Introduction and Descriptive Statistics for Exploring Data-Getting Started
Read First - Important Information About Your Course
Course Slides
Course Syllabus
Introduction and Descriptive Statistics for Exploring Data-Welcome and Introductions
Course Welcome
()
Meet Guenther Walther
()
Meeting You - Pre-Course Survey
Introduction and Descriptive Statistics for Exploring Data-Descriptive Statistics and Visualizing Information
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
()
Introduction and Descriptive Statistics for Exploring Data-Numerical Summary Measures
Mean and Median
()
Percentiles, the Five Number Summary, and Standard Deviation
()
Introduction and Descriptive Statistics for Exploring Data-Summary
[EXTRA] Industry Insight: Introduction to Andrew Radin
()
Producing Data and Sampling-Introduction
Introduction
()
Producing Data and Sampling-Simple Random Sampling and Other Sampling Plans
Simple Random Sampling and Stratified Random Sampling
()
Bias and Chance Error
()
Producing Data and Sampling-Randomized Controlled Experiments and Observation Studies
Observation vs. Experiment, Confounding, and the Placebo Effect
()
The Logic of Randomized Controlled Experiments
()
Producing Data and Sampling-Summary
[EXTRA] Industry Insights: Filing a Patent for twoXAR
()
Probability-Introduction
The Interpretation of Probability
()
Probability-Four Basic Rules
Complement, Equally Likely Outcomes, Addition, and Multiplication
()
Four Rules Example: How to Deal with "At Least One"
()
Probability-Conditional Probability and Bayes' Rule
Solving Problems by Total Enumeration
()
Bayes' Rule
()
Probability-Examples and Case Studies
Bayesian Analysis
()
Warner's Randomized Response Model
()
Probability-Summary
[EXTRA] Industry Insights: Drug Discovery at twoXAR
()
Normal Approximation and Binomial Distribution-Introduction
The Normal Curve
()
Normal Approximation and Binomial Distribution-The Normal Approximation
The Empirical Rule
()
Standardizing Data and the Standard Normal Curve
()
Normal Approximation
()
Computing Percentiles with the Normal Approximation
()
Normal Approximation and Binomial Distribution-Binomial Distributions
The Binomial Setting and Binomial Coefficient
()
The Binomial Formula
()
Random Variables and Probability Histograms
()
Normal Approximation to the Binomial; Sampling Without Replacement
()
Normal Approximation and Binomial Distribution-Summary
[EXTRA] Industry Insights: Opportunities in Life Sciences
()
Sampling Distributions and the Central Limit Theorem-Introduction
Parameter and Statistic
()
Sampling Distributions and the Central Limit Theorem-The Expected Value, Standard Error, and Sampling Distribution of a Statistic
Expected Value and Standard Error
()
EV and SE of Sum, Percentages, and When Simulating
()
The Square Root Law
()
The Sampling Distribution
()
Three Histograms
()
Sampling Distributions and the Central Limit Theorem-The Law of Large Numbers and the Central Limit Theorem
The Law of Large Numbers
()
The Central Limit Theorem
()
When does the Central Limit Theorem Apply?
()
Regression-Introduction
Prediction is a Key Task of Statistics
()
Regression-Correlation
The Correlation Coefficient
()
Correlation Measures Linear Association
()
Regression-Inference in Regression
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
()
Regression-Residuals
Residual Plots, Heteroscedasticity, and Transformations
()
Outliers and Influential Points
()
Regression-Summary
[EXTRA] Industry Insights: Challenges to Using Data Science in Medicine
()
Confidence Intervals-Introduction
Interpretation of a Confidence Interval
()
Confidence Intervals-Confidence Intervals via the Central Limit Theorem
Using the Central Limit Theorem to Find a Confidence Interval
()
Estimating the Standard Error with the Bootstrap Principle
()
More About Confidence Intervals
()
Tests of Significance-Introduction
The Idea Behind Testing Hypotheses
()
Tests of Significance-Test Statistics and p-values
Setting Up a Test Statistic
()
p-values as Measures of Evidence
()
Tests of Significance-More on Testing
Distinguishing Coke and Pepsi by Taste
()
The t-test
()
Statistical Significance vs. Importance
()
Tests of Significance-Comparing Two Populations
The Two-Sample z-test
()
Matched Pairs
()
Tests of Significance-Summary
[EXTRA] Industry Insights: Hiring Data Science Talent
()
Resampling-Introduction
Using Computer Simulations in Place of Calculations
()
Resampling-The Monte Carlo Method
Using the Law of Large Numbers to Approximate Quantities of Interest
()
Resampling-The Bootstrap
Plug-in Principle
()
Resampling-More About the Bootstrap
The Parametric Bootstrap and Bootstrap Confidence Intervals
()
Bootstrapping in Regression
()
Analysis of Categorical Data-Introduction
Relationships Between Two Categorical Variables
()
Analysis of Categorical Data-The Chi-Square Test for Goodness of Fit
The Color Proportions of M&Ms
()
Analysis of Categorical Data-The Chi-Square Test for Homogeneity and Independence
The Chi-Square Test for Homogeneity and Independence
()
One-Way Analysis of Variance (ANOVA)-Introduction
Comparing Several Means
()
One-Way Analysis of Variance (ANOVA)-The Analysis of Variance F-Test
The Idea of Analysis of Variance
()
Using the F Distribution to Evaluate ANOVA
()
More on ANOVA
()
One-Way Analysis of Variance (ANOVA)-Summary
[EXTRA] Industry Insights: Starting Your Career in Data Science
()
Multiple Comparisons-Introduction
Data Snooping and the Multiple Testing Fallacy, Reproducibility and Replicability
()
Multiple Comparisons-Accounting for Multiple Comparisons
Bonferroni Correction, False Discovery Rate, and Data Splitting
()
Multiple Comparisons-Summary
Summary
()
Multiple Comparisons-Final Words
Thank You and Course Evaluation