Question:
using stat key with this dataset under baseball salaries file from the website
https://www.statsmedic.com/post/baseball-salaries-and-the-central-limit-theorem
answer these questions
(a) How do you think the shape of the sampling distribution of the sample mean will compare for n = 2 and n = 32?
(b) How do you think the mean of the sampling distribution of the sample mean will compare for n = 2 and n = 32?
(c) How do you think the standard deviation of the sampling distribution (standard error) of the sample mean will compare for n = 2 and n = 32?
6.7 (Reachability matrix for reachable canonical form) Consider a system in reach- able canonical form. Show that the inverse of the reachability matrix is given by al a2 an 0 1 a1 . . an-1 = 0 0 1 . . a1 0 0 0 . . . 6.7 (Reachability matrix for reachable canonical form) Consider a system in reach- able canonical form. Show that the inverse of the reachability matrix is given by a a2 an 0 1 a1 . . an-1 W = 0 0 1 . . a1 0 0 0 . . .8:24 46:38 Exit Which is true about the difference between the statistical power of parametric vs. non- parametric tests (i.e., the probability that each type of test will detect a significant effect when one exists)? Parametric tests will definitely have more statistical power than non- parametric tests only if the data meet the assumptions of parametric tests (such as having a normally distributed sampling distribution) Parametric tests always have more statistical power than non-parametric tests Non-parametric tests always have more statistical power than parametric tests No difference; parametric and non- parametric tests have equivalent statistical power for any given set of data 11. When running a non-parametric test on5. The residuals from the wage-productivity regression were regressed on lagged residuals going back six periods (i.e. AR (6)) thereby yielding the following results: Dependent Variable: RESI Method: Least Squares Sample (adjusted) : 1965-1998 Included Observations: 34 after adjusting endpoints Variable Coefficient Std. Error t-Statistic Prob. 5. 590462 1.963603 2.847043 0. 0085 -0. 066605 0. 023469 -2. 838058 0. 0087 RESI (-1) 0. 814971 0. 216231 3.768978 0. 0009 RES1 (-2) -0. 268651 0. 273887 -0.980882 0.3357 RES1 (-3) -0. 106017 0. 272780 -0. 388652 0.7007 RES1 (-4) 0. 305630 0. 273258 1. 118467 0.2736 RES1 ( -5) -0. 064375 0. 280577 -0. 229438 0. 8203 RES1 (-6) 0. 216156 0. 222160 0.972976 0.3395 R? = 0. 8920 Durbin-Watson d stat 1. 7589 R = 0. 8629 a. What can you say about the nature of autocorrelation in the above regression result? b. If you think that an AR (1) mechanism characterizes autocorrelation, would you use the first difference transformation to get rid of autocorrelation? Justify your answer.Question 1 {1 point) X Saved Normal Distributions are required for: O Parametric Statistics 0 NonParametric Statistics 6) Regression Analysis 0 Parametric Statistics & Regression Analysis 0 All of the other answers Which of the following are true of binary classication/ regression trees? i. Bagging decision trees is likely to increase the model variance. ii. The deeper the decision tree is, the more likely it is to overt. iii. They are robust to small changes in the data. iv. Random forests are less likely to overt than decision trees
