Please help asap Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience.AnswerRegression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorⒸ2022 Hawkes LearningObservationsANOVA0.73520.54050.52052130.949749dfSSMSFRegression 2 245,665,739.0934 122,832,869.5467 27.0501Residual 46 208,883,538.9066 4,540,946.4980Total48 454,549,278.0000Step 1 of 2: What would be your expected salary with no education and no experience?Coefficients Standard Error t StatIntercept14281.78907 2,520.8096Education (Years) 2352.4989337.0748Experience (Years) 829.0451391.3561P-value Lower 95 %5.6656 0.000000916 9207.66256.9792 0.00000001 1674.00252.1184 0.039573837 41.2861Significance F1.7E-08MacBook ProUpper 95 %19,355.91563030.99531616.8041TablesKeypadSubmit Answer

Here we have to find the expected salary with no education and no experience

Answered: Consider the following computer output…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 4GP

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Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.73360.7336 R Square 0.53810.5381 Adjusted R Square 0.51800.5180 Standard Error 2140.27632140.2763 Observations 49 ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 245,430,999.7671245,430,999.7671 122,715,499.8836122,715,499.8836 26.789226.7892 1.9E-081.9E-08 Residual 4646 210,716,007.0084210,716,007.0084 4,580,782.76114,580,782.7611 Total 4848 456,147,006.7755456,147,006.7755 Coefficients Standard Error t� Stat P-value Lower 95%95% Upper 95%95% Intercept 14276.146814276.1468 2,531.84252,531.8425 5.63865.6386 0.0000010040.000001004 9179.81229179.8122 19,372.481419,372.4814 Education (Years) 2349.95952349.9595 338.5500338.5500 6.94126.9412 0.0000000110.000000011 1668.49371668.4937 3031.42533031.4253 Experience (Years) 833.6183833.6183…
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.73360.7336 R Square 0.53810.5381 Adjusted R Square 0.51800.5180 Standard Error 2140.27632140.2763 Observations 49 ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 245,430,999.7671245,430,999.7671 122,715,499.8836122,715,499.8836 26.789226.7892 1.9E-081.9E-08 Residual 4646 210,716,007.0084210,716,007.0084 4,580,782.76114,580,782.7611 Total 4848 456,147,006.7755456,147,006.7755 Coefficients Standard Error t� Stat P-value Lower 95%95% Upper 95%95% Intercept 14276.146814276.1468 2,531.84252,531.8425 5.63865.6386 0.0000010040.000001004 9179.81229179.8122 19,372.481419,372.4814 Education (Years) 2349.95952349.9595 338.5500338.5500 6.94126.9412 0.0000000110.000000011 1668.49371668.4937 3031.42533031.4253 Experience (Years) 833.6183833.6183…
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.73520.7352 R Square 0.54050.5405 Adjusted R Square 0.52050.5205 Standard Error 2131.18202131.1820 Observations 4949 ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 245,793,126.4218245,793,126.4218 122,896,563.2109122,896,563.2109 27.058227.0582 1.7E-081.7E-08 Residual 4646 208,929,085.5374208,929,085.5374 4,541,936.64214,541,936.6421 Total 4848 454,722,211.9592454,722,211.9592 Coefficients Standard Error t� Stat P-value Lower 95%95% Upper 95%95% Intercept 14268.6823614268.68236 2,521.08442,521.0844 5.65975.6597 0.0000009340.000000934 9194.00279194.0027 19,343.362119,343.3621 Education (Years) 2352.26982352.2698 337.1115337.1115 6.97776.9777 0.000000010.00000001 1673.69951673.6995 3030.84013030.8401 Experience (Years) 832.2096832.2096…
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?
Calculate the R2of the following multivariate sample regression functions and interpret theanswers.3.1 Investment-hat = β1-hat + β2-hat*Interest rate + β3-hat*Exchange rateESS = 900RSS = 1003.2 Investment-hat = β1 + β2-hat*Interest rate + β3-hat*number of 311 studentsESS = 400RSS = 6003.3 Salary-hat = β1 + β2-hat*Frequency of blinking eyes + β3-hat*Colour of hairRSS = 950TSS = 1000
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…
