tailieunhanh - Ebook Statistics for the life sciences (4th edition): Part 2

(BQ) Part 2 book "Statistics for the life sciences" has contents: Comparison of paired samples; categorical data - one-s ample distributions; categorical data - relationships, comparing themeans of many independent samples, linear regression and correlation, a summary of inference methods. | Chapter COMPARISON OF PAIRED SAMPLES 8 Objectives In this chapter we study comparisons of paired samples. We will • demonstrate how to conduct a paired t test. • demonstrate how to construct and interpret a confidence interval for the mean of a paired difference. • discuss ways in which paired data arise and how pairing can be advantageous. • consider the conditions under which a paired t test is valid. • show how paired data may be analyzed using the sign test and the Wilcoxon signed-rank test. Introduction In Chapter 7 we considered the comparison of two independent samples when the response variable Y is a quantitative variable. In the present chapter we consider the comparison of two samples that are not independent but are paired. In a paired design, the observations (Y1, Y2) occur in pairs; the observational units in a pair are linked in some way, so that they have more in common with each other than with members of another pair. The following is an example of a paired design. Example Blood Flow Does drinking coffee affect blood flow, particularly during exercise? Doctors studying healthy subjects measured myocardial blood flow (MBF)* during bicycle exercise before and after giving the subjects a dose of caffeine that was equivalent to drinking two cups of coffee. Table shows the MBF levels before (baseline) and after (caffeine) the subjects took a tablet containing 200 mg of Figure shows parallel dotplots of these data, with line segments that connect the baseline and caffeine readings for each subject so that the change from “before” to “after” is evident for each subject. In Example the data arise in pairs; the data in a pair are linked by virtue of being measurements on the same person. A suitable analysis of the data should take advantage of this pairing. That is, we could imagine an experiment in which some subjects are studied after being given caffeine and others are studied without ever being given .