GLM ASSIGNMENNT
RAMENDRA SINGH CHAUHAN
22/02/2020
# reading the rda file
Concrete.df <- read.csv("Concrete_Data.csv")
attach(Concrete.df)
dim(Concrete.df)## [1] 1030 12Column names of the data frame
# column names of the data frame
colnames(Concrete.df)## [1] "ID" "Cement" "BlastFurnace" "FlyAsh"
## [5] "Water" "Superplasticizer" "CoarseAggregate" "FineAggregate"
## [9] "Age" "Strength" "new_input.1" "new_input.2"Summary Statistics of the data frame
summary(Concrete.df)## ID Cement BlastFurnace FlyAsh
## Min. : 1.0 Min. :102.0 Min. : 0.0 Min. : 0.00
## 1st Qu.: 258.2 1st Qu.:192.4 1st Qu.: 0.0 1st Qu.: 0.00
## Median : 515.5 Median :272.9 Median : 22.0 Median : 0.00
## Mean : 515.5 Mean :281.2 Mean : 73.9 Mean : 54.19
## 3rd Qu.: 772.8 3rd Qu.:350.0 3rd Qu.:142.9 3rd Qu.:118.30
## Max. :1030.0 Max. :540.0 Max. :359.4 Max. :200.10
## Water Superplasticizer CoarseAggregate FineAggregate
## Min. :121.8 Min. : 0.000 Min. : 801.0 Min. :594.0
## 1st Qu.:164.9 1st Qu.: 0.000 1st Qu.: 932.0 1st Qu.:731.0
## Median :185.0 Median : 6.400 Median : 968.0 Median :779.5
## Mean :181.6 Mean : 6.205 Mean : 972.9 Mean :773.6
## 3rd Qu.:192.0 3rd Qu.:10.200 3rd Qu.:1029.4 3rd Qu.:824.0
## Max. :247.0 Max. :32.200 Max. :1145.0 Max. :992.6
## Age Strength new_input.1 new_input.2
## Min. : 1.00 Min. : 2.33 Min. :2074 Min. : 33.60
## 1st Qu.: 7.00 1st Qu.:23.71 1st Qu.:3457 1st Qu.: 60.71
## Median : 28.00 Median :34.45 Median :4081 Median : 84.87
## Mean : 45.66 Mean :35.82 Mean :4013 Mean : 87.35
## 3rd Qu.: 56.00 3rd Qu.:46.13 3rd Qu.:4419 3rd Qu.:108.00
## Max. :365.00 Max. :82.60 Max. :6679 Max. :165.00Q1 - Fit a multiple linear regression to the above data considering all the input variables.The information of the output variable is given in the information file Multiple Linear Regression Model
Model1 <- lm(Strength ~ Cement + BlastFurnace
+ FlyAsh
+ Water
+ Superplasticizer + CoarseAggregate
+ FineAggregate + Age,
data = Concrete.df)
summary(Model1)##
## Call:
## lm(formula = Strength ~ Cement + BlastFurnace + FlyAsh + Water +
## Superplasticizer + CoarseAggregate + FineAggregate + Age,
## data = Concrete.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.654 -6.302 0.703 6.569 34.450
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -23.331214 26.585504 -0.878 0.380372
## Cement 0.119804 0.008489 14.113 < 2e-16 ***
## BlastFurnace 0.103866 0.010136 10.247 < 2e-16 ***
## FlyAsh 0.087934 0.012583 6.988 5.02e-12 ***
## Water -0.149918 0.040177 -3.731 0.000201 ***
## Superplasticizer 0.292225 0.093424 3.128 0.001810 **
## CoarseAggregate 0.018086 0.009392 1.926 0.054425 .
## FineAggregate 0.020190 0.010702 1.887 0.059491 .
## Age 0.114222 0.005427 21.046 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.4 on 1021 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6125
## F-statistic: 204.3 on 8 and 1021 DF, p-value: < 2.2e-16Q2 - Explain the results obtained from the statistical software along with the diagnostic results/plots.I expect your explanation to be detailed along with interpretations Assumptions and Diagnostics Linearity of the Data
# Linearity of the data
plot(Model1, 1)
Ideally, the residual plot will show no fitted pattern. That is, the red line should be approximately horizontal at zero. The presence of a pattern may indicate a problem with some aspect of the linear model.
In our example, there is no pattern in the residual plot. This suggests that we can assume linear relationship between the predictors and the outcome variables. Normality of Residuals QQ Plot of Residuals The QQ plot of residuals can be used to visually check the normality assumption. The normal probability plot of residuals should approximately follow a straight line.
