Webcasts for Classes Jason Wilson, Ph.D
Biola University

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All webcasts are posted on www.youtube.com except the RStudio demonstrations, which are mostly .swf files posted on www.screencast.com.  They are intended for use in Professor Wilson's classes, whether assigned or as supplements.

General Purpose

• Joy of Stats Video, by Hans Rosling

• History of Statistics

• Exploratory Data Analysis

• Visualizing Data

Math 210 Intro Stats (Text: Utts, although note some webcasts are borrowed from Math 318 with the Whitlock and Schluter text)

• Chapter 3: Measurements, mistakes, and misunderstandings

• Chapter 5: Experiments and observational studies

• Chapter 7a: Plots, graphs, and pictures

• Chapter 7b: Summarizing and displaying measurement data

• Chapter 8: Bell-shaped curves and other shapes

• Chapter 9: Visualizing Data (Lecture)

• Chapter 10: Relationships between measurement variables

• Chapter 13: Statistical significance for 2x2 tables

• Chapter 16: Understanding probability and long-term expectations (Rules)

• Chapter 16: Understanding probability and long-term expectations (Examples)

• Chapter 23: Hypothesis Testing Examples 1-2

  • Chapter 23: Hypothesis Testing Examples 3 

RStudio Demonstrations

• • First R Session - Demonstration of your first session in RStudio, how to load files, use workspace, and save

  • Introduction_to_R_script_file1 - Works through the essentials of s.Introduction_to_R scripts

  • Introduction_to_R_script_file2 - Continues s.Introduction_to_R scripts, touches on finer points

• WhitlockSchluter_Ch2_Demonstration

  • Load Data - How to load data into R using RStudio in 5 ways: .csv, tab-delimited, web, scan(), other languages

  • Load Data, advanced - How to copy/paste data from another source into a spreadsheet, then to RStudio + 2 direct R data entry commands

  • LoadDataAndScripts -   Shows how to load data.frames and script files into RStudio

• AccessBuiltInDatasets

• RegressionAndCorrelation_Basic

• RegressionAndCorrelation_Advanced

  • PutROutputIntoWord -  Sample session taking RStudio output and formatting it into a Word document

Math 318 Biostatistics (Text: Whitlock and Schluter)

• Chapter 1a: Introduction to statistics (Key concepts)

• Chapter 1b: Introduction to statistics

• Chapter 2a: Displaying data

• Chapter 2b: Visualizing Data (Lecture)

• Chapter 3a: Describing data (Statistics)

• Chapter 3b: Describing data (Spread)

• Chapter 3c: Describing data (Boxplot)

• Chapter 4a: Estimating with uncertainty (Sampling distributions)

• Chapter 4b: Estimating with uncertainty (Confidence intervals)

• Chapter 5a: Probability (Rules)

• Chapter 5b: Probability (Examples)

• Chapter 5c: Probability (Advanced Examples)

• Chapter 6a: Hypothesis testing (Intro)

• Chapter 6b: Hypothesis testing (Remarks)

• Chapter 7a: Analyzing proportions (Binomial distribution)

• Chapter 7b: Analyzing proportions (Binomial examples)

• Chapter 7c: Analyzing proportions (Proportion tests)

• Chapter 7d: Analyzing proportions (Proportion CIs)

• Chapter 9a: Contingency analysis (Odds ratios)

• Chapter 9b: Contingency analysis (Chi-square tests of independence)

• Chapter 10a: The normal distribution (Introduction)

• Chapter 10b: The normal distribution (Examples)

• Chapter 10c: The normal distribution (Central limit theorem)

• Chapter 11a: Inference for a normal population (Introduction to Student's t distribution)

• Chapter 11b: Inference for a normal population (Confidence intervals)

• Chapter 11c: Inference for a normal population (Hypothesis tests)

• Chapter 12a: Comparing two means (Introduction)

• Chapter 12b: Comparing two means (Paired t-test)

• Chapter 12c: Comparing two means (Independent t-test)

• Chapter 13a: Normal QQ plots and Shapiro-Wilk test of normality

• Chapter 13b: Wilcoxon test and transformations

  • Chapter 14: No webcasts

• Chapter 15a: ANOVA theory

• Chapter 15b: ANOVA example

  • Chapter 16a: Correlation concept 

• Chapter 16b: Correlation example

• Chapter 17a: Simple linear regression concept

• Chapter 17b: Simple linear regression example

Math 331 Probability

• Chapter 3.3: Probability

• Chapter 3.4a: Random variables

• Chapter 3.4b: Moments

• Chapter 4.2a: Uniform to negative binomial

• Chapter 4.2b: Poisson to hypergeometric

• Chapter 4.3a: Uniform, Exponential, Gamma

• Chapter 4.3b: Weibull, Beta, Normal