1:58 . 5G+ 3) Below I am defining two scenarios. I want you to tell me the conclusion you would reach as a result of the information provided and whether you made a type 1 error, type 2 error or no error and your reasoning. For instance an acceptable answer is: I made a type 1 error but type 2 error is not possible because ..... (you need to give an explanation here) Scenario 1: You create a column of data in 200 rows using a random Poisson distribution (mean 4). You follow this up by creating 198 columns of randomly generated (say Uniform 0-10) data. You then run a regression model where the column with the Poisson distribution is the dependent variable and the 198 columns are the independent variables. You look at the regression summary output and see the p value of the f-statistic to be smaller than any reasonable alpha value. a) The conclusion you would reach is (please do not just say reject/ do not reject null hypothesis, explain what it is you are rejecting not rejecting) : b) Did you commit type 1/type 2 error/no error (and explain): Scenario 2: You create a column of data in 200 rows using a random Poisson distribution (mean 4). You follow this up by creating 198 columns of randomly generated (say Uniform 0-10) data. You then run a regression model where the column with the Poisson distribution is the dependent variable and the 198 columns are the independent variables. You look at the regression summary output and see the p value of the f-statistic to be larger than any reasonable alpha value. c) The conclusion you would reach is (please do not just say reject/ do not reject null hypothesis, explain what it is you are rejecting not rejecting) : d) Did you commit type 1/type 2 error/no error (and explain): 4) This question is 10 pts Total Dumreps=1000 for(i in numbers) { epsilon_normal=orm(1000mean=0,d=sqrt(10)) X=rnorm(1000 mean=1,sd=10) Y_normal=5*X+epsilon_normal EK_normal[i]=oX(X,Y_normal) o_normal=(Im(Y_normal~X)) int_normal=predict(o normal interval="prediction") above_normal[i]=sum(Y_normal>int_normal[,3]) below_normal[i]=sum(Y_normal int_gamma[,3]) below_gamma[i]=sum(Y_gamma 1:58 M2 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 28605.33 1047.28 27.314 < 2e-16 *** 406.22 77.79 5.222 4.42e-07 *** factor(JobGrade)2 2667.72 1180.04 2.261 0.0249 Ycs Exper factor(JobGrade)3 6420.38 1165.81 5.507 1.11e-07 *** factor(JobGrade)4 10524.67 1367.86 7.694 6.36e-13 *** factor(JobGrade)5 16115.28 1557.68 10.346 < 2e-16 *** factor(JobGrade)6 27450.30 2412.98 11.376