A manufacturer of television sets is interested in the effect on tube conductivity of fourdifferent types of coating for colour picture tubes. The following conductivity data areobtained. Assume that all assumptions to perform an ANOVA test are satisfied.Coating TypeConductivity1411491143150146215213714331341361321274129127132129(a)Is there a difference in conductivity due to coating type? Use a = 0.05.(b)(c)Compute a 95% interval estimate of the mean of coating type 4.Compute a 99% interval estimate of the mean difference between coating types 1 and4.(d)(e)Test all pairs of means using Duncan's multiple range test. Use a = 0.05.Assuming that coating type 4 is currently in use, what are your recommendationsto the manufacturer? We wish to minimize conductivity.

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Answered: A manufacturer of television sets is…

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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Five samples of a ferrous-type substance were used to determine if there is a difference between a laboratory chemical analysis and an X-ray fluorescence analysis of the iron content. Each sample was split into two subsamples and the two types of analysis were applied, with the accompanying results. Assuming that the populations are normal, test at the 0.05 level of significance whether the two methods of analysis give, on the average, the same result. Determine the test statistic t=?
The data and the source table below are from a Repeated-Measures ANOVA. Using this information, Calculate the HSD value for a Tukey’s HSD test – including looking up the value of q from the table. Use α = .01. Condition 1 Condition 2 Condition 3 50 52 55 48 48 50 50 54 53 52 51 54 SOURCE SS df MS Fobt Between 18.17 2 9.08 5.19 Within 40.75 9 Participants 30.25 3 Error 10.50 6 1.75 Total 58.92 11 Group of answer choices q = 4.34, HSD = 3.05 q = 4.34, HSD = 2.87 q = 6.33, HSD = 4.19 q = 6.33, HSD = 4.44
Five samples of a ferrous-type substance were used to determine if there is a difference between a laboratory chemical analysis and an X-ray fluorescence analysis of the iron content. Each sample was split into two subsamples and the two types of analysis were applied, with the accompanying results. Assuming that the populations are normal, test at the 0.02 level of significance whether the two methods of analysis give, on the average, the same result
Suppose the National Transportation Safety Board (NTSB) wants to examine the safety of compact cars, midsize cars, and full-size cars. It collects a sample of three for each of the treatments (cars types). Using the hypothetical data provided below, test whether the mean pressure applied to the driver’s head during a crash test is equal for each types of car. Use α = 5%. Compact Midsize Full-size 643 655 702 680 672 469 427 525 495 483 484 465 402 476 435 State the null and alternate hypothesis for the data. Given the results, what will be the conclusion of the team?
Suppose the National Transportation Safety Board (NTSB) wants to examine the safety of compact cars, midsize cars, and full-size cars. It collects a sample of three for each of the treatments (cars types). Using the hypothetical data provided below, test whether the mean pressure applied to the driver’s head during a crash test is equal for each types of car. Use a significant level of 5%. Compact cars (643; 655; 702). Mid-size cars (469; 427; 525). Full-size cars (484; 456; 402). SST = 86049.556. TSS = 96303.556. State the appropriate hypotheses. A. H0: μ1 = μ2 = μ3 vs H1: At least 2 of the means differ B. Ho: μ1 ≠ μ2 ≠μ3 vs H1: μ1 = μ2 = μ3 C. Ho: At least 2 of the means differ vs H1: μ1 = μ2 = μ3 D. H1: μ1 = μ2 = μ3 vs Ho: At least 2 of the means differ
