3 answered questions are shown below. The stata16 data only shows final results with no equations, processes, or definitions. Replicate the solutions in Microsoft Excel, JMP Pro 15, or by hand if you want with clear annotations for equations, variables, and steps so that I can follow along. Final results by themselves --like what is shown below-- are useless. Thank you.

1. A random sample of 500 adult residents of Maricopa County indicated that 385 were in favor of increasing the highway speed limit to 75 mph, and another sample of 400 adult residents of Pima County indicated that 267 were in favor of the increased speed limit. a. Do these data indicate that there is a difference in the support for increasing the speed limit for the residents of the two counties? Use a = 0.05. What is the P-value for this test? b. Construct a 95% confidence interval on the difference in the two proportions. Provide a practical interpretation of this interval. Answers:- A. . prtesti 500 385 400 267, count Two-sample test of proportions x: Number of obs = 500 y: Number of obs = 400 Mean Std. Err. P>|z| [95% Conf. Interval ] . 77 . 0188202 7331131 - 8068869 .6675 . 0235555 .6213322 .7136678 diff .1025 . 0301506 0434059 - 1615941 under Ho: .0299718 3.42 0.001 diff = prop(x) - prop(y) 3.4199 Ho: diff = 0 Ha: diff 0 Pr (Z |z|) = 0.0006 Pr(Z > z) = 0.0003 We solve the question using statalC 16 where we use the Proportion test from classical hypotheses test and enter the data that we have i.e N1=500 (total sample number from Maricopa county) N2=400 (Total sample number from Pima county) X1=385(Number of adults in favour of increasing the highway speed limit from Maricopa county) X2=267(Number of adults in favour of increasing the highway speed limit from Pima county)HO(Null Hypothesis) There is no significant difference in the support for increasing the speed limit between the residents of two counties H1(Alternate Hypothesis) There is significant difference in the support for increasing the speed limit between the residents of two counties By entering all these data we get the following answers Z value as 3.42 and p-value as 0.001 which is smaller than significant value Therefore we reject null hypothesis and conclude that there is a difference in support for increasing the speed limit between resident of two counties B. 95% confidence interval is as follows (0.04341,0.16159) Where 0.04341 is lower bound and 0.16159 is upper bound This confidence interval does not contain zero.so based on the sample data it seems that there is a difference in support for increasing the speed limit between resident of two counties. 2. An article in Environment International ["Influence of Water Temperature and Shower Head Orifice Size on the Release of Radon During Showering" (1992, Vol. 18(4), pp. 363-369)] described an experiment in which the amount of radon released in showers was investigated. Radon-enriched water was used in the experiment, and six different orifice diameters were tested in shower heads. The data from the experiment are shown in the following table. Orifice Diameter Radon Released (%) 0.37 80 83 83 85 0.51 75 75 79 79 0.71 74 73 76 77 1.02 67 72 74 74 1.40 62 62 69 1.99 60 61 64 66 a. Does the size of the orifice affect the mean percentage of radon released? Use a = 0.05. b. Find the P-value for the F-statistic in part (a). c. Analyze the residuals from this experiment. (Calculate the residual values and run a normality test on it) d. Find a 95% confidence interval on the mean percent of radon released when the orifice diameter is 1.40.Answers: - A.