Paired t Procedures

Jun 18th, 2015
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Albany State University
Course: general
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Objectives: In this module you will learn how to construct a confidence interval and perform a paired t-test in the case when we have two quantitative variables collected in pairs. You will make a confidence interval for and test hypotheses about the population mean difference, . You will be able to provide a statement about how confident you are about your interval estimate or in your decision.

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Paired t Procedures Objectives: In this module you will learn how to construct a confidence interval and perform a paired t-test in the case when we have two quantitative variables collected in pairs. You will make a confidence interval for and test hypotheses about the population mean difference,. You will be able to provide a statement about how confident you are about your interval estimate or in your decision.Overview: Matched or paired data results from a deliberate experimental design scheme. For example, suppose we are examining the effect of a drug on a certain type of response. The drug is administered to a group of people. Responses for each individual can be measured both before and after the drug is given. Or consider an experiment where rats are matched by weight, and then one rat in each match receives a new diet and the other rat in the match receives a control diet. These types of design are called paired data designs. Note that paired designs can occur when you have two measurements on the same individual or when you have two individuals that have been matched or paired prior to administering a treatment. The inference procedures for a paired data design are based on the one sample of differences, and thus the one-sample t procedures from Module 5 could be used. We are interested in estimating or testing hypotheses about the population mean difference, generally with the hypothesized value of zero, indicating no difference on average.Formula

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