Applied Statistics

Course description

This course designed to design experiments and carry out statistical analysis of data. Emphasis is given to the development of statistical concepts and statistical computing. Key topics covered include hypothesis testing, simple and multiple linear regression, logistic regression for binary variables and binomial counts, log-linear regression for Poisson counts, and exploratory tools for summarizing multivariate responses, model choice, experimental design, including two-level factorial design, analysis of variance, process control, contingency tables and non-parametric methods. Demonstration of software application is also included in the course.

Course outcomes

Upon the completion of this course, students will be able to:

• Appreciate and understand the role of statistics
• Develop an ability to apply appropriate statistical methods to summarize and analyze data for some of the more routine experimental settings
• Report the results in appropriate tables or statistical terms
• Perform appropriate statistical techniques
• Interpret results from various statistical software packages

Course contents

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🔽 1 h 13 min | Special Probability Distributions and Densities

Concept of probability and their application
Continuous and discrete probability distribution
Normal distribution and binomial distribution
Probabilities of events

🔽 1 h 05 min | Simple Regression

Concept of estimation of population parameters
Correlation versus regression
Simple and multiple linear regression analysis
Interpret regression coefficients
Goodness of fit of the model

🔽 1 h 14 min | Multiple Regression

Simple and multiple linear regressions
Multiple regression model
Ordinary Least Square (OLS) method
Goodness of fit of the regression model
Functional form of a regression model

🔽 1 h 48 min | Regression Analysis with Limited Dependent Variables

Economic decisions
Probit and logit models
Multinomial logit/probit models
Ordered logit/probit regression models

🔽 1 h 18 min | Hypothesis Testing

The null hypothesis and alternative hypothesis
Outcome of a hypothesis test (p-value)
One-tail and two-tail tests
Type-I and II errors and their practical importance in hypothesis testing
Analysis of Variance (ANOVA)

🔽 1 h 03 min | Experimental Design

Principles of experimental design
Classical treatments of design
The concept of complete randomized design, randomized block design and hierarchical design
Experimental error