Suppose the National Transportation Safety Board (NTSB) wants to examine the safety of compact cars, midsize cars, and full-size cars. It collects a sample of three for each of the treatments (cars types). Using the hypothetical data provided below, test whether the mean pressure applied to the driver’s head during a crash test is equal for each types of car. Use a significant level of 5%. Compact cars (643; 655; 702). Mid-size cars (469; 427; 525). Full-size cars (484; 456; 402). SST = 86049.556. TSS = 96303.556. What conclusion can be drawn from this scenario? A. Do not Reject H0 and conclude that all three populations have different mean pressure. B. Reject H0 and conclude that at least two of populations differs from the others in terms of their mean pressure. C. Reject H0 and conclude that there is insufficient evidence for differences in mean pressure for the three populations. D. Do not reject H0 and conclude that the mean pressure in these three populations are not the…
Suppose the National Transportation Safety Board (NTSB) wants to examine the safety of compact cars, midsize cars, and full-size cars. It collects a sample of three for each of the treatments (cars types). Using the hypothetical data provided below, test whether the mean pressure applied to the driver’s head during a crash test is equal for each types of car. Use a significant level of 5%. Compact cars (643; 655; 702). Mid-size cars (469; 427; 525). Full-size cars (484; 456; 402). SST = 86049.556. TSS = 96303.556. What is the critical value (rounded off 2 decimals)? A. 5.65 B. 5.12 C. 5.14 D. 5.25
This question depends on the following two-way ANOVA tables which analyzes how a student's grades are related to the amount of coffee and sleep the student has. Df Sum Sq Mean Sq F value Pr(>F) SleepTime 2 6840 3420 26.467 2.38e-07 Coffee 4 1035 259 2.002 0.120 SleepTime:Coffee 8 914 114 0.884 0.541 Residuals 30 3877 129 Df Sum Sq Mean Sq F value Pr(>F) Sleep Time 2 6480 3420 27.127 4.8e-08 Coffee 4 1035 259 2.052 0.106 Residuals 38 4791 126 At 5% significance level, do the data support that SleepTime has a main effect on average score? A. Yes B. No C. Impossible to know D. It is unlikely At 5% significance level, do the data support that Coffee has a main effect on average score? A. Yes B. No C. Impossible to know D. It is unlikely At 5% significance level, do the data support that SleepTime and Coffee interact to have an effect on average score? A. Yes B. No C. Impossible to know D. It is unlikely
Suppose the National Transportation Safety Board (NTSB) wants to examine the safety of compact cars, midsize cars, and full-size cars. It collects a sample of three for each of the treatments (cars types). Using the hypothetical data provided below, test whether the mean pressure applied to the driver’s head during a crash test is equal for each types of car. Use α = 5%. Compact cars Midsize cars Full-size cars 643 469 484 655 427 456 702 525 402 Mean 666.67 473.67 447.33 Standard deviation 31.18 49.17 41.68
Compute a one-way ANOVA on the following data using 0.05 significance level. Group A Group B Group C 14 11 14 13 12 12 10 13 13 12 13 12 14 ANOVA Source of Variation SS df MS F P-value F crit Between Groups ? ? ? ? ? Within Groups ? ? ? Total ? ?
Five samples of a ferrous-type substance were used to determine if there is a difference between a laboratory chemical analysis and an X-ray fluorescence analysis of the iron content. Each sample was split into two subsamples and the two types of analysis were applied, with the accompanying results. Assuming that the populations are normal, test at the 0.10 level of significance whether the two methods of analysis give, on the average, the same result.
Suppose the National Transportation Safety Board (NTSB) wants to examine the safety of compact cars, midsize cars, and full-size cars. It collects a sample of three for each of the treatments (cars types). Using the hypothetical data provided below, run your analyses in R to test whether the mean pressure applied to the driver’s head during a crash test is equal for each types of car (higher pressure = less safe). Compact Car Midsize Car Full-Size Car 643 469 484 655 427 456 702 525 402 What kind of statistical analysis do you need to use and why? State the null and alternative hypotheses. Check your assumptions. Include screenshots of your R output and explain why each of them are or are not met. Using R, run your analysis and include a screenshot. Using R, run post-hoc tests and include a screenshot. What do you conclude about the null hypothesis? What does each of your p-values tell you? Are there differences and in which comparisons?…

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