What Will You Learn?

• Introduction to Probability: Basic concepts of probability theory, Probability rules and axiom, Combinatorics and counting principles and Conditional probability and independence
• Discrete Probability Distributions: Probability mass functions (PMFs) and cumulative distribution functions (CDFs), Binomial, geometric, and hypergeometric distributions, Poisson distribution and its applications and Expected value, variance, and standard deviation
• Continuous Probability Distributions: Probability density functions (PDFs) and cumulative distribution functions (CDFs), Uniform, exponential, normal (Gaussian), and gamma distributions, Central Limit Theorem and its significance and Calculating probabilities using standard normal tables and z-scores
• Statistical Inference: Sampling methods and sampling distributions, Point estimation and confidence intervals, Hypothesis testing and p-values and Type I and Type II errors, power, and sample size determination
• Regression and Correlation: Simple linear regression and multiple regression models, estimating regression coefficients and interpreting their significance, Residual analysis and assessing model fit and Correlation and its properties
• Experimental Design and Analysis of Variance (ANOVA): Principles of experimental design, One-way and two-way ANOVA, Post-hoc tests and multiple comparisons and Factorial designs and interactions