Pages: 4

This workbook started as a set of course notes and handouts that I used while teaching Introduction to Biostatistics. Although I love technology, I just think that “old-school” is better for some things. I remember learning math in elementary school with workbooks. I am almost sure that none of my current students have even seen a workbook. Nonetheless, there is something about working through problems that helps people to grasp the concepts. I try to use examples in this workbook that are easy to understand, and I walk through problems step by step.

Pages: 22

This chapter will focus on descriptive statistics and will include the following: Measures of central tendency (measures of location). Measures of spread (measures of dispersion). Measures of variability. Graphic methods. Outliers and standard distribution rules.

Pages: 20

In this workbook, I will use the statistical program R with the integrated development environment RStudio to complete problems; other programs used in the workbook are SAS® and Stata®. This workbook aims to teach statistical concepts, so we have tried to make the computing aspect of this text as simple as possible. In each chapter throughout the workbook, we will review some R pointers and sample code containing the information we need to complete the computing for that chapter. This lab provides a beginner’s guide to using R software; it assumes no previous knowledge of R or any other statistical software program. It begins with technical aspects, such as the layout of the program and menu navigation. It moves on to importing data into the program and performing various procedures related to data management and data cleaning, as well as variable recoding and labeling. Sample code is given for descriptive statistics (Chapter 1) and graphing procedures. Users will begin to understand the R programming language and coding structure through experiential examples. I use R version 4.4.2 (2024-10-31) and RStudio 2024.12.0 (Build 467) throughout this workbook, but most commands will be similar regardless of which R and RStudio version is being used (although I recommend updating the software as appropriate).

Pages: 28

The choice of a statistical package is up to you. They all allow you to do the same tasks for the most part although some packages are better at certain tasks than others. However, for the purposes of the material in this book, they all work well. When deciding which package to use, think of it like learning a new language. It is more about who you want to be able to communicate with and your ability to learn new things. In this workbook, we have decided to use three programs so that you will be able to see the similarities and differences. The purpose of this lab is to introduce you to the SAS® statistical software package. We are using SAS version 9.4 (SAS Institute, Cary, North Carolina) throughout this workbook.

Pages: 23

In this workbook, we will use the statistical program Stata® (rhymes with data) to complete problems; the other programs are R and SAS®. This workbook aims to teach statistical concepts, so we have tried to make the computing aspect of this text as simple as possible. In each chapter throughout the workbook, we will review some Stata pointers containing the information we need to complete the computing for that chapter. We will use Stata version 18.5 (StataCorp LLC, College Station, Texas) throughout this workbook, but most commands will be similar regardless of which Stata version is used.

Pages: 16

This chapter will focus on the concept of probability and will include the following: Properties of probabilities. Laws of probabilities.

Pages: 20

This chapter will focus on applying the concepts of probability to diagnostic testing and screening and will include the following: Diagnostic terms and concepts. Receiver operating characteristic (ROC) curves.

Pages: 20

This chapter will discuss problems that can be put in a probabilistic framework and the following topics will be discussed: Measures of location and spread for random variables. Permutations and combinations. Binomial distribution. Poisson distribution.

Pages: 17

This chapter will focus on probability distributions for continuous random variables and will include the following: Continuous probability distributions. Standard normal distribution.

Pages: 3

To recap the binomial, Poisson, and normal distributions, fill in Table B.1.

Pages: 31

This chapter will focus on estimation for means and proportions and will include the following: Statistical inference. Sampling. Randomized clinical trial. Sampling distribution of the mean. Central limit theorem. Confidence intervals for means. Using R to construct confidence intervals for a mean. Using SAS® to construct confidence intervals for a mean. Using Stata® to construct confidence intervals for a mean. The t-distribution. Obtaining critical values in R, SAS, and Stata. Sampling distribution for proportions. Confidence intervals for proportions. Using R to obtain confidence intervals for proportions. Using SAS to obtain confidence intervals for proportions. Using Stata to obtain confidence intervals for proportions.

