Use the following partial Stata Regression output to answer this question: Source ss df MS Model 15497.3444 15497.3444 Residual 10419.6556 10 1041.96556 Total 25917 11 2356.09091 Coef. Std. Err. t P>|t| [95% Conf. Interval] -13.12222 3.402557 -3.86 0.003 -20.70359 -5.540852 _cons 20.23333 22.44141 0.90 0.388 -29.76923 70.2359 What is the correlation coefficient? O 0.773 O 0.598 O - 0.598 O - 0.773

Use the following partial Stata Regression output to answer this question: Source ss df MS Model 15497.3444 15497.3444 Residual 10419.6556 10 1041.96556 Total 25917 11 2356.09091 Coef. Std. Err. t P>|t| [95% Conf. Interval] -13.12222 3.402557 -3.86 0.003 -20.70359 -5.540852 _cons 20.23333 22.44141 0.90 0.388 -29.76923 70.2359 What is the correlation coefficient? O 0.773 O 0.598 O - 0.598 O - 0.773

Name: R2 Use the following.partial Stata Regression output to answer this qu
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Description: R2 Use the following.partial Stata Regression output to answer this question:SourcessdfMSModel15497.3444115497.3444Residual10419.6556101041.96556Total25917112356.09091Coef.Std. Err.tP>|t|[95% Conf. Interval]-13.122223.402557-3.860.003-20.70359-5.5408

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter4: Linear Functions

Section: Chapter Questions

Problem 30PT: For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or...

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For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
Analyse the following regression model Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -70.215668 83.5899819 -0.840001 0.40144881 -234.58524 94.1539002 -234.58524 94.1539002 LandArea 5.4726E-06 2.86E-05 0.19134566 0.84835946 -5.077E-05 6.1712E-05 -5.077E-05 6.1712E-05 Rooms -12.67723 10.5132648 -1.2058319 0.22865076 -33.350291 7.9958309 -33.350291 7.9958309 EquivArea 3.78736382 0.24640948 15.3702034 1.3343E-41 3.30282944 4.2718982 3.30282944 4.2718982 Condition 16.0812012 10.2954004 1.56197919 0.11914546 -4.163456 36.3258584 -4.163456 36.3258584 Years 2.13812701 0.32673784 6.54386092 1.9948E-10 1.49563664 2.78061738 1.49563664 2.78061738
You estimated a regression with the following output. Source | SS df MS Number of obs = 383 -------------+---------------------------------- F(1, 381) = 1616.74 Model | 20894411.7 1 20894411.7 Prob > F = 0.0000 Residual | 4923967.53 381 12923.7993 R-squared = 0.8093 -------------+---------------------------------- Adj R-squared = 0.8088 Total | 25818379.2 382 67587.3802 Root MSE = 113.68 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 10.5851 .2632541 40.21 0.000 10.06749 11.10272 _cons | 71.97038 19.03739 3.78 0.000 34.53887 109.4019…
You estimated a regression with the following output. Source | SS df MS Number of obs = 325 -------------+---------------------------------- F(1, 323) = 42850.36 Model | 285905003 1 285905003 Prob > F = 0.0000 Residual | 2155111.65 323 6672.17228 R-squared = 0.9925 -------------+---------------------------------- Adj R-squared = 0.9925 Total | 288060115 324 889074.429 Root MSE = 81.683 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 11.83842 .0571895 207.00 0.000 11.72591 11.95093 _cons | 52.14457 5.949458 8.76 0.000 40.43999 63.84915…
Analyse the following regression model Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 6.10606259 35.9370414 0.16991 0.86517279 -64.557919 76.770044 -64.557919 76.770044 EquivArea 3.62347902 0.1437982 25.1983622 1.3118E-82 3.34072471 3.90623332 3.34072471 3.90623332 Years 1.90428034 0.30317478 6.28113036 9.3418E-10 1.30813952 2.50042116 1.30813952 2.50042116
Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. Using the 68-95-99.7 rule, between what two values would approximately 95% of the observed responses, y, fall when x = 7?
You estimated a regression with the following output. Source | SS df MS Number of obs = 115 -------------+---------------------------------- F(1, 113) = 5454.39 Model | 186947380 1 186947380 Prob > F = 0.0000 Residual | 3873036.62 113 34274.6603 R-squared = 0.9797 -------------+---------------------------------- Adj R-squared = 0.9795 Total | 190820417 114 1673863.3 Root MSE = 185.13 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 28.58986 .3871141 73.85 0.000 27.82292 29.3568 _cons | 10.54686 26.92706 0.39 0.696 -42.80051 63.89423…
The following output comes from regression using the actual HW9 scores and the Final test scores from intro stats one semester. Sample size: 50R (correlation coefficient) = 0.3658R-sq = 0.1338Estimate of error standard deviation: 10.36256Parameter estimates: Parameter Estimate Std. Err. DF T-Stat P-Value Intercept 43.558118 11.1716 48 3.899 0.0003 Slope 0.360048 0.132225 48 2.723 0.009 Assume no assumptions are violated. Form a 87% Confidence interval for how much your final is supposed to increase for each problem done on homework 9.Use 5 decimal places
Consider the following sample regressions for the linear and the logarithmic models. Linear Logarithmic Intercept 6.7904 −5.6712 x 1.0607 NA In(x) NA 10.5447 * se 2.4935 1.5231 R2 0.8233 0.9341 Adjusted R2 0.8013 0.9259 b. Use the selected model to predict y for x = 10. (Round intermediate calculations to at least 4 decimal places and final answer to 2 decimal places.) predicted y=
Consider the following computer output from a multiple regression analysis relating the price of a used car to the variables: age of car, mileage, and safety rating. Coefficients Coefficients Standard Error t� Stat P-value Intercept 42465.6942465.69 5320.545320.54 7.9817.981 0.00000.0000 Age (Year) −21096.02−21096.02 2551.522551.52 −8.268−8.268 0.00000.0000 Mileage(in Thousands) −1312.73−1312.73 103.02103.02 −12.743−12.743 0.00000.0000 Safety Rating 1533.821533.82 165.72165.72 9.2559.255 0.00000.0000 Does the sign of the coefficient for the variable safety rating make sense?
Here is a bivariate data set.xy182425915011737106295223812246 Find the correlation coefficient and report it accurate to four decimal places. r =
The following data is a regression model where the U.S. Department of Transportation has tried to relate the rate of fatal traffic accidents (per 1000 licenses) to the percentage of motorists under the age of 21. Data has been collected for 42 major cities in the United States. SUMMARY OUTPUT Regression Statistics Multiple R 0.83938748 R Square 0.70457134 Adjusted R Square 0.69718562 Standard Error 0.58935028 Observations 42 ANOVA df SS MS F Regression 1 33.13441764 33.1344 95.3964 Residual 40 13.89335048 0.34733 Total 41 47.02776812 Coefficients Standard Error t Stat P-value Intercept -1.5974138 0.371671454 -4.2979 0.00010 Percent Under 21 0.28705317 0.029389769 9.76711 3.79E-9…