Statistical Analysis for Business
( 24 Modules )

Module #1
Introduction to Statistical Analysis
Overview of statistical analysis, importance in business decision-making, and course objectives

Module #2
Descriptive Statistics
Measures of central tendency, variability, and data visualization techniques

Module #3
Data Collection and Sampling
Types of data, data collection methods, and sampling techniques

Module #4
Data Preparation and Cleaning
Handling missing values, outliers, and data transformation techniques

Module #5
Probability Theory
Basic concepts of probability, rules of probability, and conditional probability

Module #6
Discrete Probability Distributions
Binomial, Poisson, and Hypergeometric distributions

Module #7
Continuous Probability Distributions
Normal, Uniform, and Exponential distributions

Module #8
Confidence Intervals
Constructing and interpreting confidence intervals for means and proportions

Module #9
Hypothesis Testing
Formulating hypotheses, types of errors, and testing hypotheses

Module #10
Inferential Statistics for Means
Hypothesis testing and confidence intervals for means

Module #11
Inferential Statistics for Proportions
Hypothesis testing and confidence intervals for proportions

Module #12
Chi-Square Tests
Goodness-of-fit, independence, and homogeneity tests

Module #13
Regression Analysis
Simple and multiple regression, coefficient interpretation, and assumptions

Module #14
Time Series Analysis
Components of time series, trend analysis, and forecasting techniques

Module #15
Forecasting Methods
Moving average, exponential smoothing, and ARIMA models

Module #16
Non-Parametric Tests
Wilcoxon signed-rank test, Mann-Whitney U test, and Kruskal-Wallis test

Module #17
ANOVA and ANCOVA
One-way ANOVA, two-way ANOVA, and analysis of covariance

Module #18
Cluster Analysis
Hierarchical and non-hierarchical clustering, and cluster validation

Module #19
Decision Trees and Random Forests
Decision tree construction, random forests, and variable importance

Module #20
Statistical Process Control
Control charts, process capability, and six sigma methodology

Module #21
Survival Analysis
Kaplan-Meier estimator, hazard function, and Cox proportional hazards model

Module #22
Big Data Analytics
Overview of big data, Hadoop, and Spark, and statistical analysis with big data

Module #23
Statistical Software and Tools
Overview of popular statistical software and tools, including R, Python, and Excel

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Statistical Analysis for Business career

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