This chapter introduces the core engine of statistical decision-making: Hypothesis Testing. It provides a rigorous framework for making inferences about populations based on sample evidence, a critical skill for any Data Scientist. Core Topics covered: The Logic of Hypothesis Testing One-Sample Tests Two-Sample Tests Paired Sample Test One-Way ANOVA Non-Parametric Alternatives Effect Size and Statistical Power Chapter Lab Activity: Exploring Hypothesis Testing with the ToothGrowth Dataset

This chapter serves as the theoretical bridge between descriptive analysis and statistical inference. It introduces the fundamental concepts of probability and explores the mathematical distributions that model real-world data behavior. Core Topics covered: Types of Data and Measurement Scales Probability Fundamentals Conditional Probability and Bayes’ Theorem Discrete Probability Distributions Continuous Probability Distributions Sampling Distributions and the Central Limit Theorem Assessing Normality Chapter Lab Activity: Exploring Distributions with the airquality Dataset

This document serves as the introductory chapter for the Statistics for Data Science course at the graduate level. It focuses on the fundamental principles of Exploratory Data Analysis (EDA), shifting the focus from simple computation to critical statistical interpretation . Topics covered: Measures of Central Tendency Measures of Dispersion Measures of Shape: Skewness and Kurtosis Data Visualization for Descriptive Statistics Multivariate Descriptive Statistics Chapter Lab Activity: Exploring the mtcars Dataset

Students are able to 1)perform descriptive statistics 2)apply appropriate non-parametric statistics tests to answer research questions of interest.

Students are able to 1)perform descriptive statistics 2)apply appropriate non-parametric statistics tests to answer reseach questions of interest.

Students are able to use R language to analyse data using multiple linear regression: 1. Perform linear regression analysis 2. Check Normality Assumptions 3. Check Constant Variance Assumptions 4. Check Independence (Autocorrelation) Assumptions 5. Dealing with Invalid Model Assumption

Students are able to use R language to 1. perform descriptive statistics 2. construct scatterplot between two quantitative variables 3. perform correlation analysis 4. perform linear regression analysis and inference on regression parameters 5. interpret the results

Students are able to use R language to analyse data using multiple linear regression: 1. perform descriptive statistsics 2. transform qualitative independent variable into dummy variables 3. select independent variables 4. perform linear regression analysis and inference on regression parameters 5. interpret the results