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SEMESTER II

MDC2 – MULTI-DISCIPLINARY COURSE
KU2MDCSTA151: INTRODUCTION TO DATA ANALYSIS
SemesterCourse TypeCourse LevelCourse CodeCreditsTotal Hours
IIMULTI-DISCIPLINARY100 – 199KU2MDCSTA151345

Learning Approach (Hours/Week) / Marks Distribution
LecturePractical/InternshipTutorialCEESETotalDuration of ESE (Hours)
3--2550751½
Evaluation Type
Evaluation TypeMarks
End Semester Evaluation50
Continuous Evaluation25
a) Test Paper- 15
b) Test Paper-25
c) Assignment5
d) Seminar-
e) Book/ Article Review-
f) Viva-Voce-
g) Field Report/Practical10
Total75
Course Description

This course provides a comprehensive understanding of statistical measures including central tendency, dispersion, correlation analysis, and regression analysis with practical applications and examples.

Course Prerequisite

HSE level Mathematics/Statistics Courses

Course Outcomes
CO No.Expected OutcomeLearning Domains
1 Understand and calculate various measures of central tendency including the arithmetic mean, median, mode, and quartiles, deciles, and percentiles. U
2 Calculate and interpret measures of dispersion such as range, quartile deviation, mean deviation, standard deviation, and coefficient of variation to assess the spread of data. R
3 Analyse relationships between variables using correlation techniques including scatter diagrams, Karl Pearson’s correlation coefficient, and Spearman’s rank correlation coefficient. An
4 Apply regression analysis techniques to model relationships between variables, including understanding regression types, fitting regression lines, and interpreting regression coefficients. A
5 Evaluate and interpret statistical summaries obtained from measures of central tendency, dispersion, correlation, and regression analysis to draw meaningful conclusions from data. E

*Remember (R), Understand (U), Apply (A), Analyse (An), Evaluate (E), Create (C)

COURSE CONTENTS
Contents for Classroom Transaction
ModuleDescriptionHoursContents
1 Measures of Central Tendency 8
  1. Arithmetic mean
  2. Median
  3. Mode
  4. Quartiles, Deciles and Percentiles
2 Measures of Dispersion 8
  1. Range
  2. Quartile deviation
  3. Mean deviation
  4. Standard deviation and coefficient of variation
3 Correlation Analysis 7
  1. Definition and types of correlation
  2. Scatter Diagram
  3. Karl Pearson’s correlation coefficient
  4. Spearman’s rank correlation coefficient (without tie)
4 Regression Analysis 7
  1. Definition and types of regression
  2. Regression lines
  3. Fitting of regression equations, examples
  4. Properties of regression coefficients
5 Open End 15 Practical using MS Excel
Analysis of data using concepts explained in Module 1 to 4 by Ms Excel, Preparation and submission of a report.
Essential Readings
  1. Gupta S. C. and Kapoor, V. K. (2002): Fundamentals of Mathematical Statistics, Sultan Chand & Co.
  2. Gupta S. C. (2018): Fundamentals of Statistics, Himalaya Publishing House.
  3. B L Agrawal (2013): Basic Statistics, New Age International Publishers.
Suggested Readings
  1. Mood A. M., Gray bill F. A., Bose D. C. (2007): Introduction to the theory of statistics - Tata Magraw Hill.
  2. Goon A. M., Gupta M. K., Das Gupta. B. (1999): Fundamentals of Statistics, Vol. I, World Press, Calcutta.
Assessment Rubrics
Evaluation TypeMarks
End Semester Evaluation50
Continuous Evaluation25
a) Test Paper- 15
b) Test Paper-25
c) Assignment5
d) Seminar-
e) Book/ Article Review-
f) Viva-Voce-
g) Field Report/Practical10
Total75