getwd()[1] "/Users/gretacapelletti/Downloads"# make sure the packages for this chapter
# are installed, install if necessary
pkg <- c("ggplot2", "scales", "maptools",
"sp", "maps", "grid", "car" )
new.pkg <- pkg[!(pkg %in% installed.packages())]
if (length(new.pkg)) {
install.packages(new.pkg)
}Warning in install.packages :
package ‘maptools’ is not available for this version of R
A version of this package for your version of R might be available elsewhere,
see the ideas at
https://cran.r-project.org/doc/manuals/r-patched/R-admin.html#Installing-packages# read the CSV with headers
regression1<-read.csv("incidents.csv", header=T,sep =",")#View(regression1)summary(regression1) area zone population incidents
Length:16 Length:16 Length:16 Min. : 103.0
Class :character Class :character Class :character 1st Qu.: 277.8
Mode :character Mode :character Mode :character Median : 654.0
Mean : 695.2
3rd Qu.: 853.0
Max. :2072.0 str(regression1)'data.frame': 16 obs. of 4 variables:
$ area : chr "Boulder" "California-lexington" "Huntsville" "Seattle" ...
$ zone : chr "west" "east" "east" "west" ...
$ population: chr "107,353" "326,534" "444,752" "750,000" ...
$ incidents : int 605 103 161 1703 1003 527 721 704 105 403 ...regression1$population <- as.numeric(gsub(",","",regression1$population))
regression1$population [1] 107353 326534 444752 750000 64403 2744878 1600000 2333000 1572816 712091 6900000 2700000 4900000
[14] 4200000 5200000 7100000str(regression1$population) num [1:16] 107353 326534 444752 750000 64403 ...regression2<-regression1[,-1]#new data frame with the deletion of column 1 head(regression2)reg.fit1<-lm(regression1$incidents ~ regression1$population)summary(reg.fit1)
Call:
lm(formula = regression1$incidents ~ regression1$population)
Residuals:
Min 1Q Median 3Q Max
-684.5 -363.5 -156.2 133.9 1164.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.749e+02 2.018e+02 2.353 0.0337 *
regression1$population 8.462e-05 5.804e-05 1.458 0.1669
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 534.9 on 14 degrees of freedom
Multiple R-squared: 0.1318, Adjusted R-squared: 0.0698
F-statistic: 2.126 on 1 and 14 DF, p-value: 0.1669Based on the output obtained above, please answer the following question:
Is Population significant at a 5% significance level? What is the adjusted-R squared of the model? To understand if population is significant, I have to look at the p-value for population, which is 0.1669. Since this value is greater than the 5% significance level (0.05), population is not significant.
Adjusted R-squared of the model is 0.0698.
reg.fit2<-lm(incidents ~ zone+population, data = regression1)summary(reg.fit2)
Call:
lm(formula = incidents ~ zone + population, data = regression1)
Residuals:
Min 1Q Median 3Q Max
-537.21 -273.14 -57.89 188.17 766.03
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.612e+02 1.675e+02 0.962 0.35363
zonewest 7.266e+02 1.938e+02 3.749 0.00243 **
population 6.557e-05 4.206e-05 1.559 0.14300
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 384.8 on 13 degrees of freedom
Multiple R-squared: 0.5828, Adjusted R-squared: 0.5186
F-statistic: 9.081 on 2 and 13 DF, p-value: 0.003404Based on the output obtained above, please answer the following question:
Are Population and/or Zone significant at a 5% significance level? What is the adjusted-R squared of the model?
At the 5% significance level, Zone is significant, as its p-value is 0.00243, which is less than 0.05. Population tho, is not significant, as its p-value is 0.14300, which is greater than 0.05. The adjusted R-squared value of the model is 0.5186.
regression1$zone <- ifelse(regression1$zone == "west", 1, 0)#Please explain the syntax and the output#View(regression1)str(regression1)'data.frame': 16 obs. of 4 variables:
$ area : chr "Boulder" "California-lexington" "Huntsville" "Seattle" ...
$ zone : num 1 0 0 1 1 0 1 1 0 0 ...
$ population: num 107353 326534 444752 750000 64403 ...
$ incidents : int 605 103 161 1703 1003 527 721 704 105 403 ...#regression1$zone<-as.integer((regression1$zone),replace=TRUE) was not necessaryinteraction<-regression1$zone*regression1$population#Explain the syntaxreg.fit3<-lm(regression1$incidents~interaction+regression1$population+regression1$zone)summary(reg.fit3)
Call:
lm(formula = regression1$incidents ~ interaction + regression1$population +
regression1$zone)
Residuals:
Min 1Q Median 3Q Max
-540.91 -270.93 -59.56 187.99 767.99
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.659e+02 2.313e+02 0.717 0.4869
interaction 2.974e-06 9.469e-05 0.031 0.9755
regression1$population 6.352e-05 7.868e-05 0.807 0.4352
regression1$zone 7.192e+02 3.108e+02 2.314 0.0392 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 400.5 on 12 degrees of freedom
Multiple R-squared: 0.5829, Adjusted R-squared: 0.4786
F-statistic: 5.589 on 3 and 12 DF, p-value: 0.01237Based on the output obtained above, please answer the following question:
Is Population significant at a 5% significance level? Is Zone significant at a 5% significance level? Is the interaction term significant at a 5% significance level? What is the adjusted-R squared of the model?
At the 5% significance level, Population is not significant, as its p-value is 0.4352, which is greater than 0.05. Zone is significant, as its p-value is 0.0392, which is less than 0.05. The interaction term is not significant, as its p-value is 0.9755, which is greater than 0.05. The adjusted R-squared value of the model is 0.4786.
Let us now run a model where the only feature is the interaction term.
reg.fit4<-lm(regression1$incidents~interaction)summary(reg.fit4)
Call:
lm(formula = regression1$incidents ~ interaction)
Residuals:
Min 1Q Median 3Q Max
-650.28 -301.09 -83.71 123.23 1103.76
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.951e+02 1.320e+02 3.751 0.00215 **
interaction 1.389e-04 4.737e-05 2.932 0.01093 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 451.9 on 14 degrees of freedom
Multiple R-squared: 0.3804, Adjusted R-squared: 0.3361
F-statistic: 8.595 on 1 and 14 DF, p-value: 0.01093Is the interaction term significant at a 5% significance level? What is the adjusted-R squared of the model? At the 5% significance level, the interaction term is significant, as its p-value is 0.01093, which is less than 0.05. The adjusted R-squared value of the model is 0.3361.
Which of the models run above would you choose to make predictions? Why?? Model 2 (reg.fit2) would be the best choice for making predictions. This model includes both “zone” and “population” as predictors, with “zone” being statistically significant at the 5% significance level. It also has a higher adjusted R-squared value (0.5186) compared to the other models, suggesting that it provides a better fit to the data and will likely yield more reliable predictions.