Coming soon
Statistical Computing using R (Skill Enhancement Course)
This paper aims at teaching the students to solve the statistical problem using computer software and C language.
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Course |
Course Outcomes |
Learning and teaching strategies |
Assessment Strategies |
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Course Code |
Course Title |
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25SSTT401 |
Statistical Computing using R (Practical) |
CO 1: Identify and collect relevant data from various sources, including databases, surveys, and web scraping techniques. CO 2: Apply statistical tools to analyze data and address questions. CO 3: Interpret the results of statistical analyses accurately and effectively and develop the ability to draw meaningful conclusions. CO 4: Demonstrate the ability to communicate statistical concepts, methodologies, and findings clearly and concisely both orally and in writing. CO 5: 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, Record, Viva-voice |
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, Summary).
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.
Preparation of frequency table, measures of central tendency: Mean, Median, Mode (for grouped and ungrouped data), Skewness, Kurtosis.
Quartiles, Deciles and percentiles, Box-plot, Measures of Dispersion: - Range, Inter-quartile range, Mean deviation, standard deviation, variance, Coefficient of variation.
Testing of hypothesis: Chi-Square, t and F.
- Sudha, Sharad, Shailaja(2015): Statistics using R
- Robert L. Kabacoff: R in Action
- Hadley Wickham and Garrett Gorlemund: R for Data Science
SUGGESTED READINGS:
- 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!)
- 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
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Scheme of Evaluation for Continuous Assessment Practical (30 %) Time Duration: 60 minutes |
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Students are required to attempt 2 questions Out of 3 questions. |
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Experiment 1 |
Experiment 1 |
Practical Record |
Viva Voce |
Attendance |
Total |
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5 |
5 |
10 |
05 |
05 |
30 |
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Scheme of Evaluation for Semester End Examination Practical (70 %) Time Duration: 3 hrs. |
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Students are required to attempt 3 questions Out of 5 questions. |
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Experiment 1 |
Experiment 2 |
Experiment 3 |
VIVA VOICE |
TOTAL |
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20 |
20 |
20 |
10 |
70 |
Note: Scientific calculators are allowed in all papers of continuous assessment and semester end exam.
