Predictive Analytics Using R

Paper Code: 
MBB 323
Credits: 
4
Contact Hours: 
90.00
Max. Marks: 
100.00
Objective: 

COURSE OUTCOMES (Cos)

Courseoutcome

Learningandteaching

strategies

AssessmentStrategies

  

On completion of this course, the students will be able to;

CO 120.        Install R and run commands and scripts in Rstudio environment for business analytics

CO 121.        Apply descriptive and inferential statistics on business problems using R

CO 122.         Generate charts and plots for analysis in R environment and interpret results.

CO 123.         Design and Analyze regression model for different business problem using R.

CO 124.         Evaluate the performance of regression model.

CO 125.         Communicate the results in form of analysis report.

Approach inteaching:Interactive Lectures,Group Discussion,Tutorials,CaseStudy,Practicaldemonstration

 

Learning activitiesfor the students:Self-learningassignments,presentations,exercises

Class test,Semester endexaminations,Quiz, PracticalAssignments,Presentation

  
 

 

18.00

Introduction to R Programming

R and R Studio, Logical Arguments, Missing Values, Characters, Factors and Numeric, Help in R, Vector to Matrix, Matrix Access, Data Frames, Data Frame Access, Basic Data Manipulation Techniques, Usage of various apply functions – apply, lapply, sapply and tapply, Outliers treatment.

 

18.00

Descriptive Statistics

Measures of Central Tendency (Mean, Mode and Median), Charts (Bar, Pie and Box Plot, Histogram, Stem and Leaf Diagram), Measures of dispersion (Range, Inter-Quartile-Range, Standard Deviation, Skewness and Kurtosis), Standard Error of Mean and Confidence Intervals.

Discrete Probability Distributions: Binomial, Poisson, Continuous Probability Distribution, Normal Distribution & t-distribution, Sampling Distribution and Central Li               

18.00

Statistical Inference and Hypothesis Testing

Parametric and non-parametric tests (one sample, independent sample, paired sample and two and more then two samples)

18.00

Correlation and Regression

Analysis of Relationship, Positive and Negative Correlation, Perfect Correlation, Correlation Matrix, Scatter Plots, Simple Linear Regression, R Square, Adjusted R Square, Testing of Slope, Standard Error of Estimate, Overall Model Fitness, Assumptions of Linear Regression, Multiple Regression, Coefficients of Partial Determination, Durbin Watson Statistics, Variance Inflation Factor.

 

 

18.00

Logistic Regression

Binary Classification versus Point Estimation, Odds versus Probability, Logit Function, Classification Matrix, Individual Group Classification Efficiency, Overall Classification Efficiency, Nagelkerke R Square, Receiver Operating Characteristic Curve, Sensitivity, Specificity, Area Under ROC Curve, Cut-Offs, True Positive Rate and False Positive Rate.

*Casestudiesrelated toentiretopicsaretobe taught.

 

Essential Readings: 
  • Maindonald,John,Braun john ,”Data Analysis and Graphics Using R”, Cambridge University Press,2013
  • Gardener Mark,”Beginning R: The Statistical Programming Language “ Wiley India Pvt. Ltd. 2015
  • Srivasa K.G., Siddesh G M,Shetty,” Statistical Programming in R”, Oxford University Press 2017
  • Business Statistics: Naval Bajpai, Pearson
  • Menard, S. (2002). Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage.

 

References: 

Suggested readings

  • Menard, S. (2002). Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage.

E resources

Journals

https://journals.sagepub.com/home/hrm

 

 

Academic Year: 
2022-2023
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