Introduction to Statistics by Coursera
Skills you will learn!
Curriculum
16 Topics
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
7 Topics
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
9 Topics
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
11 Topics
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
10 Topics
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
11 Topics
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
5 Topics
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
10 Topics
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
6 Topics
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
4 Topics
Relationships Between Two Categorical Variables
The Color Proportions of M&Ms
The Chi-Square Test for Homogeneity and Independence
Analysis of Categorical Data
6 Topics
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
5 Topics
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
Related Data Analysis Courses
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