Syllabus
The complete module-and-topic map of the book. Click any module to open its overview, or jump straight to a numbered topic.
Five-Module Path · 60 Topics
R Basics
Python Essentials
Introduction to Business Analytics
Data Collection and Preparation
13Data Collection Methods
14Sampling Techniques and Sample Size Determination
15Data Cleaning and Preparation
Descriptive Analytics
16Introduction to Descriptive Analytics
17Measures of Central Tendency
18Measures of Dispersion
19Measures of Skewness
20Measures of Kurtosis
Data Visualization Using R Graphics and R Commander / R Deducer
Probability and Estimation
27Introduction to Probability
28Probability Distributions
29Sampling Distributions and the Central Limit Theorem
30Confidence Intervals and Estimation
Test Selection Framework
31Introduction to Diagnostic Analytics
32Parametric Vs Non-Parametric Tests
33Choose your Test for Data Analysis
Nominal Tests
34Introduction to Nominal Tests
35Binomial Test
36Mc Nemar's Test
37Cochran's Q test-post-hoc test
38Chi-square test
39Phi-Coefficient of Correlation
Scale Tests (Parametric Tests)
40Introduction to Parametric Tests
41T-tests
42One-Sample T-Test
43Two-Sample T-Test (Independent Samples)
44Paired-Samples T-Test
45ANOVA
46One-Way ANOVA
47Two-Way ANOVA
48Post-Hoc Tests for ANOVA
49Repeated Measures ANOVA
50Karl Pearson's Coefficient of Correlation
Ordinal Tests (Non-parametric Tests)
Regression Analysis