Stanford University - Introduction to Statistics
4.6 /5- Offered byCoursera
Introduction to Statistics at Coursera Overview
Duration | 15 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to Statistics at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level Basic familiarity with computers and productivity software No calculus required
- Approx. 15 hours to complete
- English Subtitles: English
Introduction to Statistics at Coursera Course details
- 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.
Introduction to Statistics at Coursera Curriculum
Introduction and Descriptive Statistics for Exploring Data
Course Welcome
Meet Guenther Walther
Introduction
Pie Chart, Bar Graph, and Histograms
Box-and-Whisker Plot and Scatter Plot
Providing Context is Key for Statistical Analyses
Pitfalls when Visualizing Information
Mean and Median
Percentiles, the Five Number Summary, and Standard Deviation
[EXTRA] Industry Insight: Introduction to Andrew Radin
Read First - Important Information About Your Course
Course Slides
Course Syllabus
Meeting You - Pre-Course Survey
Quick Quiz About the Requirements
Introduction and Descriptive Statistics for Exploring Data
Producing Data and Sampling
Introduction
Simple Random Sampling and Stratified Random Sampling
Bias and Chance Error
Observation vs. Experiment, Confounding, and the Placebo Effect
The Logic of Randomized Controlled Experiments
[EXTRA] Industry Insights: Filing a Patent for twoXAR
Producing Data and Sampling
The Interpretation of Probability
Complement, Equally Likely Outcomes, Addition, and Multiplication
Four Rules Example: How to Deal with "At Least One"
Solving Problems by Total Enumeration
Bayes' Rule
Bayesian Analysis
Warner's Randomized Response Model
[EXTRA] Industry Insights: Drug Discovery at twoXAR
Probability
Normal Approximation and Binomial Distribution
The Normal Curve
The Empirical Rule
Standardizing Data and the Standard Normal Curve
Normal Approximation
Computing Percentiles with the Normal Approximation
The Binomial Setting and Binomial Coefficient
The Binomial Formula
Random Variables and Probability Histograms
Normal Approximation to the Binomial; Sampling Without Replacement
[EXTRA] Industry Insights: Opportunities in Life Sciences
The Normal Approximation for Data and the Binomial Distribution
Parameter and Statistic
Expected Value and Standard Error
EV and SE of Sum, Percentages, and When Simulating
The Square Root Law
The Sampling Distribution
Three Histograms
The Law of Large Numbers
The Central Limit Theorem
When does the Central Limit Theorem Apply?
Sampling Distributions and the Central Limit Theorem
Regression
Prediction is a Key Task of Statistics
The Correlation Coefficient
Correlation Measures Linear Association
Regression Line and the Method of Least Squares
Regression to the Mean, The Regression Fallacy
Predicting y from x and x from y
Normal Approximation Given x
Residual Plots, Heteroscedasticity, and Transformations
Outliers and Influential Points
[EXTRA] Industry Insights: Challenges to Using Data Science in Medicine
Regression
Confidence Intervals
Interpretation of a Confidence Interval
Using the Central Limit Theorem to Find a Confidence Interval
Estimating the Standard Error with the Bootstrap Principle
More About Confidence Intervals
Confidence Intervals
The Idea Behind Testing Hypotheses
Setting Up a Test Statistic
p-values as Measures of Evidence
Distinguishing Coke and Pepsi by Taste
The t-test
Statistical Significance vs. Importance
The Two-Sample z-test
Matched Pairs
[EXTRA] Industry Insights: Hiring Data Science Talent
Tests of Significance
Resampling
Using Computer Simulations in Place of Calculations
Using the Law of Large Numbers to Approximate Quantities of Interest
Plug-in Principle
The Parametric Bootstrap and Bootstrap Confidence Intervals
Bootstrapping in Regression
Resampling
Relationships Between Two Categorical Variables
The Color Proportions of M&Ms
The Chi-Square Test for Homogeneity and Independence
Analysis of Categorical Data
One-Way Analysis of Variance (ANOVA)
Comparing Several Means
The Idea of Analysis of Variance
Using the F Distribution to Evaluate ANOVA
More on ANOVA
[EXTRA] Industry Insights: Starting Your Career in Data Science
One-Way Analysis of Variance
Multiple Comparisons
Data Snooping and the Multiple Testing Fallacy, Reproducibility and Replicability
Bonferroni Correction, False Discovery Rate, and Data Splitting
Summary
Thank You and Course Evaluation
Multiple Comparisons
Introduction to Statistics at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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