**Statistics with R Programming**

## OBJECTIVE:

After taking the course, students will be able to

ïƒ˜ Use R for statistical programming, computation, graphics, and modeling, ïƒ˜ Write functions and use R in an efficient way,

ïƒ˜ Fit some basic types of statistical models

ïƒ˜ Use R in their own research,

ïƒ˜ Be able to expand their knowledge of R on their own.

### UNIT-I:

Introduction, How to run R, R Sessions and Functions, Basic Math, Variables, Data Types, Vectors, Conclusion, Advanced Data Structures, Data Frames, Lists, Matrices, Arrays, Classes.

### UNIT-II:

R Programming Structures, Control Statements, Loops, – Looping Over Nonvector Sets,- If-Else, Arithmetic and Boolean Operators and values, Default Values for Argument, Return Values, Deciding Whether to explicitly call return- Returning Complex Objects, Functions are Objective, No Pointers in R, Recursion, A Quicksort Implementation-Extended Extended Example: A Binary Search Tree.

### UNIT-III:

Doing Math and Simulation in R, Math Function, Extended Example Calculating ProbabilityCumulative Sums and Products-Minima and Maxima- Calculus, Functions Fir Statistical Distribution, Sorting, Linear Algebra Operation on Vectors and Matrices, Extended Example: Vector cross Product- Extended Example: Finding Stationary Distribution of Markov Chains, Set Operation, Input /out put, Accessing the Keyboard and Monitor, Reading and writer Files,

### UNIT-IV:

Graphics, Creating Graphs, The Workhorse of R Base Graphics, the plot() Function â€“ Customizing Graphs, Saving Graphs to Files. UNIT-V: Probability Distributions, Normal Distribution- Binomial Distribution- Poisson Distributions Other Distribution, Basic Statistics, Correlation and Covariance, T-Tests,-ANOVA.

### UNIT-VI:

Linear Models, Simple Linear Regression, -Multiple Regression Generalized Linear Models, Logistic Regression, – Poisson Regression- other Generalized Linear Models-Survival Analysis, Nonlinear Models, Splines- Decision- Random Forests,

### OUTCOMES:

At the end of this course, students will be able to:

ïƒ˜ List motivation for learning a programming language

ïƒ˜ Access online resources for R and import new function packages into the R workspace

ïƒ˜ Import, review, manipulate and summarize data-sets in R

ïƒ˜ Explore data-sets to create testable hypotheses and identify appropriate statistical tests

ïƒ˜ Perform appropriate statistical tests using R Create and edit visualizations with

### TEXT BOOKS:

1) The Art of R Programming, A K Verma, Cengage Learning.

2) R for Everyone, Lander, Pearson

3) The Art of R Programming, Norman Matloff, No starch Press. REFERENCE

### BOOKS:

1) R Cookbook, Paul Teetor, Oreilly.

2) R in Action, Rob Kabacoff, Manning

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