The following data represent the results from an independent-measures study comparing two treatmentconditions.Conduct ANOVA with a=Treatment TreatmentOneTwo3.34.64.83.75.125.224.45.9546.161.74.70.05 for this data and calculate the F-ratio and p-value. Round your answer tothree decimal places. Assume all population and ANOVA requirements are met.F-ratio:p-value:Now, conduct a t-test with a 0.05 on the same data and calculate the t-statisitc and p-value. Roundyour answer to three decimal places.t-statistic:p-value:Observing the p-values, was there a difference in the result of the hypothesis test?O Yes, the p-values are different enough to change the outcome of the test.O No, the p-values aren't that far off where it would change the outcome of the test.Submit Question

F-ratio = 0.216 p-value = 0.649 t stat = 0.465 p-value = 0.649 Explanation:Step 1: F-ratio =…

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Dex Research Limited conducted a research to investigate consumer characteristics that can be used to predict the amount charged by credit card users. The following multiple regression output is based on a data collected by this research company on annual income, household size and annual credit card charges for a sample if 50 consumers. Regression Statistics Multiple R 0.9086 R Square A Adjusted R Square 0.8181 Standard Error 398.091 Observations B ANOVA df SS MS F Significance F Regression 2 C E G 1.50876E-18 Residual D 7448393 F Total 49 42699149 Coefficients Standard Error t Stat P-Value Intercept 1304.9048 197.6548 6.6019 3.28664E-08 Income ($1000s) 33.1330 3.9679 H 7.68206E-11 Household size 356.2959 33.2009 10.7315 3.12342E-14 a. Complete the missing entries from A to H in this outputb. Estimate the annual credit card charges…
6. The following results were obtained from a multiple regression analysis. How many observations were involved in this study? A. 25 B. None C. 9 D. 12 E. 30
The following data represent the results from an independent-measures study comparing two treatment conditions. Treatment One Treatment Two 4.1 5.1 3.8 3.8 1.9 2.1 5.2 3.6 5.4 5.9 4.3 2.9 Conduct ANOVA with a = 0.05 for this data and calculate the F-ratio and p-value. Round your answer to three decimal places. Assume all population and ANOVA requirements are met. F-ratio: p-value: Now, conduct a t-test with a = 0.05 on the same data and calculate the t-statisitc and p-value. Round your answer to three decimal places. t-statistic: p-value: Observing the p-values, was there a difference in the result of the hypothesis test? O Yes, the p-values are different enough to change the outcome of the test. O No, the p-values aren't that far off where it would change the outcome of the test.
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The following data represent the results from an independent-measures study comparing two treatment conditions. Treatment Two Treatment One 3.4 3.2 4.3 4.3 4.9 4.4 2.7 5.5 3.4 4.1 3.8 4.3 4.1 3.6 4.3 2.2 Conduct ANOVA with a = 0.05 for this data and calculate the F-ratio and p-value. Round your answer to three decimal places. Assume all population and ANOVA requirements are met. F-ratio: p-value: Now, conduct a t-test with a = 0.05 on the same data and calculate the t-statisitc and p-value. Round your answer to three decimal places. t-statistic: p-value:
The following data represent the results from an independent-measures study comparing two treatment conditions. Treatment Treatment One Two 6.9 5.2 8.2 2.2 3.7 1.7 7.6 7.4 3.4 Conduct ANOVA with a = 0.05 for this data and calculate the F-ratio and p-value. Round your answer to three decimal places. Assume all population and ANOVA requirements are met. F-ratio: p-value: Now, conduct a t-test with a = 0.05 on the same data and calculate the t-statisitc and p-value. Round your answer to three decimal places. t-statistic: p-value: Observing the p-values, was there a difference in the result of the hypothesis test? O No, the p-values aren't that far off where it would change the outcome of the test. O Yes, the p-values are different enough to change the outcome of the test.
A company manufactures printers and fax machines at plants located in Atlanta, Dallas, and Seattle. To measure how much employees at these plants know about quality management, a random sample of 6 employees was selected from each plant and the employees selected were given a quality awareness examination. The examination scores for these 18 employees are shown in the following table. The sample means, sample variances, and sample standard deviations for each group are also provided. Managers want to use these data to test the hypothesis that the mean examination score is the same for all three plants. Sample mean Sample variance Sample standard deviation Plant 1 Atlanta 84 76 81 76 70 81 78 25.2 5.02 Plant 2 Plant 3 Dallas Seattle 72 74 72 74 70 88 75 42.8 6.54 60 64 62 68 76 66 66 32.0 5.66
Refer to the below table. Using an alpha = 0.05, test the claim that IQ scores are the same for children in three different blood lead level groups: low lead level, medium lead level, and high lead level). One-Way Analysis of Variance Summary Table for IQ Measurements for Children among Three Blood Lead Level Groups: Low Lead Level, Medium Lead Level, and High Lead Level. Source df SS MS F p Between-group (treatment) 2 469.1827 2677.864 2.30 0.104 Within-group (error) 118 203.6918 11745.05 Total 120
Answer the Question 1. In multiple regression: a. the ANOVA table tells us whether the three main regression coefficients (R, R-square, adjusted R-square) are significantly different from 1. .50 2. 1.00 3. Zero 4. 1.50 b. the adjusted R-square can be interpreted as 1. Th percentage of variance accounted for in the dependent variable by the set of independent variables 2. The percentage of variance accounted for in the dependent variable by the set of independent variables minus an estimate penalty 3. The percentage of variance accounted for in the dependent variable by a single independent variable 4. The strength of a relationship between the dependent variable and the set of independent variables

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