1. The further the hypothesized mean is from the actual mean the lower is the power of the test.

2. The manager of the quality department for a tire manufacturing company wants to know the average tensile strength of rubber used in making a certain brand of radial tire. She knows the population standard deviation and uses a Z test to test the null hypothesis that the mean tensile strength is 800 pounds per square inch. The calculated Z test statistic is a positive value that leads to a p-value of .047 for the test. If the significance level is .05, the null hypothesis would be rejected. Assume that the population of pressure values is normally distributed.

3. The larger the p-value, the more we doubt the null hypothesis.

4. You cannot make a Type I error when the null hypothesis is true.

5. 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 II error.

6. When the null hypothesis is false, you can make Type II error.

7. The error term is the difference between the actual value of the dependent variable and the corresponding mean value of the dependent variable.

8. The Coefficient of Determination shows the direction of relationship between the dependent and the independent variables.

9. The intercept 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).

10. The correlation coefficient is the ratio of explained variation to total variation.

11. Even if there is a strong correlation between the independent and dependent variable, we may not expect that an increase in the value of the independent variable is associated with an increase in the value of the dependent variable.