Predictive Analysis using R

Paper Code: 
BAC 332
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

This paper will enable students to exercise Multivariate Techniques in R environment in different Business Cases. They will know the different techniques covered under the scope of Multivariate Analysis and will be able to apply and build select Predictive Models in the context of Binary Classification and Time Series.

12.00
Unit I: 
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.

12.00
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 Limit Theorem.

 

12.00
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)

12.00
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.

12.00
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.

Essential Readings: 
  1. Maindonald,John,Braun john ,”Data Analysis and Graphics Using R”, Cambridge University Press,2007
  2. Gardener Mark,”Beginning R: The Statistical Programming Language “ Wiley India Pvt. Ltd. 2015
  3. Srivasa K.G., Siddesh G M,Shetty,” Statistical Programming in R”, Oxford University Press 2017
  4. Business Statistics: Naval Bajpai, Pearson
  5. Menard, S. (2002). Applied Logistic Regression Analysis. Thousand Oaks, CA: Sage.
Academic Year: 
2019-2020
  • Printer-friendly version
  • PDF version