Statistics for the Behavioral Sciences
2nd Edition
ISBN: 9781452286907
Author: Gregory J. Privitera
Publisher: SAGE Publications, Inc
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Chapter 3, Problem 6FP
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Explain when the weighted
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A marketing professor has surveyed the students at her university to better understand attitudes towards PPT usage for higher education. To be able to make inferences to the entire student body, the sample drawn needs to represent the university’s student population on all key characteristics. The table below shows the five key student demographic variables. The professor found the breakdown of the overall student body in the university’s fact book posted online.
A non-parametric chi-square test was used to test the sample demographics against the population percentages shown in the table above. Review the output for the five chi-square tests on the following pages and answer the five questions:
Based on the chi-square test, which sample variables adequately represent the university’s student population and which ones do not? Support your answer by providing the p-value of the chi-square test and explaining what it means.
Using the results from Question 1, make recommendation for…
A marketing professor has surveyed the students at her university to better understand attitudes towards PPT usage for higher education. To be able to make inferences to the entire student body, the sample drawn needs to represent the university’s student population on all key characteristics. The table below shows the five key student demographic variables. The professor found the breakdown of the overall student body in the university’s fact book posted online.
A non-parametric chi-square test was used to test the sample demographics against the population percentages shown in the table above. Review the output for the five chi-square tests on the following pages and answer the five questions:
Based on the chi-square test, which sample variables adequately represent the university’s student population and which ones do not? Support your answer by providing the p-value of the chi-square test and explaining what it means.
Using the results from Question 1, make recommendation for…
Chapter 3 Solutions
Statistics for the Behavioral Sciences
Ch. 3.1 - Prob. 1.1LCCh. 3.1 - Prob. 1.2LCCh. 3.2 - Prob. 2.1LCCh. 3.2 - Prob. 2.2LCCh. 3.2 - Prob. 2.3LCCh. 3.2 - Prob. 3.1LCCh. 3.2 - Prob. 3.2LCCh. 3.2 - Prob. 3.3LCCh. 3.2 - Prob. 3.4LCCh. 3.2 - Prob. 4.1LC
Ch. 3.2 - Prob. 4.2LCCh. 3.2 - Prob. 4.3LCCh. 3.3 - Prob. 5.1LCCh. 3.3 - Prob. 5.2LCCh. 3.3 - Prob. 5.3LCCh. 3.3 - Prob. 5.4LCCh. 3.3 - Prob. 5.5LCCh. 3.4 - Prob. 6.1LCCh. 3.4 - Prob. 6.2LCCh. 3.4 - Prob. 6.3LCCh. 3.4 - Prob. 6.4LCCh. 3 - Prob. 1FPCh. 3 - Prob. 2FPCh. 3 - Prob. 3FPCh. 3 - Prob. 4FPCh. 3 - Prob. 5FPCh. 3 - Prob. 6FPCh. 3 - Prob. 7FPCh. 3 - Prob. 8FPCh. 3 - Prob. 9FPCh. 3 - Prob. 10FPCh. 3 - Prob. 11CAPCh. 3 - Prob. 12CAPCh. 3 - Prob. 13CAPCh. 3 - Prob. 14CAPCh. 3 - Prob. 15CAPCh. 3 - Prob. 16CAPCh. 3 - Prob. 17CAPCh. 3 - Prob. 18CAPCh. 3 - Prob. 19CAPCh. 3 - Prob. 20CAPCh. 3 - Prob. 21CAPCh. 3 - Prob. 22CAPCh. 3 - Prob. 23CAPCh. 3 - Prob. 24CAPCh. 3 - Prob. 25CAPCh. 3 - Prob. 26CAPCh. 3 - Prob. 27CAPCh. 3 - Prob. 28CAPCh. 3 - Prob. 29PRCh. 3 - Prob. 30PRCh. 3 - Prob. 31PRCh. 3 - Prob. 32PRCh. 3 - Prob. 33PRCh. 3 - Prob. 34PR
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- Question 4 An article in Quality Progress (May 2011, pp. 42-48) describes the use of factorial experiments to improve a silver powder production process. This product is used in conductive pastes to manufacture a wide variety of products ranging from silicon wafers to elastic membrane switches. Powder density (g/cm²) and surface area (cm/g) are the two critical characteristics of this product. The experiments involved three factors: reaction temperature, ammonium percentage, stirring rate. Each of these factors had two levels, and the design was replicated twice. The design is shown in Table 3. A222222222222233 Stir Rate (RPM) Ammonium (%) Table 3: Silver Powder Experiment from Exercise 13.23 Temperature (°C) Density Surface Area 100 8 14.68 0.40 100 8 15.18 0.43 30 100 8 15.12 0.42 30 100 17.48 0.41 150 7.54 0.69 150 8 6.66 0.67 30 150 8 12.46 0.52 30 150 8 12.62 0.36 100 40 10.95 0.58 100 40 17.68 0.43 30 100 40 12.65 0.57 30 100 40 15.96 0.54 150 40 8.03 0.68 150 40 8.84 0.75 30 150…arrow_forward- + ++ Table 2: Crack Experiment for Exercise 2 A B C D Treatment Combination (1) Replicate I II 7.037 6.376 14.707 15.219 |++++ 1 བྱ॰༤༠སྦྱོ སྦྱོཋཏྟཱུ a b ab 11.635 12.089 17.273 17.815 с ас 10.403 10.151 4.368 4.098 bc abc 9.360 9.253 13.440 12.923 d 8.561 8.951 ad 16.867 17.052 bd 13.876 13.658 abd 19.824 19.639 cd 11.846 12.337 acd 6.125 5.904 bcd 11.190 10.935 abcd 15.653 15.053 Question 3 Continuation of Exercise 2. One of the variables in the experiment described in Exercise 2, heat treatment method (C), is a categorical variable. Assume that the remaining factors are continuous. (a) Write two regression models for predicting crack length, one for each level of the heat treatment method variable. What differences, if any, do you notice in these two equations? (b) Generate appropriate response surface contour plots for the two regression models in part (a). (c) What set of conditions would you recommend for the factors A, B, and D if you use heat treatment method C = +? (d) Repeat…arrow_forwardQuestion 2 A nickel-titanium alloy is used to make components for jet turbine aircraft engines. Cracking is a potentially serious problem in the final part because it can lead to nonrecoverable failure. A test is run at the parts producer to determine the effect of four factors on cracks. The four factors are: pouring temperature (A), titanium content (B), heat treatment method (C), amount of grain refiner used (D). Two replicates of a 24 design are run, and the length of crack (in mm x10-2) induced in a sample coupon subjected to a standard test is measured. The data are shown in Table 2. 1 (a) Estimate the factor effects. Which factor effects appear to be large? (b) Conduct an analysis of variance. Do any of the factors affect cracking? Use a = 0.05. (c) Write down a regression model that can be used to predict crack length as a function of the significant main effects and interactions you have identified in part (b). (d) Analyze the residuals from this experiment. (e) Is there an…arrow_forward
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