In our example, all the points fall approximately along this reference line, so we can assume normality.
# Normality of residuals
plot(Model1, 2)
Perform a Shapiro-Wilk Normality Test
library(MASS)
# distribution of studentized residuals
sresid <- studres(Model1)
shapiro.test(sresid)##
## Shapiro-Wilk normality test
##
## data: sresid
## W = 0.99514, p-value = 0.002233The p-value is less than 0.05, which is significant, So we can say that data is deviated from the normality assumption. Testing the Homoscedasticity Assumption Scale-location plot This assumption can be checked by examining the Scale-location plot, also known as the spread-location plot.
plot(Model1, 3)
This plot shows if residuals are spread equally along the ranges of predictors. it is good if you see a horizontal line with equally spread points. In our example, this is the case. we can assume Homogeneity of variance
ncvTest() For Homoscedasticity
library(car)## Loading required package: carDataLoading required package: carData
# non-constant error variance test
ncvTest(Model1)## Non-constant Variance Score Test
## Variance formula: ~ fitted.values
## Chisquare = 119.2972, Df = 1, p = < 2.22e-16p < .05, suggesting that our data is not homoscedastic. Breusch-Pagan Test For Homoscedasticity
library(lmtest)## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericbptest(Model1)##
## studentized Breusch-Pagan test
##
## data: Model1
## BP = 137.2, df = 8, p-value < 2.2e-16p < .05, suggesting that our data is not homoscedastic. Goldfeld–Quandt test Test For Homoscedasticity
library(lmtest)
gqtest(Model1)##
## Goldfeld-Quandt test
##
## data: Model1
## GQ = 0.48746, df1 = 506, df2 = 506, p-value = 1
## alternative hypothesis: variance increases from segment 1 to 2p > .05, suggesting that our data is homoscedastic.
Testing the Independence (Autocorrelation) Assumption
# durbin watson test
durbinWatsonTest(Model1)## lag Autocorrelation D-W Statistic p-value
## 1 0.3559936 1.281638 0
## Alternative hypothesis: rho != 0p < 0.05, so the errors are autocorrelated. We have violated the independence assumption. Testing the Multicollinearity Assumption Correlation Matrix
library(Hmisc)## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:base':
##
## format.pval, units# creating subset of continuous independent variables from the dataframe
expVar <- Concrete.df[c("Cement", "BlastFurnace", "FlyAsh","Water" ,"Superplasticizer",
"CoarseAggregate","FineAggregate","Age")]
# correlation matrix
rcorr(as.matrix(expVar))## Cement BlastFurnace FlyAsh Water Superplasticizer
## Cement 1.00 -0.28 -0.40 -0.08 0.09
## BlastFurnace -0.28 1.00 -0.32 0.11 0.04
## FlyAsh -0.40 -0.32 1.00 -0.26 0.38
## Water -0.08 0.11 -0.26 1.00 -0.66
## Superplasticizer 0.09 0.04 0.38 -0.66 1.00
## CoarseAggregate -0.11 -0.28 -0.01 -0.18 -0.27
## FineAggregate -0.22 -0.28 0.08 -0.45 0.22
## Age 0.08 -0.04 -0.15 0.28 -0.19
## CoarseAggregate FineAggregate Age
## Cement -0.11 -0.22 0.08
## BlastFurnace -0.28 -0.28 -0.04
## FlyAsh -0.01 0.08 -0.15
## Water -0.18 -0.45 0.28
## Superplasticizer -0.27 0.22 -0.19
## CoarseAggregate 1.00 -0.18 0.00
## FineAggregate -0.18 1.00 -0.16
## Age 0.00 -0.16 1.00
##
## n= 1030
##
##
## P
## Cement BlastFurnace FlyAsh Water Superplasticizer
## Cement 0.0000 0.0000 0.0088 0.0030
## BlastFurnace 0.0000 0.0000 0.0006 0.1652
## FlyAsh 0.0000 