Question 1 & 2: Develope the Model & Access Predictor
Significance
Step 1: Install and load required libraries
#installed.packages("Hmisc")
#installed.packages("pscl")
#if(!require(pROC)) install.packages("pROC")
library(readxl) #allows us to import excel files
library(Hmisc) #allows us to call the correlation function
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
##
## format.pval, units
library(pscl) #allows us ti cakk the pseudo R-square package to evaluate our model
## Classes and Methods for R originally developed in the
## Political Science Computational Laboratory
## Department of Political Science
## Stanford University (2002-2015),
## by and under the direction of Simon Jackman.
## hurdle and zeroinfl functions by Achim Zeileis.
library(pROC) #allows us to run the area under the curve(AUC) package to get the plot and AUC score
## Type 'citation("pROC")' for a citation.
##
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
##
## cov, smooth, var
Step 2: Import & Clean the data
college_df <- read_excel("Lakeland College Data.xlsx")
coll_df <- subset(college_df, select = -c(Student))
Step 3: Summarize the data
head(coll_df)
## # A tibble: 6 × 3
## GPA Program Return
## <dbl> <dbl> <dbl>
## 1 3.78 1 1
## 2 2.38 0 1
## 3 1.3 0 0
## 4 2.19 1 0
## 5 3.22 1 1
## 6 2.68 1 1
Data Description: A description of some of the features are presented in the
table below.
Variable | Definition
--------------|--------------
1. GPA | A students grade point average after the first year of college
2. Program | A student attending a voluntary one-week orientation (1:yes and 0:no)
3. Return | A student returning to college for their sophomore year(1:yes and 0:no)
summary(coll_df)
## GPA Program Return
## Min. :1.210 Min. :0.00 Min. :0.00
## 1st Qu.:2.377 1st Qu.:0.00 1st Qu.:0.00
## Median :2.735 Median :1.00 Median :1.00
## Mean :2.740 Mean :0.64 Mean :0.66
## 3rd Qu.:3.120 3rd Qu.:1.00 3rd Qu.:1.00
## Max. :4.000 Max. :1.00 Max. :1.00
Interpretation: The median GPA is 2.7, with a median of 1 meaning students
attended the orientation and returned for the sophomore year.
Step 4: Feature selection (i.e.,correlation analysis)
corr <- rcorr(as.matrix(coll_df))
corr
## GPA Program Return
## GPA 1.00 0.50 0.58
## Program 0.50 1.00 0.52
## Return 0.58 0.52 1.00
##
## n= 100
##
##
## P
## GPA Program Return
## GPA 0 0
## Program 0 0
## Return 0 0
Interpretation: All the predictors are significant with the target variable.
There's no multicollinearity in the data.
Step 5: Build the logistic regression model
model <- glm(Return ~ GPA + Program, data = coll_df, family = binomial)
summary(model)
##
## Call:
## glm(formula = Return ~ GPA + Program, family = binomial, data = coll_df)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.8926 1.7472 -3.945 7.98e-05 ***
## GPA 2.5388 0.6729 3.773 0.000161 ***
## Program 1.5608 0.5631 2.772 0.005579 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 128.207 on 99 degrees of freedom
## Residual deviance: 80.338 on 97 degrees of freedom
## AIC: 86.338
##
## Number of Fisher Scoring iterations: 5
Interpretation: All the independent variables were significant(p-value < 0.05)
Question 3: Overall Model Signnificance
Likelihood Ratio Test
# Fit a null model
null_model <- glm(Return ~ 1, data = coll_df, family = binomial)
# Perform likelihood ratio test
anova(null_model, model, test ="Chisq")
## Analysis of Deviance Table
##
## Model 1: Return ~ 1
## Model 2: Return ~ GPA + Program
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 99 128.207
## 2 97 80.338 2 47.869 4.03e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Interpretation: The inclusion of GPA and Program as predictictors in our LR model does indeed significantly predictthe likelihood of students returning to Lakeland College for their sophomore year, relative to a model that predicts based solely on the mean of observed outcomes.
Pseudo -R-squared
pR2(model)
## fitting null model for pseudo-r2
## llh llhNull G2 McFadden r2ML r2CU
## -40.1688662 -64.1035478 47.8693631 0.3733753 0.3804077 0.5264883
Interpretation: A McFadden R-squared of 0.373 means that our LR model explains about 37.3% of the variability in the outcome relative to a model with no predictors. This is considered a moderate to good fit, where values above 0.2 to 0.4 are often seen as indicative of a useful model.
Area Under the Curve(AUC)
# Compute ROC Curve and the AUC score
roc_curve <- roc(coll_df$Return, fitted(model))
## Setting levels: control = 0, case = 1
## Setting direction: controls < cases
plot(roc_curve)
auc(roc_curve)
## Area under the curve: 0.8841
Interpretation: An AUC score of 0.88 indicates that the LR model has a high level of accuracy in predicting student retention.
Question 6: Odds Ratio
# Extract the coefficients
model_summary <- summary(model)
coefficients <- model_summary$coefficients
# Calculate the odds ratio for 'Program'
odds_ratio_program <- exp(coefficients["Program", "Estimate"])
odds_ratio_program
## [1] 4.762413
Interpretation: The odd ratio is greater than 1 and this indicates that attending
the orientation program is associated with higher odds of students returning
for sophomore year compared to not attendiing the orientation at Lakeland college.