MEANS OF TWO SAMPLES (VARIANCES UNKNOWN) AND OBSERVED OBSERVATIONSGive the complete Test of Hypothesis 2. Adult males between the ages of 35 and 50 participated in a study to evaluate the effect ofdiet and exercise on blood cholesterol levels. The total cholesterol was measured in eachsubject initially and then three months after participating in an aerobic exercise program andswitching to a low-fat diet. The data are shown in the accompanying table. Do the datasupport the claim that low-fat diet and aerobic exercise are of value in producing a meanreduction in blood cholesterol levels? Use a = 5%.Blood Cholesterol LevelBlood Cholesterol LevelBeforeAfterBeforeAfter249241265229239220240231238210260242235215255234247248250248245232230228225226260235247233

Answered: Image /qna-images/answer/125e0e72-d19c-4153-b276-76a4ea5bbc89.jpg

Answered: 2. Adult males between the ages of 35…

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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Relative to a balanced ANOVA an unbalanced ANOVA: Is typically more sensitive to non-uniform variances Has a smaller power All of these answers are correct. Does not have the same number of observations for each treatment An important difference between fixed- and random-effects models is: Fixed-effects models have no variance Random-effects models provide a better estimate of the variance Random-effects models are more reliable Fixed-effects models apply only to the selected treatments
Two-Sample Tests for Proportions What are the limitations of two-sample tests of proportions? Do these limitations present serious problems? Can the use of multiple confidence intervals for proportions overcome these limitations?
How will you test the difference between the variances? * No need to test differences. Standard error of the difference between means Standard error of the mean difference F-test T-test ANOVA single factor for uncorrelated samples
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When we conduct a hypothesis test to compare two population means with known population standard deviations, which test statistic do we use? z test statistic t test statistic p test statistic Kai-square test statistic
The lifetime of a light bulb is normally distributed with an average life of 500 hours and a standard deviation of 100 hours. If a company installs 5000 of these light bulbs, find the number of bulbs that can be expected to last between 500 hours and 675 hours.
true or false ? 1- If the large variance is combined with the small sample size and the small variance with the large sample size, this leads to a reduction in the error of the first type and the null hypothesis is accepted, then the decision is correct. 2- Test hypotheses and statistical estimation with a period of time achieve the same purpose 3- The strength of a statistical test increases with the increase in the level of statistical significance
Independent random samples were selected from two quantitative populations, with sample data given below. Using the p-value approach for the data given below, is there sufficient evidence to show that u, is larger than H, at the 1% level of significance? n, = n, = 50, x, = 125.1, x, = 123.8, S1 = 5.7, S2 = 6.9 In USE SALT Use the value of the test statistics 1.03 to calculate the p-value for the test. (Round your answer to four decimal places.) p-value = Enter a number. State your conciusion. O H, is rejected. There is sufficient evidence to indicate that the mean for population 1 is larger than the mean for population 2. O H, is rejected. There is insufficient evidence to indicate that the mean for population 1 is larger than the mean for population 2. O H, is not rejected. There is sufficient evidence to indicate that the mean for population 1 is larger than the mean for population 2. O H, is not rejected. There is insufficient evidence to indicate that the mean for population 1 is…
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What graph do we use the check if the assumption of constant variance of error is valid? (check Var(8) = o) O Sscatterplot ofY vs. X1 O Histogram of Residuals O Normal Probability Plot O Residuals vs. Fitted Values

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