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- Question #1 You observe the following data that represents the average outcomes of two groups of individualsthose who received an intervention ("Treated") and those who did not (“Not Treated")-over two periods before the treated group received the intervention and after. Group Treated Not Treated Before After 75 30 40 20 Compute the pre-/post- (BTs), cross section (Bxs), and difference-in-differences (Bpp) estimators of the causal impact of treatment.FACTORS FOR I AND R CONTROL CHARTS Observations in Sample, n dz A2 dz D3 D4 2 1.128 1.880 0.853 3.267 3 1.693 1.023 0.888 2.574 4 2.059 0.729 0.880 2.282 5. 2.326 0.577 0.864 2.114 6. 7. 2.534 0.483 0.848 2.004 2.704 0.419 0.833 0.076 1.924 8. 2.847 0.373 0.820 0.136 1.864 2.970 0.337 0.808 0.184 1.816 10 3.078 0.308 0.797 0.223 1.777 11 3.173 0.285 0.787 0.256 1.744 12 13 3.258 3.336 3.407 0.266 0.249 0.235 0.778 0.770 0.763 0.283 1.717 0.307 1.693 1.672 1.653 14 0.328 15 3.472 0.223 0.756 0.347 3.532 0.212 0.750 0.744 0.739 0.363 0.378 0.391 16 1.637 17 18 3.588 0.203 1.622 3.640 0.194 1.608 19 20 3.689 3.735 0.187 0.734 0.403 1.597 0.180 0.729 0.415 1.585 21 22 3.778 3.819 1.575 1.566 0.173 0.724 0.425 0.434 0.167 0.720 23 24 25 3.858 3.895 3.931 0.162 0.157 0.153 0.716 0.712 1.557 1.548 0.443 0.451 0.708 0.459 1.541Group I Given N1 = 20 E(X- 8) = 10 E(X-8)? = 152 50. Group II N2 = 30 (Y- 10) = - 15 E(Y-10)2 = 300 %3D %3D %3D Find arithmetic mean and standard deviation of each group and the combined arithmeti nean, standard deviation and the overall coefficient of variation.
- 10. You estimated a regression with the following output. Source | SS df MS Number of obs = 333 -------------+---------------------------------- F(1, 331) = 4608.21 Model | 32636494.1 1 32636494.1 Prob > F = 0.0000 Residual | 2344225.8 331 7082.25316 R-squared = 0.9330 -------------+---------------------------------- Adj R-squared = 0.9328 Total | 34980719.9 332 105363.614 Root MSE = 84.156 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 30.79902 .4537022 67.88 0.000 29.90652 31.69153 _cons | 20.85313 42.1964 0.49 0.621 -62.1538 103.8601…QUESTION 20 A treatment for anxiety is found to demonstrate a consistent reduction of anxiety sympotms by 2 points on a 50- point anxiety symptoms scale. Although patients overall do not report feeling much difference, the experiemnter finds that the change in symptoms on the anxiety symptoms scale results in a p-value of less than .05. This is an example of A statitically significant effect that does not have clinical/practical significance A clinically significant change which is supported by a statistically significant effect A correlational research design A placebo effect6.1-11
- need ansQUESTION 3 A study is done to find out whether there is a relationship between a woman's age and the amount of money she has saved (in RM'000). A random sample of data was collected from fifteen women in a residential area. The woman's age ranges from twenty-five to fifty-nine. Below are the results using MS Excel : Table 2 Regression Statistics Multiple R R Square 0.986 R2 Adjusted R Square 0.969 Standard Error 1.098 Observations 15 Table 3 Standard Coefficients Error t Stat P-value Intercept -4.488 1.123 -3.996 0.002 Variable 1 0.580 0.028 0.000 20.999 a. State the independent and dependent variables. b. Interpret the value of the correlation coefficient obtained in Table 2. С. Compute the coefficient of detemination and explain its meaning. Determine the regression equation of amount of money saved on woman's age. е. Comment on the value of the coefficient, b, in the regression equation in (d). f. Predict the amount of money saved for a woman whose age is i. 39 ii. Is the prediction…The values under n, N, and (n/N,)x 100% for the following table are not real, they are presented here for reasons of clarity. Table 1: Age distribution. Age Group Number of Patients Size of Age Group in Catchment A rea (N,) Proportion (n/ N,)x100% (n) 0- 4 5 - 14 15 - 44 45 - 59 2 60 925 4675 5762 19.75 15.63 58.08 901 4213 7253 23.93 13.84 153 635 76 549 i. Explain why the use of the proportions (n,/ N, )x100%, i= 1, 2,. .. 5, in the construction of the multiple bar chart is comparatively better than the use of the number of patients, (n,). ii. Assuming you are the bio-statis tician, use the multiple bar chart to write a report to management Be guided by the purpose for which multiple bar charts are used.