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Question 1 of 4 1.0 Points You have been given sample data from two offices and told that the 95% confidence interval for the difference of mean in overtime hours per year is -.01 to 100. What can you say about the difference in average overtime hours for each office? A. Overtime hours are statistically significant at alpha= 05 B. Overtime hours are not statistically significant at a 95% confidence level. C. Overtime hours are practically significant. D. We can't tell from the data. Question 2 of 4 1.0 Points What if told you in Q1 above that the sample sizes were n=20 and n=30 for each office, what would you say then? A.A sample size of 30 is the minimum sample size to make the law of large numbers work, so we cannot draw conclusions from this data B. Use a t-score for the smaller sample and z-score for the larger one. C. If the sample sizes were increased, we would likely see statistically significant difference at the 95% confidence level. D and C B and C Question 3 of 4 1.0 Points You have conducted a pilot study of new initiative to improve employee morale, using experimental design on samples of employees, and you have found that ina regression equation morale has improved by points out of 10, with a p-value of .07. What can you say about your pilot study? B. My regression coefficient does not meet standards for statistical significance, and on that basis cannot draw firm conclusions about my innovation. c. The confidence interval for morale likely includes 0. D.A and C. E. All of the above. Reset Selection Question 4 of 4 1.0 Points For the examples below comparing Boys' and Girls Mean Test Scores, match the example to the proper qualitative description. (Pay attention to sample size, difference in scores, and measures of significance). T-scores are given throughout for comparability A. We're sure that there is no difference of magnitude large enough to matter. B. An important difference that's really there. C. Could be large, important difference, but we have no idea. Not enough data to tell. D. Lots of data make small, unimportant difference statistically significant. 1. Example A Sample 10,000 Size Overall 200 Mean Std. Dev. 25 Girls' Mean 175 Score select Boys' Mean 225 Score Difference 50 in Score Standard =2xSD/sqrt(10,000) Error of =2x25/sqrt(10,000) the =.5 Difference t-score = 100 and << .0001 p-value Example B Sample 10.000 Size Overall 200 Mean Std. Dev. 25 Girls' Mean 199 Score Boys' select Mean 201 Score Difference in Score Standard =2xSD/sqrt(10,000) Error of 2225/sqrt(10,000) the -.5 Difference t-score =4 and p<.001 p-value 3. Example C Sample Size 9 Overall Mean 200 Std. Dev. 100 Girls' Mean 175 Score select Boys' Mean 225 Score Difference in 50 Score Standard =2xSD/sqrt(9) Error the =2x100/sgrt(9) Difference -66.6 t-score and =.75 p-value p>.40 4. Example D Sample 1,000 Size Overall 200 Mean Std. Dev. 25 Girls' Mean 199 Score select Boys' Mean 201 Score Difference 2 in Score Standard =2xSD/sqrt(1,000 Error -2x25/sqrt(1,000 the =1.58 Difference t-score 1.26 land p-value

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