0.0000 0.0000 0.0000
## Water 0.0088 0.0006 0.0000 0.0000
## Superplasticizer 0.0030 0.1652 0.0000 0.0000
## CoarseAggregate 0.0004 0.0000 0.7495 0.0000 0.0000
## FineAggregate 0.0000 0.0000 0.0111 0.0000 0.0000
## Age 0.0085 0.1559 0.0000 0.0000 0.0000
## CoarseAggregate FineAggregate Age
## Cement 0.0004 0.0000 0.0085
## BlastFurnace 0.0000 0.0000 0.1559
## FlyAsh 0.7495 0.0111 0.0000
## Water 0.0000 0.0000 0.0000
## Superplasticizer 0.0000 0.0000 0.0000
## CoarseAggregate 0.0000 0.9230
## FineAggregate 0.0000 0.0000
## Age 0.9230 0.0000Variance Inflation Factor for Multicollinearity
# detecting multicollinearity
library(car)
vif(Model1)## Cement BlastFurnace FlyAsh Water
## 7.488944 7.276963 6.170634 7.003957
## Superplasticizer CoarseAggregate FineAggregate Age
## 2.963776 5.074617 7.005081 1.118367Q3 -Based on your diagnostics plots’ interpretations, do you want to recommend any changes inthe model? If so do the changes along with the reasons and fit the model again. If no changerequired then support your arguments Variable Importance
library(caret)##
## Attaching package: 'caret'## The following object is masked from 'package:survival':
##
## clustervarImp(Model1)## Overall
## Cement 14.112863
## BlastFurnace 10.247419
## FlyAsh 6.988168
## Water 3.731446
## Superplasticizer 3.127937
## CoarseAggregate 1.925656
## FineAggregate 1.886652
## Age 21.046426Taking the Most Important variable in the Model (Also Reducing Multicollinearity)
Model2 <- lm(Strength ~ Cement
+ BlastFurnace
+ Age,
data = Concrete.df)
summary(Model2)##
## Call:
## lm(formula = Strength ~ Cement + BlastFurnace + Age, data = Concrete.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.830 -9.095 -0.614 8.418 40.630
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.921151 1.340214 2.18 0.0295 *
## Cement 0.088941 0.003980 22.34 <2e-16 ***
## BlastFurnace 0.058296 0.004810 12.12 <2e-16 ***
## Age 0.078438 0.006337 12.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.79 on 1026 degrees of freedom
## Multiple R-squared: 0.4151, Adjusted R-squared: 0.4134
## F-statistic: 242.7 on 3 and 1026 DF, p-value: < 2.2e-16Q4 -In the same email, you will get another data as “add columns”. Merge this new data with the previous data. Now fit a multiple linear regression on the merged data with all the input variables. Explain your results. Comment on the newly added variables and whether you want to keep them in the model or not. Justify your answer in either case Fitting Model With Extra Variables
Concrete.df <- read.csv("Concrete_Data.csv")
attach(Concrete.df)## The following objects are masked from Concrete.df (pos = 14):
##
## Age, BlastFurnace, Cement, CoarseAggregate, FineAggregate, FlyAsh,
## ID, new_input.1, new_input.2, Strength, Superplasticizer, Waterdim(Concrete.df)## [1] 1030 12Model3 <- lm(Strength ~ Cement
+ BlastFurnace
+ FlyAsh
+ Water
+ Superplasticizer
+ CoarseAggregate
+ FineAggregate
+ Age
+new_input.1
+ new_input.2,
data = Concrete.df)
summary(Model3)##
## Call:
## lm(formula = Strength ~ Cement + BlastFurnace + FlyAsh + Water +
## Superplasticizer + CoarseAggregate + FineAggregate + Age +
## new_input.1 + new_input.2, data = Concrete.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.668 -6.309 0.693 6.609 34.527
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.131e+01 2.339e+02 -0.048 0.96146
