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Code(Credit) :CUTM1533(2-1-1)

## Course Objectives

• Ability to summarize and present data numerically and visually.
• Knowledge of which statistical methods to use in which situations
• Ability to think critically about data-based claims and quantitative arguments
• Ability to learn new statistical analysis techniques on your own

## Learning Outcomes

Upon successful completion of this course, students will be able to:
• Apply statistical methods and hypothesis testing to business problems
• Learn the details and complexities of Analysis of Variance (ANOVA)
• Learn some of the details and complexities of Multiple Regression (MR)

## Course Syllabus

Module I: (2 hrs+0 hrs+2hr)
Statistics: Population, Sample, Sampling, Estimators and Estimates, Maximum Likelihood , Confidence Intervals
Project-1
Application of Confidence intervals as a tool in decision making

Module II: (3 hrs+0hr+2hr)
Hypothesis Testing: Null and the alternative hypothesis, Rejection region and significance level, Chi-Square Test

Project-2

Hypothesis Testing in Quality Management

Module III: (4 hrs+4 hrs+0hr)
Regression: Multiple Regression and Logistic Regression
Practice-1
Multiple Regression Analysis in Python
Practice-2
Logistic Regression using Python

Module IV: (3 hrs+4 hrs+2hr)
Analysis of Variance(ANOVA): F- Distribution, One way ANOVA, Two Way ANOVA
Practice-3
One way ANOVA using Python
Practice-4
Two way ANOVA using Python
Project-3

The utility of multivariate statistical techniques in hydro geochemical studies

Module V: (3 hrs+2 hrs+2hr)
Covariance: (ANCOVA): Analysis of Covariance (ANCOVA), Bivariate Pearson Correlation, Alternative Correlation Coefficients
Practice-5

Python Analysis of covariance (ANCOVA)

Project-4

Application of Analysis of covariance (ANCOVA) in psychological research

Module VI: (3 hrs+0hr+2hr)
Multivariate analysis of variance (MANOVA): One-way MANOVA, Two-way MANOVA
Project-5
Comparison of MANOVA to ANOVA Using an Example

Module VII: (3 hrs+2 hrs+2hr)
Time Series Analysis: Introducing Time Series Analysis, Components of Time Series Analysis, Multivariate Time Series Analysis
Practice-6
Time Series Analysis using Python
Project-6
A Report on Applications of Time Series Analysis in Census Analysis

Text Books:
1. Statistical Methods By S.P. Gupta (31st Edition) ; Publisher: Sultan Chand & Sons
2. Mathematical Statistics by S.C. Gupta & V.K. Kapur (10th Edition); Publisher: Sultan Chand & Sons.

Reference Books:
Understanding And Using Advanced Statistics by Jeremy Foster Emma Barkus Christian Yavorsky, SAGE Publications

Course outline Prepared by: Dr.Banitamani Mallik
Date: 18-06-2020
Source of reference: udemy, coursera, Harvard University

Note: 1 credit theory=10 hrs lecture, 1 credit practice/project=12.5 hrs lab/workshop/field work in a semester.

## Session 1

Population, Sample, Sampling, Estimators and Estimates

PPT1(Parameter Estimation)

## Session 2

Maximum likelihood, Confidence Intervals

PPT2(Maximum Likelihood method )

## Session 4

Project-1

Application of Confidence intervals as a tool in Confidence Interval decision making

PPT3Confidence Interval

## Session 5

Null and the alternative hypothesis

## Session 6

Rejection region and significance level

Chi-Square Test

## Session 8

Project-2

Hypothesis Testing in Quality Management

Hypothesis Testing in Quality Management

## Session 9

Project-2

Hypothesis Testing in Quality Management

## Session 10

Multiple Regression

PPT4(Regression Analysis)

Session 11

Multiple Regression

PPT5( Regression and Correlation Analysis )

## Session 12

Logistic Regression

## Session 13

Logistic Regression

## Session 14

Practice-1
Multiple Regression Analysis in Python

## Session 15

Practice-1
Multiple Regression Analysis in Python

## Session 16

Practice-2
Logistic Regression using Python

## Session 17

Practice-2
Logistic Regression using Python

F- Distribution

One way ANOVA

Two Way ANOVA

## Session 21

Practice-3

One way ANOVA using Python
https://reneshbedre.github.io/blog/anova.html

## Session 22

Practice-3

One way ANOVA using Python

## Session 23

Practice-4

Two way ANOVA using Python
https://raphaelvallat.com/pingouin.html

## Session 24

Practice-4

Two way ANOVA using Python

## Session 25

Project-3
Application of Analysis of covariance (ANCOVA) in psychological research

PPT6(Uses of regression and correlation analysis in bussines )

## Session  26

Application of Analysis of covariance (ANCOVA) in psychological research

## Session 27

Analysis of Covariance (ANCOVA)

## Session 28

Bivariate Pearson Correlation

PPT7(SIMPLE LINEAR CORRELATION)

## Session  29

Alternative Correlation Coefficients

## Session 30

Practice-5

Python Analysis of covariance (ANCOVA)

## Session 31

Python Analysis of covariance (ANCOVA)

## Session 32

Project-4

Application of Analysis of covariance (ANCOVA) in psychological research

## Session 33

Application of Analysis of covariance (ANCOVA) in psychological research

One-way MANOVA

Two-way MANOVA

Two-way MANOVA

## Session 37

Project-5
Comparison of MANOVA to ANOVA Using an Example

## Session 38

Project-5
Comparison of MANOVA to ANOVA Using an Example

## Session 39

Introducing Time Series Analysis

## Session 40

Components of Time Series Analysis

## Session 41

Multivariate Time Series Analysis

## Session 42

Practice-6
Time Series Analysis using Pandas

## Session 43

Practice-6
Time Series Analysis using Pandas

## Session 44

Project-6
A Report on Applications of Time Series Analysis in Different Real Life Situations

## Session 45

Project-6
A Report on Applications of Time Series Analysis in Different Real Life Situations