Statistical Computing using R (Skill Enhancement Course)

Paper Code: 
SSTT 401
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 

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

Course Outcomes: 

Course

Course Outcomes

Learning and teaching strategies

Assessment Strategies

Paper Code

Paper Title

SSTT 401/601

Statistical Computing using R

(Practical)

CO1: Analyze and apply methods of data input, utilizing built-in functions effectively to perform basic calculations and data manipulation tasks.

CO2: Evaluate and implement various graphical representations of data in R, to effectively visualize and communicate data.

CO3: Explain and apply techniques for the preparation of frequency tables and calculation of measures of central tendency.

CO4: Interpret and compute quartiles, deciles, percentiles, and measures of dispersion enabling effective analysis of the spread of data.

CO5: Apply the concepts of random experiments, discrete distributions, and continuous probability distribution in real-world scenarios.

CO6: Contribute effectively in course-specific interaction.

Approach in teaching:

 

Interactive Lectures,

Group Discussion,

Classroom Assignment

Problem Solving Sessions

 

Learning activities for the students:

 

Assignments

Seminar

Presentation

Subject based Activities

Software based Assignments,

Class Test,

Individual Presentation,

Record,

Viva-voice

 

12.00
Unit I: 
Foundations of R

Introduction to R, R as a calculator, Methods of data inputs, Data accessing or indexing, Useful built-in function (length, max, mean, sort, diff, which). 

 

12.00
Unit II: 
Visual Data Representation

Graphics with R, getting help, Diagrammatic representation of data (Bar chart, Subdivided bar diagram, multiple bar diagram, Pie chart), Graphical representation of data: -Stem and leaf plot, Histogram: for grouped and ungrouped data, Rod or spike graph, Frequency Polygon.

 

12.00
Unit III: 
Central tendency measures

Preparation of frequency table, Measures of central tendency: Mean, Median, Mode (for grouped and ungrouped data), Skewness, Kurtosis.

 

12.00
Unit IV: 
Dispersion

Quartiles, Deciles and percentiles, Box-plot, Measures of Dispersion: - Range, Inter-quartile range, Mean deviation, standard deviation, variance, Coefficient of variation. 

 

12.00
Unit V: 
Probability Fundamentals

Random experiment, Trials, Events, sample space, Discrete Distribution: Binomial, Poisson, Continuous probability distribution: Normal.

 

Essential Readings: 
  • Sudha, Sharad, Shailaja (2015): Statistics using R: Alpha Science International Ltd
  • R Programming An Approach to Data Analytics(2023): MJP Publishers
  • R for Data Science(2023): Hadley Wickham, Mine Çetinkaya-Rundel:Shroff/O’Reilly Pvt. Ltd

SUGGESTED READINGS:

  • Rakshit, Sandip(2007):R Programming for Beginners:Mc Graw Hill education publisher
  • Cotton, Richard(2013) Learning R: A Step-by-Step Function Guide to Data Analysis: Shroff/O'Reilly
  • Shubhilall(2003): Structured Programming and computer graphics, University book house pvt

 

e-RESOURCES:

 

JOURNALS:

  • Sankhya The Indian Journal of Statistics, Indian Statistical Institute
  • Aligarh Journal of Statistics, Department of Statistics and Operations Research, Aligarh Muslim University
  • Afrika Statistika, Saint-Louis Senega University
  • International Journal of Statistics and Reliability Engineering, Indian Association for Reliability and Statistic
  • Journal of the Indian Society for Probability and Statistics, Indian Society for Probability and Statistics
  • Journal of the Indian Statistical Association, Indian Statistical Association
  • Statistica, Department of Statistical Sciences Paolo Fortunato, University of Bologna
  • Statistics and Applications, Society of Statistics, Computer and Applications
  • Stochastic Modeling and Applications, MUK Publications and Distributions

 

 

Academic Year: 
2023-2024
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