A researcher notes that, in a certain region, a disproportionate number of software millionaires were born around the year 1955. Is this a coincidence, or does birth year matter when gauging whether a software founder will besuccessful? The researcher investigated this question by analyzing the data shown in the accompanying table. Complete parts a through c below. a. Find the coefficient of determination for the simple linear regression model relating number (y) of software millionaire birthdays in a decade to total number (x) of births in the region. Interpret the result. The coefficient of determination is 1.___? (Round to three decimal places as needed.) This value indicates that 2.____ of the sample variation in the number of software millionaire birthdays is explained by the linear relationship with the total number of births in the region. (Round to one decimal place as needed.) b. Find the coefficient of determination for the simple linear regression model…
The following is a partial computer output of a multiple regression analysis of a data set containing 20 sets of observations on the dependent variableThe regression equation isSALEPRIC = 1470 + 0.814 LANDVAL + 0.820 IMPROVAL + 13.5 AREA Predictor Coef SE Coef T P Constant 1470 5746 0.26 0.801 LANDVAL 0.8145 0.5122 1.59 0.131 IMPROVAL 0.8204 0.2112 3.88 0.0001 AREA 13.529 6.586 2.05 0.057 S = 79190.48 R-Sq = 89.7% R-Sq(adj) = 87.8% Analysis of Variance Source DF SS MS Regression 3 8779676741 2926558914 Residual Error 16 1003491259 62718204 Total 19 9783168000 For the problem above, we want to carry out the significance test about the coefficient of LANDVAL, what is the t-value for this test, and is it significant? 46.66, significant 2.05, significant 1.59, not significant 0.26, not significant
Which of the following assumptions is not necessary for unbiasedness of a slope coefficient in a multiple regression model? MLR1 MLR 4 Homoskedasticity Random sampling
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver’s credit score, and safety rating of the car. Coefficients Coefficients Standard Error tt Stat P-value Intercept 841841 85.9385.93 9.7879.787 0.00000.0000 Car Accidents(In last 3 years) 151.38151.38 15.9815.98 9.4739.473 0.00000.0000 Credit Score −92.51−92.51 10.2310.23 −9.043−9.043 0.00000.0000 Safety Rating −164.66−164.66 15.9815.98 −10.304−10.304 0.00000.0000 Does the sign of the coefficient for the variable credit score make sense?
A part of the output of a regression analysis of Y against X using Excel is given below:SUMMARY OUTPUTRegression StatisticsMultiple R 0.954704R Square 0.91146Adjusted R Square 0.896703Standard Error 28.98954Observations 8ANOVAdf SS MS F Significance FRegression 1 51907.64 51907.64Residual 6 5042.361 840.3936Total 7 56950Coefficients Standard Error t Stat P-valueIntercept 45.2159 39.8049Age 5.3265 0.6777a. State the estimated regression line and interpret the slope coefficient.
A statistical program is recommended. A paper gave the following data on n = 11 female black bears. Age(years) Weight(kg) Home-RangeSize (km2) 10.5 54 43.0 6.5 40 46.6 28.5 62 57.4 6.5 55 35.7 7.5 56 62.0 6.5 62 33.8 5.5 42 39.7 7.5 40 32.3 11.5 59 57.2 9.5 51 24.3 5.5 50 68.6 (a) Fit a multiple regression model to describe the relationship between y = home-range size and the predictors x1 = age and x2 = weight. (Round your numerical values to four decimal places.) = (b) If appropriate, carry out a model utility test with a significance level of 0.05 to determine if at least one of the predictors age and weight is useful for predicting home range size. State the null and alternative hypotheses. H0: ?1 = ?2 = 1Ha: at least one of ?1 or ?2 is not 1.H0: At least one of ?1 or ?2 is not 1.Ha: ?1 = ?2 = 1 H0: ?1 = ?2 = 0Ha: at least one of ?1 or ?2 is not 0.H0: At least one of ?1 or ?2 is not 0.Ha: ?1 = ?2 = 0 Calculate the test…

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