TRUE OR FALSE:
1. The sampling distribution of (X-bar) is always a normal distribution according to the Central limit theorem.
4. If the sampled population is a normal distribution, then the
sampling distribution of (X-bar is normal only for a
large enough sample size.
5. If p=.8 and n=50, then we can conclude that the sampling distribution of the proportions is approximately a normal distribution.
8. Assuming the same level of significancea, as the sample size increases, the critical t-value becomes smaller but remains larger than Z-value.
9. When constructing a confidence interval for a sample Mean, the t distribution is appropriate whenever the sample size is small, whether population standard deviation is known or not.
14. 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 .055 for the test. If the significance level is .05, the null hypothesis would be rejected.
15. The larger the p-value, the less we doubt the null hypothesis.
19. Everything else being constant, increasing the sample size decreases the probability of committing a Type II error.
21. The Coefficient of Determination does not show the direction of relationship between the dependent and the independent variables.
24. 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.
25. In simple linear regression analysis, if the error terms exhibit a positive or negative autocorrelation over time, then the assumption of constant variance is violated.
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