Part II – True/False (15 marks, 1 mark each) VANESSA
1. In a simple regression model, if the regression model is deemed to be statistically
significant, it means that the regression slope coefficient is significantly greater than
2. In a hypothesis test, the p-value measures the probability that the alternative
hypothesis is true.
3. If a hypothesis test is conducted for a population mean where only non-negative
values can be sampled, a null and alternative hypothesis of the form: H0 : μ = 100, Ha : μ
100, will result in a one-tailed hypothesis test since the statistic can only assume non-
4. Two variables have a correlation coefficient that is very close to zero. This means
that there is no relationship between the two variables.
5. All other things held constant, increasing the level of confidence for a confidence
interval estimate for the difference between two population means will result in a wider
confidence interval estimate.
6. The method used in regression analysis for incorporating a categorical variable (no.
of categories = 5) into the model is by organizing the categorical variable into five
7. In a recent one-way ANOVA test, Mean SSW was equal to 1,590 and the Mean SSB
was equal to 310. Therefore, SST is equal to 1,900.
8. A local medical center has advertised that the mean wait for services will be less
than 15 minutes (but more than 0 minutes). Given this claim, the hypothesis test for the
population mean should be a one-tailed test with the rejection region in the lower (left-
hand) tail of the sampling distribution.
9. Consider the following regression equation: ŷ = 356 + 18.0x1 – 2.5x2. The x1 variable
is a quantitative variable and the x2 variable is a dummy with values 1 and 0. Given this,
we can interpret the slope coefficient on variable x2 as follows: holding x1 constant, if the
value of x2 is changed from 1 to 0, the average value of y will increase by 2.5 units.
10. The coefficient of determination measures the percentage of variation in the
independent variable that is explained by the dependent variables in the model.
11. A perfect correlation between two variables will always produce a correlation
coefficient of +1.0.
12. The prediction interval developed from a simple linear regression model will be at its
narrowest point when the value of x used to predict y is equal to the mean value of x.
13. When testing a hypothesis about the variability of a population, the statistical
requirements call for us to convert the variance to standard deviation and run a chi-
14. When the expected cell frequencies are smaller than 30, the cells should be
combined in a meaningful way such that the expected cell frequencies do exceed 30.
15. If it is known that a simple linear regression model explains 56 percent of the
variation in the dependent variable and that the slope on the regression equation is
negative, then we also know that the correlation between x and y is approximately (-
16. In estimating the difference between two population means, if a 95 percent
confidence interval includes zero, than we can conclude that there is a 95 percent
chance that the difference between the two population means is zero.
17. In a multiple regression analysis, even if only some of the independent variables
have values equal to zero, the regression intercept, b0, can still be meaningful.