Introduction to Statistics
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4,291 reviews
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Skills you'll gain
- Exploratory Data Analysis
- Statistical Inference
- Probability & Statistics
- Sampling (Statistics)
- Descriptive Statistics
- Probability Distribution
- Regression Analysis
- Correlation Analysis
- Statistical Visualization
- Analysis
- Statistics
- Statistical Hypothesis Testing
- Data Analysis
- Statistical Methods
- Statistical Analysis
- Data Collection
- Probability
- Statistical Machine Learning
Details to know
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There are 12 modules in this course
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.
Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.
This module introduces the course and its expectations, then covers the basic tools of descriptive statistics for exploring and summarizing data. Learners will examine common data visualizations and key numerical measures used to describe real-world datasets.
What's included
10 videos2 readings3 assignments
10 videos•Total 30 minutes
- Course Welcome•2 minutes
- Meet Guenther Walther•1 minute
- Introduction•2 minutes
- Pie Chart, Bar Graph, and Histograms•6 minutes
- Box-and-Whisker Plot and Scatter Plot•3 minutes
- Providing Context is Key for Statistical Analyses•2 minutes
- Pitfalls when Visualizing Information•2 minutes
- Mean and Median•5 minutes
- Percentiles, the Five Number Summary, and Standard Deviation•2 minutes
- [EXTRA] Industry Insight: Introduction to Andrew Radin•3 minutes
2 readings•Total 20 minutes
- Important information about your course•10 minutes
- Meeting You - Pre-Course Survey•10 minutes
3 assignments•Total 65 minutes
- Introduction and Descriptive Statistics for Exploring Data•40 minutes
- Quick Quiz about the requirements•10 minutes
- Practice Quiz: Summarizing and Interpreting Data•15 minutes
In this module, you will look at the main concepts for sampling and designing experiments. You will learn about curious pitfalls and how to evaluate the effectiveness of such experiments.
What's included
6 videos1 assignment
6 videos•Total 14 minutes
- Introduction•3 minutes
- Simple Random Sampling and Stratified Random Sampling•3 minutes
- Bias and Chance Error•1 minute
- Observation vs. Experiment, Confounding, and the Placebo Effect•4 minutes
- The Logic of Randomized Controlled Experiments•1 minute
- [EXTRA] Industry Insights: Filing a Patent for twoXAR•2 minutes
1 assignment•Total 45 minutes
- Producing Data and Sampling•45 minutes
In this module, you will learn about the definition of probability and the essential rules of probability that you will need for solving both simple and complex challenges. You will also learn about examples of how simple rules of probability are used to create solutions for real-life complex situations.
What's included
8 videos1 assignment
8 videos•Total 27 minutes
- The Interpretation of Probability•3 minutes
- Complement, Equally Likely Outcomes, Addition, and Multiplication•4 minutes
- Four Rules Example: How to Deal with "At Least One"•2 minutes
- Solving Problems by Total Enumeration•4 minutes
- Bayes' Rule•3 minutes
- Bayesian Analysis•5 minutes
- Warner's Randomized Response Model•4 minutes
- [EXTRA] Industry Insights: Drug Discovery at twoXAR•2 minutes
1 assignment•Total 45 minutes
- Probability•45 minutes
This module covers the empirical rule and normal approximation for data, a technique that is used in many statistical procedures. You will also learn about the binomial distribution and the basics of random variables.
What's included
10 videos1 assignment
10 videos•Total 27 minutes
- The Normal Curve•1 minute
- The Empirical Rule•3 minutes
- Standardizing Data and the Standard Normal Curve•2 minutes
- Normal Approximation•4 minutes
- Computing Percentiles with the Normal Approximation•2 minutes
- The Binomial Setting and Binomial Coefficient•4 minutes
- The Binomial Formula•4 minutes
- Random Variables and Probability Histograms•2 minutes
- Normal Approximation to the Binomial; Sampling Without Replacement•4 minutes
- [EXTRA] Industry Insights: Opportunities in Life Sciences•1 minute
1 assignment•Total 45 minutes
- The Normal Approximation for Data and the Binomial Distribution•45 minutes
In this module, you will learn about the Law of Large Numbers and the Central Limit Theorem. You will also learn how to differentiate between the different types of histograms present in statistical analysis.
What's included
9 videos1 assignment
9 videos•Total 23 minutes
- Parameter and Statistic•2 minutes
- Expected Value and Standard Error•3 minutes
- EV and SE of Sum, Percentages, and When Simulating•6 minutes
- The Square Root Law•3 minutes
- The Sampling Distribution•1 minute
- Three Histograms•2 minutes
- The Law of Large Numbers•1 minute
- The Central Limit Theorem•5 minutes
- When does the Central Limit Theorem Apply?•1 minute
1 assignment•Total 45 minutes
- Sampling Distributions and the Central Limit Theorem•45 minutes
This module covers regression, arguably the most important statistical technique based on its versatility to solve different types of statistical problems. You will learn about inference, regression, and how to do regression diagnostics.