Pages: 45

This chapter will focus on one-sample hypothesis tests and will include the following topics: Basics of hypothesis testing. Confidence intervals and hypothesis tests. One-sample tests for the mean (using software). Inference for proportions (using software). Determining power and calculating sample size (using software).

Pages: 7

Along with exercise, a healthy diet is a key part of maintaining a healthy weight. The weights of U.S. men aged 18 or older who self-report having an excellent, very good, or good diet were measured. 1 In a sample of 216 adult men who self-report having an excellent, very good, or good diet, the mean weight is 83.74 kg with a standard deviation of 19.98 kg. The data can be found in the labc_diet_weight file.

Pages: 42

This chapter will focus on two-sample hypothesis testing and will include the following topics: Dependent samples (paired tests). Independent samples. Sample size and power for two-sample test of means.

Pages: 29

This chapter will focus on nonparametric statistical methods and will include the following topics: Types of data. Parametric vs. nonparametric tests. Nonparametric tests for paired data. Nonparametric tests for independent data.

Pages: 6

As the demographic composition of the United States changes, certain areas of the country are becoming minority-majorities: places where racial and ethnic minoritized populations together make up a majority of the residents. We want to look at the relationship between minority-majority status and unemployment rates. Our data uses the Metropolitan Statistical Area (MSA) as the geographical unit of analysis. Specifically, we will test whether the employment rate in MSAs with a minority-majority is the same as in MSAs without a minority-majority. The data are in the file labd_metro_areas. 1 The variable minority_majority takes a value of 1 for minority-majority MSAs and 0 for non–minority-majority MSAs. The variable unemployment_rate is the unemployment rate in the MSA. What are the null and alternative hypotheses? Write and run the code (R, SAS® , or Stata® ) to perform the test. B) Look at the output. How many of the MSAs are minority-majorities? How many still have a population that is majority non-Hispanic White? What is the mean unemployment rate in minority-majority MSAs? What is the mean in MSAs without a minority-majority? Can we make the equal variance assumption for these data? What are the value and distribution of the test statistic? What is the p-value of the test? Do you reject or fail to reject the null hypothesis? What is your conclusion from the hypothesis test?

Pages: 60

This chapter will focus on hypothesis testing with categorical data and will include the fol-lowing topics: Two-sample test for proportions. The chi-squared distribution. Odds ratios. Sample size and power for comparing two binomial proportions.

Pages: 35

This chapter will focus on one-way analysis of variance and will include the following topics: Within- and between- group variation. ANOVA assumptions. Testing for significance. Multiple comparisons.

Pages: 37

This chapter will focus on linear correlation between two continuous variables and will include the following topics: Population Correlation Coefficient (ρ). Pearson Correlation Coefficient (r). Spearman Rank Correlation Coefficient (rs ).

Pages: 42

This chapter will focus on investigating the change in a continuous variable in response to a change in one or more predictor variables and will include the following topics: Simple linear regression. Regression concepts. Method of least squares. Linear relationship. Inference for predicted values. Evaluation of the model. Multiple linear regression. Model evaluation. Other explanatory variables. Model selection.

Pages: 31

This chapter will focus on logistic regression analysis and will include the following topics: Interpretation of coefficients. Hypothesis tests and confidence intervals for estimated regression coefficients. Model evaluation.

Pages: 20

This chapter will focus on a basic introduction to survival analysis and will include the following topics: The life-table method. The product-limit method.

Pages: 10

A random sample of 1000 births from the 2010 birth cohort–linked birth and infant death dataset is provided in the labe_births_2010 dataset. 1 Use the sample to answer the following questions. The questions are just a guide, and it is important to conduct descriptive analyses to explore the dataset, write the null and alternative hypotheses, interpret confidence intervals, and draw conclusions. Include all assumptions, justifications, interpretations, and explanations in the answers.

Pages: 16

This chapter will focus on the concept of data and will include the following: Data and data use. Data literacy. Data ethics. Data communication. Data visualization.