Introductory Probability and Statistics

This is an introductory course in probability and statistics. This course helps to serve as a foundation for higher levels of a statistics course, particularly inferential statistics and research methods course.

This course provides 85 video lectures and it also teaches you how to estimate the probability and do statistical analysis using spreadsheets.

The course is structured into 10 sections:

What is Statistics– Meaning of Statistics in Singular & Plural Sense, Characteristics of Stat, Nature & Scope, Types –Descriptive & Inferential, Distrust and other limitations of Statistics.

Descriptive Statistics– Measures of Central Tendency, Measures of Dispersion and Measures of Shape

Probability– Introduction to Probability, Fundamental Rules of Counting, Events & and Sample Space, Set & Venn Diagram, Approaches to Probability, Addition Rule, Multiplication Rule, The Law of Total Probability, Bayes’ Theorem.

Random Variable– Meaning, Discrete Random Variable, Continous Random Variable, Expected Value, Variance, Probability distributions– Binomial, Poisson, Normal Distribution

Sampling Distribution– Population & Sample, Parameters & Statistics, Sampling Distribution of Mean, Types of Sampling, Non–Probability Sampling, Theorems of Sampling Distribution 

Estimation –Estimator & Estimate, Qualities of a good estimator,  Point Estimate, Interval Estimate, the concept of standard error Confidence Interval construction, Sample size determination.

Hypothesis Testing– Introduction, Meaning of Null and Alternate Hypothesis, Two–tail & One–tail Tests, Types of Error, Hypothesis Testing Procedure, Hypothesis Test of a Population Mean: Large and Small Sample, Hypothesis Test of  Population Mean: Two Independent Samples, Hypothesis Test of a Population Mean: Paired t–test, Hypothesis Test of Two Population Variance: F–test.