Homework Chapter 11-13

Homework Chapters 11-13/ 50 points Theory testing, Assumptions, Relationships 1. Should you use significance tests of skew and kurtosis in large samples? 4 pts. a. No, because they are likely to be non-significant even when skew and kurtosis are significantly different from normal. b. No, because they are likely to be significant even when skew and kurtosis are not too different from normal. c. Yes, because large samples produce more accurate results. d. Yes, because large samples add power to the test. 2. What can we say about a z -score of 1.98 in a small sample where N=14? 4 pts. a. It is significant at p < 0.05 b. It is significant at p < 0.001 c. It is significant at p < 0.01 d. It is non-significant. 3. Which of the following is true for Cohen’s d ? 4 pts. a. The units of measurement of the effect size are in their original form. b. It is not affected by sample size. c. The effect size works for continuous and categorical data. d. It tells us the position of a score relative to the mean. 4. A Pearson’s correlation coefficient of zero has been calculated. What does it mean? 4 pts. a. The two continuous variables are not correlated. b. The two continuous variables are not linearly correlated. c. The two continuous variables are not non-linearly correlated. d. The two continuous variables are strongly correlated. 5. Which of these variables would be considered not to have met the assumptions of parametric tests based on the normal distribution? ( Hint : Many statistical tests rely on data measured at the interval level.) 4 pts. a. Reaction time b. Temperature c. Gender d. Heart rate 6. What does the graph below indicate about the normality of our data? Why do you make that conclusion? 2+2 pts.

a. The P-P plot reveals that the data deviate substantially from normal. b. We cannot infer anything about the normality of our data from this type of graph. c. The P-P plot reveals that the data deviate mildly from normal. d. The P-P plot reveals that the data are normal. 7. To interpret a correlation coefficient, which of the followings need to be considered? 4 pts. a. The ± sign of the correlation coefficient. b. The magnitude of the correlation coefficient. c. The significance of the correlation coefficient. d. a. b. and c. 8. The correlation between two variables A and B is 0.12 with a significance of p < 0.01. What can we conclude? 4 pts. a. That there is a substantial relationship between A and B . b. That variable A causes variable B . c. That there is a small relationship between A and B . d. That there is no relationship between A and B. 9. What is the difference between a confidence interval and a credible interval? 4 pts. The difference between a confidence and a credible interval is that the confidence interval is the probability that a population parameter will fall between two sets of values. The confidence interval has a probability of 1 or 0 of containing the parameter. A credible interval is an interval within an unobserved parameter where the value falls with a particular probability. Usually, the percentage for the credible interval is 95%. 10. Describe the assumptions of additivity and linearity. 4 pts. Additivity assumes that if you have several predictors then their combined effect is best described by adding their effects together. Or it could be because the effect of changes from the predictor on a response is independent of the effect of changes in another predictor. Linearity assumes the outcome variable is, and linearly related to any predictors. Or the effects of several variables. 11. What is the relationship between covariance and the correlation coefficient? 4 pts.