431 Course Notes, Fall 2018
Introduction
1 Data Science
2 Setting Up R
Part A. Exploring Data
3 Visualizing Data
4 Data Structures and Types of Variables
5 Summarizing Quantitative Variables
6 Summarizing Categorical Variables
7 The National Youth Fitness Survey (nyfs1)
8 Assessing Normality
9 Using Transformations to “Normalize” Distributions
10 Summarizing data within subgroups
11 Straight Line Models and Correlation
12 Studying Crab Claws (crabs)
13 The Western Collaborative Group Study
14 Part A: A Few of the Key Points
Part B. Making Comparisons
15 Introduction to Part B
16 The Serum Zinc Study
17 A Paired Sample Study: Lead in the Blood of Children
18 A Study Comparing Two Independent Samples: Ibuprofen in Sepsis Trial
- 18.1 The Ibuprofen in Sepsis Randomized Clinical Trial
- 18.2 Exploratory Data Analysis
19 Confidence Intervals for a Single Sample of Quantitative Data
20 Confidence Intervals from Two Paired Samples of Quantitative Data
21 Confidence Intervals from Two Independent Samples of Quantitative Data
22 Hypothesis Testing of a Population Mean
23 Type I and Type II Error: Power and Confidence
24 Comparing Two Means Using Paired Samples
25 Comparing Two Means Using Independent Samples
26 Power and Sample Size Issues Comparing Two Means
27 A Review: Two Examples, Comparing Means
28 Comparing 3 or more Population Means: Analysis of Variance
29 Estimating a Population Rate or Proportion
30 Comparing Population Rates / Proportions
31 Power and Sample Size for Comparing Two Population Proportions
32 Larger Contingency Tables - Testing for Independence
33 Three-Way Tables: A 2x2xK Table and a Mantel-Haenszel Analysis
- 33.1 Smoking and Mortality in the UK
- 33.2 The whickham data including age, as well as smoking and mortality
34 Some Thoughts on p values
35 Study Design: Type S and Type M Errors
- 35.1 Materials to come.
36 Partial Review to help you prepare for Quiz 2
Part C. Building Models
37 Introduction for Part C
38 Re-Expression, Tukey’s Ladder & Box-Cox Plot
39 Dehydration Recovery in Kids: A Small Study
- 39.1 A Scatterplot Matrix
- 39.2 Are the recovery scores well described by a Normal model?
40 Simple Regression: Using Dose to predict Recovery
41 Multiple Regression with the hydrate data
42 Regression Diagnostics
43 Model Selection and Out-of-Sample Validation
44 Dealing with Missing Data
45 BMI and Employment: Working with Categorical Predictors
46 Species Found on the Galapagos Islands
47 Influence Measures for Multiple Regression
- 47.1 DFBETAs
- 47.2 Other Available Influence Measures
48 Building Predictions from our models
- 48.1 Predictions for a “typical” island
  - 48.1.1 Questions about the Prediction and Confidence Interval Methods
- 48.2 Making a Prediction with New Data
49 Standardizing/Rescaling in Regression Models
50 Missing Data Mechanisms and Simple Imputation
References
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Data Science for Biological, Medical and Health Research: Notes for 431

Chapter 35 Study Design: Type S and Type M Errors

35.1 Materials to come.

Dr. Love will add materials here, soon.