Multiple Linear Regression Model
MD. Mehedi Hassan Galib
19 July, 2020
LOading dataset from Desktop
LungCapData <- read.delim(file.choose(),header = T)
attach(LungCapData)
names(LungCapData)## [1] "LungCap" "Age" "Height" "Smoke" "Gender" "Caesarean"
Linear Regression Model with 2 explanatory variables
mmod <- lm(LungCap~Age + Height)
summary(mmod)##
## Call:
## lm(formula = LungCap ~ Age + Height)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4080 -0.7097 -0.0078 0.7167 3.1679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -11.747065 0.476899 -24.632 < 2e-16 ***
## Age 0.126368 0.017851 7.079 3.45e-12 ***
## Height 0.278432 0.009926 28.051 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.056 on 722 degrees of freedom
## Multiple R-squared: 0.843, Adjusted R-squared: 0.8425
## F-statistic: 1938 on 2 and 722 DF, p-value: < 2.2e-16
Examine the residual plot or error
Checking linear regression assumptions
par(mfrow= c(2,2))
plot(mmod)
Linear Regression Model with all explanatory variables
mmod1 <- lm(LungCap~Age + Height+Smoke+Gender+Caesarean)
summary(mmod1)##
## Call:
## lm(formula = LungCap ~ Age + Height + Smoke + Gender + Caesarean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3388 -0.7200 0.0444 0.7093 3.0172
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -11.32249 0.47097 -24.041 < 2e-16 ***
## Age 0.16053 0.01801 8.915 < 2e-16 ***
## Height 0.26411 0.01006 26.248 < 2e-16 ***
## Smokeyes -0.60956 0.12598 -4.839 1.60e-06 ***
## Gendermale 0.38701 0.07966 4.858 1.45e-06 ***
## Caesareanyes -0.21422 0.09074 -2.361 0.0185 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.02 on 719 degrees of freedom
## Multiple R-squared: 0.8542, Adjusted R-squared: 0.8532
## F-statistic: 842.8 on 5 and 719 DF, p-value: < 2.2e-16
Examine the residual plot or error
Checking linear regression assumptions
par(mfrow= c(2,2))
plot(mmod1)