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MQM Stats Minor Exam: Regression, ANCOVA, Hypothesis Testing, Effect Size, Assignments of Statistics

Effect Size MeasuresHypothesis TestingAnalysis of Variance (ANOVA)Survey ResearchRegression Analysis

A comprehensive exam for the statistics minor at mqm, covering topics such as standard errors, multiple regression, analysis of covariance (ancova), hypothesis testing, and effect size measures. Students are required to answer four questions, with the first two being mandatory and the remaining two to be chosen from the part ii questions. The exam includes questions on factors influencing standard errors, differences between standardized and raw regression coefficients, the role of ancova in comparative quasi-experimental and experimental research studies, the concept of test-statistics and its importance in hypothesis testing, advantages and disadvantages of effect size measures, and factors affecting the power of a statistical test.

What you will learn

  • How can these factors be handled to obtain a more precise estimate?

Typology: Assignments

2021/2022

Uploaded on 08/01/2022

hal_s95
hal_s95 🇵🇭

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Download MQM Stats Minor Exam: Regression, ANCOVA, Hypothesis Testing, Effect Size and more Assignments Statistics in PDF only on Docsity! MQM Statistics Minor Page 1 of 2 MQM Comprehensive Examination Statistics Minor Day 3 Directions: On this exam you must answer four questions. The first two (in Part I) are required, and you must choose two from among the remaining four questions in Part II. Begin each response on a new page, and clearly number the questions to which you are responding. Part I. Answer both questions. 1. Standard errors: The standard error is central to survey research. Assume you are to estimate the proportion of the United States adult population that believes the United States should sign the Kyoto agreement. A. What factors would influence the standard error of this estimate? B. How would you suggest that these factors be handled so as to obtain a more precise estimate of the proportion? 2. Multiple regression: Standardized regression coefficients, or “betas,” from a multiple regression model are sometimes preferred to as “raw” regression coefficients because the differences in their magnitudes indicate their relative importance in predicting the outcome variable. A. Discuss how these two types of regression coefficients differ. B. For each type, describe a context when that type would be preferable. Part II. Answer two of the following four questions. 3. Analysis of Covariance (ANCOVA) A. Using analysis of variance (ANOVA) as the frame of reference, discuss why analysis of covariance (ANCOVA) is a frequently used statistical technique. B. Discuss ANCOVA’s primary role in both comparative quasi-experimental research studies and in experimental (randomized) research studies. 4. Hypothesis testing: Employing the following terms in your answer: sample sizes, standard errors, critical value, Type I error, and p-value, answer the following: A. What is a test-statistic, and how is it helpful for making statistical inference?
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