Advanced Statistical Methods

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 Plan

Session 1

Population, Sample, Sampling, Estimators and Estimates
Web Link
https://www.youtube.com/watch?v=yx5KZi5QArQ

PPT1(Parameter Estimation)

Session 2

Maximum likelihood, Confidence Intervals
Web Link
https://www.youtube.com/watch?v=tFWsuO9f74o

PPT2(Maximum Likelihood method )

Session 3

Project-1
Application of Confidence intervals as a tool in Confidence Interval decision making

https://www.coursera.org/lecture/hypothesis-testing-confidence-intervals/application-of-confidence-interval-4UgKo

Session 4

Project-1

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

Web Link

PPT3Confidence Interval

Session 5

Null and the alternative hypothesis
Web Link
https://www.youtube.com/watch?v=ZzeXCKd5a18

Session 6

Rejection region and significance level
Web Link
https://www.youtube.com/watch?v=BdeuCflLPQI

Session 7

Chi-Square Test
Web Link
https://www.youtube.com/watch?v=2SKXRB-bJKo

Session 8

Project-2

Hypothesis Testing in Quality Management

Web Link

Hypothesis Testing in Quality Management

Session 9

Project-2

Hypothesis Testing in Quality Management

Web Link

https://www.youtube.com/watch?v=J9HPugGlcRI.

Session 10

Multiple Regression
Web Link
https://www.youtube.com/watch?v=zITIFTsivN8

PPT4(Regression Analysis)

Session 11

Multiple Regression
Web Link

https://www.youtube.com/watch?v=K_EH2abOp00.

PPT5( Regression and Correlation Analysis )

Session 12

Logistic Regression
Web Link
https://www.youtube.com/watch?v=yIYKR4sgzI8

Session 13

Logistic Regression
Web Link

https://www.youtube.com/watch?v=yFKVI7vgPPs.

Session 14

Practice-1
Multiple Regression Analysis in Python
Web Link
https://www.youtube.com/watch?v=M32ghIt1c88

Session 15

Practice-1
Multiple Regression Analysis in Python

Web Link

https://www.youtube.com/watch?v=fJOhLKvWOJI.

Session 16

Practice-2
Logistic Regression using Python
Web Link
https://www.youtube.com/watch?v=VCJdg7YBbAQ

Session 17

Practice-2
Logistic Regression using Python
Web Link

https://www.youtube.com/watch?v=VK6v9Ure8Lk.

Session 18

F- Distribution
Web Link
https://www.youtube.com/watch?v=w1TvaQgoNCY

Session 19

One way ANOVA
Web Link
https://www.youtube.com/watch?v=rMnlVIkdukU

Session 20

Two Way ANOVA
Web Link
https://www.youtube.com/watch?v=lZFmFuZGQTk

Session 21

Practice-3

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

Session 22

Practice-3

One way ANOVA using Python
Web Link

https://www.youtube.com/watch?v=AhZ-hllEVxs.

Session 23

Practice-4

Two way ANOVA using Python
Web Links
https://www.youtube.com/watch?v=T4IgVnmNH5A
https://raphaelvallat.com/pingouin.html

Session 24

Practice-4

Two way ANOVA using Python
Web Links

https://www.youtube.com/watch?v=vvy_YRiEvpA.

Session 25

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

Web Link

https://www.youtube.com/watch?v=LtdvDrZA4r0.

PPT6(Uses of regression and correlation analysis in bussines )

Session  26

Application of Analysis of covariance (ANCOVA) in psychological research

Web Link

https://www.youtube.com/watch?v=_uYASFVUNpQ.

Session 27

Analysis of Covariance (ANCOVA)

Web Link
https://www.youtube.com/watch?v=rpe4kPGteCQ

Session 28

Bivariate Pearson Correlation
Web Link
https://www.youtube.com/watch?v=cagmwv4rMno

PPT7(SIMPLE LINEAR CORRELATION)

Session  29

Alternative Correlation Coefficients
Web Link
https://www.youtube.com/watch?v=jmE1rfDjDGQ

Session 30

Practice-5

Python Analysis of covariance (ANCOVA)

Web Link

https://www.youtube.com/watch?v=Kx6QhjGcjA0

Session 31

Python Analysis of covariance (ANCOVA)

Web Link

https://www.youtube.com/watch?v=AhZ-hllEVxs.

Session 32

Project-4

Application of Analysis of covariance (ANCOVA) in psychological research

Web Link

https://www.youtube.com/watch?v=a61mkzQRf6c.

Session 33

Application of Analysis of covariance (ANCOVA) in psychological research

Web Link

https://www.youtube.com/watch?v=LtdvDrZA4r0.

Session 34

One-way MANOVA
Web Link
https://www.youtube.com/watch?v=HdAwASZ7bNY

Session 35

Two-way MANOVA
Web Link
https://www.youtube.com/watch?v=57do6ZVMp6Y

Session 36

Two-way MANOVA
Web Link

https://www.youtube.com/watch?v=qM4-MrWeUnQ.

Session 37

Project-5
Comparison of MANOVA to ANOVA Using an Example

Web Link

https://www.youtube.com/watch?v=jUksjmKvwos.

Session 38

Project-5
Comparison of MANOVA to ANOVA Using an Example

Web Link

https://www.youtube.com/watch?v=Q116ZnLy5uA.

Session 39

Introducing Time Series Analysis
Web Link
https://www.youtube.com/watch?v=GUq_tO2BjaU

Session 40

Components of Time Series Analysis
Web Link
https://www.youtube.com/watch?v=e8Yw4alG16Q

Session 41

Multivariate Time Series Analysis
Web Link
https://www.youtube.com/watch?v=3dvzReXBUMs

Session 42

Practice-6
Time Series Analysis using Pandas
Web Link
https://www.youtube.com/watch?v=r0s4slGHwzE

Session 43

Practice-6
Time Series Analysis using Pandas
Web Link

https://www.youtube.com/watch?v=0unf-C-pBYE.

Session 44

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

Web Link

https://www.youtube.com/watch?v=wGUV_XqchbE.

Session 45

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

Web Link

https://www.youtube.com/watch?v=m4b5yYx8oWw.