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Predictive Analytics Using R
Course Objectives: The objective of this course is to enable students to - Course Outcomes (COs): Course Course outcome (at course level) Learning and teaching strategies Assessment Strategies Paper Code Paper Title CLO 117.Install R and run commands and scripts in Rstudio environment for business analytics CLO 118.Apply descriptive and inferential statistics on business problems using R CLO 119.Generate charts and plots for analysis in R environment and interpret results. CLO 120.Design and Analyze regression model for different business problem using R. CLO 121.Evaluate the performance of regression model. CLO 122.Communicate the results in form of analysis report. Approach in teaching: Interactive Lectures, Group Discussion, Tutorials, Case Study, Practical demonstration Learning activities for the students: Self-learning assignments, presentations, R exercises Class test, Semester end examinations, Quiz, Practical Assignments, Presentation
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.
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
Statistical Inference and Hypothesis Testing Parametric and non parametric tests (one sample, independent sample, paired sample and two and more then two samples)
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.
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.
