Predictive Analytics Using R

Paper Code: 
MBB 324
Credits: 
4
Contact Hours: 
90.00
Max. Marks: 
100.00
Objective: 

Course outcome

Learning and

teaching strategies

Assessment

Strategies

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

CO 1: Install R and run commands and scripts in Rstudio environment for business analytics

CO 2: Apply descriptive and inferential statistics on business problems using R

CO 3: Generate charts and plots for analysis in R environment and interpret results.

Approach  in  teaching:

Interactive         Lectures,

Group                     Discussion, Tutorials, Case Study, Practical demonstration

 

Learning activities for the students: Self-learning assignments, presentations, exercises

Class              test,

Semester                     end examinations, Quiz, Practical Assignments, Presentation

CO 4: Design and Analyze regression model for different business problem using R.

 

 

CO 5: Evaluate the performance of regression model.

 

 

CO 6: Communicate the results in form of analysis report.

 

 

 

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
Unit II: 

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
Unit III: 

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
Unit IV: 

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
Unit V: 

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.

*Case studies related to entire topics are to be taught.

 

Essential Readings: 

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

 

References: 
E resources

 

Journals

 

Academic Year: 
2023-2024
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