1. If the population is normally distributed then the sample mean is also normally distributed even for small sample size
2. The level of significance indicates the probability of rejecting a false null hypothesis.
3. An estimator is called consistent if its variance and standard deviations consistently remain the same regardless of changes in the sample size
4. We do not need to perform the continuity correction if the population is 20 times or more than the sample size
5. When the level of confidence and sample standard deviation remain the same, a confidence interval for a population mean based on a sample of n=100 will be narrower than a confidence interval for a population mean based on a sample of n=50.
6. For a continuous distribution, the exact probability of a particular value is always zero.
7. A fastener manufacturing company uses a chi-square goodness of fit test to determine if a population of all lengths of ¼ inch bolts it manufactures is distributed according to a normal distribution. If we reject the null hypothesis, it is reasonable to assume that the population distribution is at least approximately normally distributed.
8. The larger the p-value, the more the chance of rejecting the null hypothesis.
9. When the population is normally distributed and the population standard deviation s is unknown, then for any sample size n, the sampling distribution of Xbar is based on the t distribution.
10. When determining the sample size n, if the value found for n is 79.2, we would choose to sample 79 observations.
11. In a regression model, at any given combination of values of the independent variables, the population of potential error terms is assumed to have an F-distribution.
12. The chi-square distribution is a continuous probability distribution that is skewed to the left.
13. The correlation coefficient is the ratio of explained variation to total variation.
14. The error term is the difference between the observed value of the dependent variable and the predicted value of the dependent variable.
15. The error term in the regression model describes the effects of all factors other than the independent variables on y (response variable).