## Cement 1.463e+00 2.296e+01 0.064 0.94922
## BlastFurnace 1.036e-01 1.033e-02 10.027 < 2e-16 ***
## FlyAsh 8.758e-02 1.287e-02 6.807 1.7e-11 ***
## Water -1.266e-01 1.998e-01 -0.634 0.52655
## Superplasticizer 2.940e-01 9.501e-02 3.095 0.00202 **
## CoarseAggregate 1.806e-02 9.445e-03 1.912 0.05612 .
## FineAggregate 1.983e-02 1.104e-02 1.796 0.07273 .
## Age 1.144e-01 5.629e-03 20.325 < 2e-16 ***
## new_input.1 -6.618e-04 5.508e-03 -0.120 0.90439
## new_input.2 -4.474e+00 7.653e+01 -0.058 0.95339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.41 on 1019 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6118
## F-statistic: 163.1 on 10 and 1019 DF, p-value: < 2.2e-16Step AIC on Model 3
library(MASS)
StepAICModel <- stepAIC(Model3)## Start: AIC=4836.89
## Strength ~ Cement + BlastFurnace + FlyAsh + Water + Superplasticizer +
## CoarseAggregate + FineAggregate + Age + new_input.1 + new_input.2
##
## Df Sum of Sq RSS AIC
## - new_input.2 1 0 110412 4834.9
## - Cement 1 0 110412 4834.9
## - new_input.1 1 2 110413 4834.9
## - Water 1 43 110455 4835.3
## <none> 110411 4836.9
## - FineAggregate 1 350 110761 4838.1
## - CoarseAggregate 1 396 110807 4838.6
## - Superplasticizer 1 1038 111449 4844.5
## - FlyAsh 1 5021 115432 4880.7
## - BlastFurnace 1 10895 121306 4931.8
## - Age 1 44759 155170 5185.4
##
## Step: AIC=4834.9
## Strength ~ Cement + BlastFurnace + FlyAsh + Water + Superplasticizer +
## CoarseAggregate + FineAggregate + Age + new_input.1
##
## Df Sum of Sq RSS AIC
## - new_input.1 1 2 110413 4832.9
## - Water 1 43 110455 4833.3
## <none> 110412 4834.9
## - FineAggregate 1 354 110766 4836.2
## - CoarseAggregate 1 402 110814 4836.6
## - Superplasticizer 1 1040 111452 4842.6
## - FlyAsh 1 5070 115481 4879.1
## - BlastFurnace 1 10910 121322 4930.0
## - Cement 1 17396 127807 4983.6
## - Age 1 44760 155171 5183.4
##
## Step: AIC=4832.91
## Strength ~ Cement + BlastFurnace + FlyAsh + Water + Superplasticizer +
## CoarseAggregate + FineAggregate + Age
##
## Df Sum of Sq RSS AIC
## <none> 110413 4832.9
## - FineAggregate 1 385 110798 4834.5
## - CoarseAggregate 1 401 110814 4834.6
## - Superplasticizer 1 1058 111471 4840.7
## - Water 1 1506 111919 4844.9
## - FlyAsh 1 5281 115694 4879.0
## - BlastFurnace 1 11356 121769 4931.7
## - Cement 1 21539 131952 5014.5
## - Age 1 47902 158315 5202.1summary(StepAICModel)##
## Call:
## lm(formula = Strength ~ Cement + BlastFurnace + FlyAsh + Water +
## Superplasticizer + CoarseAggregate + FineAggregate + Age,
## data = Concrete.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.654 -6.302 0.703 6.569 34.450
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -23.331214 26.585504 -0.878 0.380372
## Cement 0.119804 0.008489 14.113 < 2e-16 ***
## BlastFurnace 0.103866 0.010136 10.247 < 2e-16 ***
## FlyAsh 0.087934 0.012583 6.988 5.02e-12 ***
## Water -0.149918 0.040177 -3.731 0.000201 ***
## Superplasticizer 0.292225 0.093424 3.128 0.001810 **
## CoarseAggregate 0.018086 0.009392 1.926 0.054425 .
## FineAggregate 0.020190 0.010702 1.887 0.059491 .
## Age 0.114222 0.005427 21.046 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.4 on 1021 degrees of freedom
## Multiple R-squared: 0.6155, Adjusted R-squared: 0.6125
## F-statistic: 204.3 on 8 and 1021 DF, p-value: < 2.2e-16Q5 -You have decided that you will only allow three input variables in the model to make it simple. Choose the most appropriate three input variables and justify your answer along with results Variable Importance
library(caret)
varImp(StepAICModel)## Overall
## Cement 14.112863
## BlastFurnace 10.247419
## FlyAsh 6.988168
## Water 3.731446
## Superplasticizer 3.127937
## CoarseAggregate 1.925656
## FineAggregate 1.886652
## Age 21.046426Taking the Most Important Three variables in the Model
Model4 <- lm(Strength ~ Cement
+ BlastFurnace
+ Age,
data = Concrete.df)
summary(Model4)##
## Call:
## lm(formula = Strength ~ Cement + BlastFurnace + Age, data = Concrete.df)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.830 -9.095 -0.614 8.418 40.630
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.921151 1.340214 2.18 0.0295 *
## Cement 0.088941 0.003980 22.34 <2e-16 ***
## BlastFurnace 0.058296 0.004810 12.12 <2e-16 ***
## Age 0.078438 0.006337 12.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.79 on 1026 degrees of freedom
## Multiple R-squared: 0.4151, Adjusted R-squared: 0.4134
## F-statistic: 242.7 on 3 and 1026 DF, p-value: < 2.2e-16