Course Name: CSE1005/CS1005L: Applied Statistical Analysis
L(Lecture) T(Tutorial) P(Practical) C(Credits): 2 0 2 3
Course Prerequisite:
The course enables students to
1. Learn how to analyse statistical data properly.
2. Understand the role of formal statistical theory and informal data analytic methods.
COURSE OUTCOMES
On completion of this course, the students will be able to
1. Choose a suitable statistical analysis technique for a given scenario/ problem
2. Able to draw meaningful inferences from the statistical analysis.
Unit 1 Introduction to Statistical Analysis
Introduction, Meaning of Statistics, The Scientific Method, Basic Steps of the Research Process, Experimental Data and Survey Data, Populations and Samples, Census and Sampling Method, Parameter and Statistic, Independent and Dependent Variables, Examining Relationships, Introduction to SPSS Statistics.
Unit 2 Describing Data
Introduction, Types of Data, Data Transformation, Summarizing Data: Graphical Methods, Summarizing Data: Measures of Central Tendency, Summarizing Data: Measures of Dispersion, Levels of Measurement, Random Variables and Probability Distributions, Discrete and Continuous Random Variable, Making Inferences about Populations from samples, Estimator and Estimate, Confidence Interval for Population Mean (Large Sample).
Unit 3 Testing Hypothesis
Introduction, Null and Alternative Hypothesis, Type I and Type II Error, The Procedure of Hepothesis Testing, Hypothesis Testing of a Population Mean: Large Sample, Hypothesis Testing of a Population Mean: Small Sample, Hypothesis Test of a Proportion (One Sample), Hypothesis Test of Population Variance, Hypothesis Test of Population Mean: Two Independent Samples(), Hypothesis Test of Population Mean: Dependent Samples (Paired Samples), Hypothesis Test about Two Population Proportion, Hypothesis Teest about Two Population Variances, Analysis of Variance (ANOVA), Nonparametric Test, Sign Test for Paired Data, Wilcoxon Matched Pairs Signed Ranks Test (for n>10 pairs), Mann-Whitney U Test, Kruskal-wallis Tests (H Test).
Unit 4 Examining Relationships
Introduction, Types of Correlation, Karl Pearson Coefficient Correlation, Spearman’s Rank Order Correlation, Partial Correlation, Residuals and Plots, Simple Linear Regression, Multiple Regression Model, Repeated Measures, Non-linear Regression, Polynomial Regression Models, Weighted Least
Squares, Two Stage Least Squares 1, Structural Equation Modeling.
Unit 5 Advanced Techniques
Identifying Groups: Classification, Probit Analysis, Discriminant Function Analysis, Proportional Odds Models, Decision Trees, Neural Networks, Cluster Analysis, Factor Analysis, Multidimensional Scaling.
Text Books
Applied Statistical Analysis (IBM ICE Publication)
References:
Business Statistics: For Contemporary Decision Making, 8th Edition, Author: Ken Black, Wiley
Beginning R: The Statistical Programming Language, Author: Mark Gardener, Wrox Publication
Practical Data Science with R, Author: Nina Zumel, John Mount, Jim Porzak, Dreamtech
Quizzes, Assignments, Seminar/Presentation, Written Examinations
MSE I – First Mid Semester Examination
MSE II – Second Mid Semester Examination
ESE – End Semester Examination
ESE- End Semester Examination