What's included
10 videos1 assignment
10 videos•Total 34 minutes
- Prediction is a Key Task of Statistics•2 minutes
- The Correlation Coefficient•2 minutes
- Correlation Measures Linear Association•4 minutes
- Regression Line and the Method of Least Squares•3 minutes
- Regression to the Mean, The Regression Fallacy•4 minutes
- Predicting y from x and x from y•6 minutes
- Normal Approximation Given x•3 minutes
- Residual Plots, Heteroscedasticity, and Transformations•4 minutes
- Outliers and Influential Points•4 minutes
- [EXTRA] Industry Insights: Challenges to Using Data Science in Medicine•2 minutes
1 assignment•Total 45 minutes
- Regression•45 minutes
In this module, you will learn how to construct and interpret confidence intervals in standard situations.
What's included
4 videos1 assignment
4 videos•Total 15 minutes
- Interpretation of a Confidence Interval•6 minutes
- Using the Central Limit Theorem to Find a Confidence Interval•3 minutes
- Estimating the Standard Error with the Bootstrap Principle•3 minutes
- More About Confidence Intervals•2 minutes
1 assignment•Total 45 minutes
- Confidence Intervals•45 minutes
In this module, you will look at the logic behind testing and learn how to perform the appropriate statistical tests for different samples and situations. You will also learn about common misunderstandings and pitfalls in testing.
What's included
9 videos1 assignment
9 videos•Total 34 minutes
- The Idea Behind Testing Hypotheses•3 minutes
- Setting Up a Test Statistic•2 minutes
- p-values as Measures of Evidence•4 minutes
- Distinguishing Coke and Pepsi by Taste•4 minutes
- The t-test•5 minutes
- Statistical Significance vs. Importance•3 minutes
- The Two-Sample z-test•7 minutes
- Matched Pairs•6 minutes
- [EXTRA] Industry Insights: Hiring Data Science Talent•1 minute
1 assignment•Total 45 minutes
- Tests of Significance•45 minutes
This module focuses on the two main methods used in computer-intensive statistical inference: The Monte Carlo method, and the Bootstrap method. You will learn about the theoretic principles behind these methods and how they are applied in different contexts, such as regression and constructing confidence intervals.
What's included
5 videos1 assignment
5 videos•Total 17 minutes
- Using Computer Simulations in Place of Calculations•2 minutes
- Using the Law of Large Numbers to Approximate Quantities of Interest•4 minutes
- Plug-in Principle•5 minutes
- The Parametric Bootstrap and Bootstrap Confidence Intervals•4 minutes
- Bootstrapping in Regression•3 minutes
1 assignment•Total 45 minutes
- Resampling•45 minutes
This module focuses on the three important statistical analysis for categorical data: Chi-Square Goodness of Fit test, Chi-Square test of Homogeneity, and Chi-Square test of Independence.
What's included
3 videos1 assignment
3 videos•Total 14 minutes
- Relationships Between Two Categorical Variables•2 minutes
- The Color Proportions of M&Ms•5 minutes
- The Chi-Square Test for Homogeneity and Independence•7 minutes
1 assignment•Total 45 minutes
- Analysis of Categorical Data•45 minutes
This module covers the basics of ANOVA and how F-tests work on one-way ANOVA examples.
What's included
5 videos1 assignment
5 videos•Total 16 minutes
- Comparing Several Means•1 minute
- The Idea of Analysis of Variance•4 minutes
- Using the F Distribution to Evaluate ANOVA•6 minutes
- More on ANOVA•2 minutes
- [EXTRA] Industry Insights: Starting Your Career in Data Science•3 minutes
1 assignment•Total 45 minutes
- One-Way Analysis of Variance•45 minutes
In this module, you will learn about very important issues that have surfaced in the era of big data: data snooping and the multiple testing fallacy. You will also explore the reasons behind challenges in data reproducibility and applicability, and how to prevent such issues in your own work.
What's included
3 videos1 reading1 assignment
3 videos•Total 12 minutes
- Data Snooping and the Multiple Testing Fallacy, Reproducibility and Replicability•3 minutes
- Bonferroni Correction, False Discovery Rate, and Data Splitting•7 minutes
- Summary•1 minute
1 reading•Total 10 minutes
- Thank You and Course Evaluation•10 minutes
1 assignment•Total 45 minutes
- Multiple Comparisons•45 minutes
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Reviewed on Aug 11, 2022
Gives a great overview of every important topic in statistics. However, lots of things aren't explained thoroughly. I had to use other websites to gain a sufficient understanding of lots of material.
Reviewed on Nov 17, 2023
The course was incredibly informative. I am glad that I got the opportunity to study in a course on statistics from Stanford University itself. I thank the creators and participants of the course!
Reviewed on Jun 2, 2023
Peer reviews helped me ensure I learned the content. Sometimes the test doesn't verify that a student is learning. Aside from that I was able to freshen up on my math skills.
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