11. The closer the hypothesized mean is to the actual mean the greater the power
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11. The closer the hypothesized mean is to the actual mean the greater the power of the test. 12. The manager of the quality department for a tire manufacturing company wants to know the population standard deviation and uses a Z test to test the null hypothesis that the mean tensi strength is 800 pounds per square inch. The calculated Z test statistic is a positive value that Icads to a p-value of .067 for rejected. Assume that the population of pressure le the test. If the significance level is 01, the null hypothesis would be values is normally distributed. 13. The smaller the p-value, the more we doubt the null hypothesis 14. You cannot make a Type II error when the null hypothesis is true. 15. A Type II error is rejecting a true null hypothesis. 16. When conducting a hypothesis test about a single mean, other relevant factors held constant, increasing the level of significance from.05 to .10 will reduce the probability of a Type I error. 17. When conducting a hypothesis test about a single mean, other relevant factors held constant, increasing the level of significance from.05 to .10 will reduce the probability of a Type Il error. 18. The null hypothesis always includes an equal ()sign. 19. When the null hypothesis is true, there is no possibility of making a Type I error. Chapter 13 20. The error term is the difference between an individual value of the dependent variable and the corresponding mean value of the dependent variable. 21·The residual is the difference between the observed value of the dependent variable and the predicted value of the dependent variable. 22. The slope of the simple linear regression equation represents the average change in the value of the dependent variable per unit change in the independent variable (X). 23. A significant positive correlation between X and Y does not imply that changes in X cause Y to change. 24. The correlation coefficient is the ratio of explained variation to total variation. 25. When using simple regression analysis, if there is a strong correlation between the independent and dependent variable, then we can conclude that an increase in the value of the independent variable causes an increase in the value of the dependent variable.Explanation / Answer
I presume these are true or False questions. The answers are as below:
11. The closer the hypothesized mean is to the actual mean the greater the power of the test. FALSE. The further the hypothesized mean is from the actual mean the greater the power of the test.
12. p-value of 0.067 for the test. If the significance level is 0.01. Null hypothesis can't be rejected. Hence FALSE.
13. The smaller the p-value, the more we doubt the null hypothesis. TRUE
14. You cannot make a Type II error when the null hypothesis is true. TRUE.
15. A Type II error is rejecting a true null hypothesis. FALSE. This is Type I error.
16. When conducting a hypothesis test about a single mean, other relevant factors held constant, increasing the level of significance from 0.05 to 0.10 will reduce the probability of a Type I error. FALSE.
17. When conducting a hypothesis test about a single mean, other relevant factors held constant, increasing the level of significance from 0.05 to 0.10 will reduce the probability of a Type II error. TRUE.
18. The null hypothesis always includes an equal (=) sign. TRUE.
19. When the null hypothesis is true, there is no possibility of making a Type I error. FALSE. When the null hypothesis is true and you reject it, you make a type I error. If the null hypothesis is false, then it is impossible to make a Type I error.
20. The error term is the difference between an individual value of the dependent variable and the corresponding mean value of the dependent variable. FALSE. It is the difference between an individual value of the dependent variable and the corresponding predicted value (not the mean value).
21. The residual is the difference between the observed value of the dependent variable and the predicted value of the dependent variable. TRUE
22. The slope of the simple linear regression equation represents the average change in the value of the dependent variable per unit change in the independent variable (X). TRUE.
23. A significant positive correlation between X and Y doesn't imply that changes in X cause Y to change. TRUE. Change in X will cause change in Y only if X is the independent variable and Y is the dependent variable.
24. The correlation coefficient is the ratio of the explained variation to total variation. This is FALSE.The coefficient of determination is the ratio of the explained variation to the total variation.
25. This is FALSE. The strong correlation could be negative, so an increase in independent variable could cause a decrease in the dependent variable.
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