0426, Adjusted R-squared:  0.04057 
## F-statistic: 20.94 on 2 and 941 DF,  p-value: 1.269e-09

So this means that the model only explains about 4.26 percent of variance of the dependent variable. A significant value would be the percent of disadvantaged because that has a strong negative relationship with the dependent variable. The coefficient for this strong relationship is -3.3484. So when the independent variable does up, the dependent variable goes down by 3.3484. Then the insignificant variable would be the attendance rate because its positive but its very weak and not close to the p value.

plot(district_multiple, which=1)

The model violates the assumption of linearity and the red line is not completely horizontally.