Statistical Programming With R and C

Paper Code: 
STT 601
Credits: 
3
Contact Hours: 
45.00
Max. Marks: 
100.00
Objective: 

This paper aims at teaching the students to solve the statistical problem using computer software and C language.

Course

Learning outcomes (at course level

Learning and teaching strategies

Assessment Strategies

 

Paper Code

Paper Title

STT-601

Statistical Programming with R and C

 

CO 60: Able to make the program in R and C.

 

CO 61: Apply programming concept to solve the statistical techniques.

 

CO 62: Able to learn the knowledge of statistical software for further enhancement in career.

Approach in teaching:

Interactive Lectures,

Group Discussion,

Classroom Assignment

Problem Solving Sessions

 

Learning activities for the students:

Assignments

Seminar

Presentation

Subject based  Activities

Classroom Quiz

Assignments

Class Test

Individual Presentation

 

9.00
Unit I: 
UNIT I

Overview of C, Basic structures of c programs, sample c program, programming style, exe.a c program, data types, constants and variables, operators and expression, managing input and output operators.

 

9.00
Unit II: 
UNIT II

Decision making and branching IF, IF ELSE, NESTED IF ELSE, IF ELSE ladder, SWITCH statement, ?: operator, GOTO statement, Decision making and looping: WHILE statement; DO statement, FOR statement, jumps in loops, Arrays: Introduction to arrays, single dimensional array and two dimensional arrays

 

9.00
Unit III: 
UNIT III

Introduction of R: History, data types, operators, basic structure of R, Functions for reading, writing and loading and interpretation data. Import and export of data Plot a graph: histograms, frequency polygon, pie chart, ogives, scatter diagrams.

 

 

9.00
Unit IV: 
UNIT IV

Control Structures: IF, IF ELSE,FOR, WHILE. Calculation of measure of central tendency and dispersion, correlation and lines of Regression. Random number generation and sampling procedures. Fitting of Population Distribution.

 

9.00
Unit V: 
UNIT V

Basics of statistical inference in order to understand hypothesis testing and compute p-values and confidence intervals solution of applications of sampling distributions and  Analysis of variance.

Essential Readings: 
  • Gardener, M (2012) Beginning R: The Statistical Programming Language, Wiley Publications.
  • Braun W J, Murdoch D J (2007): A First Course in Statistical Programming with R. Cambridge University Press. New York
  • Rakshit, Sandip(2007):R Programming for Beginners
  • Cotton, Richard(2016) Learning R: A Step-by-Step Function Guide to Data Analysis
  • Wickham, Hadley(2010): Elegant Graphics for Data Analysis (Use R!)

 

 

 

Academic Year: 
2021-2022
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