&b. . oneway var2 vari Analysis of Variance Source SS df MS F Prob > F Between groups 1133.375 5 226.675 30.85 0.0000 Within groups 132.25 18 7.34722222 Total 1265.625 23 55.0271739 B.Null hypothesis HO:- The mean percentage of radon released for the six different orifice diameters is zero Alternate Hypothesis:- The mean percentage of radon released for the six different orifice diameters is not zero We have used statalC to solve the problem from the above table we can see that the f-value is 30.85 whereas the critical value FO.05,18=2.73 since the calculated value is greater than critical value we reject the null hypothesis and conclude that the mean percentage of radon released for the six different orifice diameters is not zero A.The size of the orifice affects the mean percentage of radon released. C. There is indication that the variability of the response may be increasing as the mean response increases there appears to be outlier on the normal probability plotNormal Q-Q Plot of VAR00004 Expected Normal Value 70 60 50 70 BO 90 Observed Value Detrended Normal Q-Q Plot of VAR00004 3 2 Deviation from Normal -1 65 70 75 80 85 Observed Value\f3. An experiment was conducted to determine whether either firing temperature or furnace position affects the baked density of a carbon anode. The data are as follows: Temperature (\"Ci Poon BUG B25 850 l 5?0 1053 555 555 108D 51D 583 1043 590 2 528 988 526 54? l2 533 521 l4 532 a. State the hypotheses of interest. b. Test the hypotheses in part {all using the analysis of variance with o = 0.05. What are your conclusions? c. Analyze the residuals from this experiment. {Calculate the residual values and run a normality test on it} d. Using Fisher's LSD method, investigate the differences between the mean baked anode density at the three different levels of temperature. Use {1 = [1.05. {Check the video under Panoptol Answers: - A. Ho: that the means of observations grouped by one factor are the same; that the means of observations grouped by the other factor are the same: and that there is no interaction between the two factors. H1: that the means of observations grouped by one factor are the not some or that the means of observations grouped by the other factor are the not same or that there is interaction between the two factors. anova var3 var2 vari vari#var2 Number of obs = 18 R-squared = 0.9944 Root MSE 21.1555 Adj R-squared = 0.9921 Source Partial SS df MS F Prob>F Model 953320.28 5 190664.06 426.01 0.0000 var2 945342.11 2 472671.06 1056.12 0.0000 var1 7160.0556 1 7160.0556 16.00 0.0018 vari#var2 818. 11111 2 409.05556 0.91 0.4271 Residual 5370.6667 12 447.55556 Total 958690.94 17 56393.585 B. In the above table the f-value for interaction effect is 0.91 F-value for temperature is 1056.12 F-value for position is 16.00 By observation we can conclude that Main effect of both Temperature and Position is significant but interaction effect is insignificantD. Stata/IC 16.0 - \\\\smu.edu\\Files\\usersS\\aambre\\Apps.SMU\\Desktop\\b.dta EN English (U File Edit Data Graphics Statistics User Window Help pwcompare var2#vari, mcompare(tukey) effects Pairwise comparisons of marginal linear predictions Margins : asbalanced Number of Comparisons var2#var1 15 Tukey Tukey Contrast Std. Err. t p>/t/ [95% Conf. Interval] var2#var1 (800 2) VS (800 1) -40. 66667 17.2734 -2.35 0.246 -98.68665 17.35332 (825 1) VS (800 1) 489.3333 17.2734 28.33 0.000 431.3133 547.3533 (825 2) VS (800 1) 433.3333 17.2734 25.09 0.000 375.3133 491.3533 (850 1) VS (800 1) -17.66667 17.2734 -1.02 0.901 -75.68665 40.35332 (850 21Stata/IC 16.0 - \\\\smu.edu\\Files\\usersS\\aambre\\Apps.SMU\\Desktop\\b.dta EN English ( File Edit Data Graphics Statistics User Window Help (825 1) VS (800 2) 530 17.2734 30.68 0.000 471.98 588.02 (825 2) VS (800 2) 474 17.2734 27.44 0.000 415.98 532.02 (850 1) VS (800 2) 23 17.2734 1.33 0.764 -35.01999 81.01999 (850 2) VS (800 2) 2.49e-14 17.2734 0.00 1.000 -58.01999 58.01999 (825 2) VS (825 1) -56 17.2734 -3.24 0.061 -114.02 2.019986 (850 1) VS (825 1) -507 17.2734 -29.35 0.000 -565.02 -448.98 (850 2) VS (825 1) -530 17.2734 -30.68 0.000 -588.02 -471.98 (850 1) VS (825 2) -451 17.2734 -26. 11 0.000 -509.02 -392.98 (850 2) vs (825 2) -474 17.2734 -27.44 0.000 -532.02 -415.98 (850 2) VS (850 1) -23 17.2734 -1.33 0.764 -81.01999 35.01999 The mean temperature of 825 is slightly different than the others