This course covers topics in probability and statistics, including Chi-Square tests, testing for independence, applications of N-P-Lemma, testing equality of proportions, paired t-Test, estimation, descriptive statistics, F-Distribution, and various types of distributions. Students will learn how to analyze data, test hypotheses, and make statistical inferences. The course is designed for individuals interested in gaining a solid foundation in probability and statistics.
Overview
Syllabus
Chi-Square Test for Goodness Fit - I.
Testing for Independence in rxc Contingency Table - II.
Applications of N-P-Lemma - I.
Testing Equality of Proportions.
Testing for Independence in rxc Contingency Table - I.
Neyman- Pearson Fundamental Lemma.
Examples.
Two Types of Errors.
Paired t-Test.
Testing for Normal Variance.
Large Sample Test for Variance and Two Sample Problem.
Testing for Normal Mean.
Chi-Square Test for Goodness Fit - II.
Examples on MLE - I.
Examples on MME, MLE.
LSE, MME.
Introduction to Estimation.
Descriptive Statistics - IV.
Descriptive Statistics - III.
Descriptive Statistics - II.
Descriptive Statistics - I.
F-Distribution.
Chi - Square Distribution (Contd.)., t-Distribution.
Chi - Square Distribution.
Transformation of Random Variables.
Additive Properties of Distributions - II.
Additive Properties of Distributions - I.
Bivariate Normal Distribution - II.
Bivariate Normal Distribution - I.
Linearity property of Correlation and Examples.
Independence , product moments.
Joint Distributions - II.
Joint Distributions - I.
Function of a random variable - II.
Function of a random variable - I.
Problems on special distributions - II.
Problems on special distributions - I.
Problems on normal distribution.
Special continuous distributions - IV.
Special continuous distributions - III.
Special continuous distributions - V.
Normal distribution.
Special continuous distributions - II.
Taught by
Ch 30 NIOS: Gyanamrit
