# Introduction to Statistics Course Syllabus — Curriculum & Modules | Course

> Detailed syllabus and module breakdown for Introduction to Statistics Course. See what you'll learn, estimated hours per module, prerequisites, and outcomes.

# Introduction to Statistics Course Syllabus

Full curriculum breakdown — modules, lessons, estimated time, and outcomes.

Overview: This beginner-friendly course from Stanford introduces core statistical concepts used in data analysis and decision-making. Designed for learners with no advanced math background, it covers descriptive and inferential statistics, probability, regression, and resampling methods. The course is self-paced with approximately 35–40 hours of content, structured across 7 modules. Each module includes video lectures, practical quizzes, and hands-on exercises to reinforce understanding and real-world application.

### Module 1: Descriptive Statistics & Data Visualization

Estimated time: 5 hours

- Identify types of data and variables

- Create and interpret graphical representations (histograms, box plots)

- Calculate measures of central tendency (mean, median, mode)

- Compute measures of spread (variance, standard deviation)

- Summarize datasets using numerical and visual techniques

### Module 2: Producing and Sampling Data

Estimated time: 5 hours

- Design surveys and experiments

- Apply random and stratified sampling methods

- Recognize sources of bias in data collection

- Distinguish between observational studies and randomized experiments

### Module 3: Probability Concepts

Estimated time: 6 hours

- Apply basic rules of probability

- Calculate conditional probabilities and assess independence

- Work with discrete probability distributions (e.g., binomial)

- Understand continuous distributions including the normal distribution

### Module 4: Sampling Distributions & Central Limit Theorem

Estimated time: 6 hours

- Describe how sample statistics vary across samples

- Construct and interpret sampling distributions

- Apply the Central Limit Theorem for inference

- Understand the role of sample size in estimation accuracy

### Module 5: Regression Analysis

Estimated time: 5 hours

- Fit simple linear regression models

- Interpret slope and intercept in context

- Use correlation to measure linear association

- Analyze residuals and assess model fit

### Module 6: Significance Tests

Estimated time: 6 hours

- Perform one- and two-sample t-tests

- Conduct chi-square tests for categorical data

- Interpret p-values and construct confidence intervals

- Understand Type I and Type II errors in hypothesis testing

### Module 7: Resampling Techniques

Estimated time: 5 hours

- Apply bootstrapping to estimate uncertainty

- Use permutation tests for hypothesis testing

- Implement simulation-based inference methods

- Compare resampling approaches to traditional tests

### Module 8: Multiple Comparisons

Estimated time: 4 hours

- Identify challenges with multiple hypothesis testing

- Apply corrections for false discovery rate

- Interpret results when conducting many tests simultaneously

## Prerequisites

- Basic familiarity with arithmetic and reading comprehension

- No prior programming or advanced math required

- Willingness to engage with data conceptually and critically

## What You'll Be Able to Do After

- Summarize and visualize data using appropriate statistical methods

- Design reliable data collection strategies and avoid bias

- Apply probability and sampling theory to real-world problems

- Conduct hypothesis tests and interpret results accurately

- Use regression and resampling techniques to analyze relationships in data

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