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Given below are ANOVA tables for two restricted models. Model 2: Y = Bo + B.X:...
Using the appropriate model, sample size n, and output below: Model: y = β0 + β1x1...
Using the appropriate model, sample size n, and output below: Model: y = β0 + β1x1 + β2x2 + β3x3 + ε Sample size: n = 16 Regression Statistics Multiple R 0.9975 R Square 0.9950 Adjusted R Square 0.9937 Standard Error 440.3187 Observations 16 ANOVA DF SS MS F Significance F Regression 3 461,801,144.1072 153,933,714.7024 793.9616 0.0000 Residual 12 2,326,566.6701 193,880.5558 Total 15 464,127,710.7773 (1) Report the total variation, unexplained variation, and explained variation as shown on the output. (Round...
1. In order to test whether the multiple linear regression model y bo +b,x1 + b2X2 is better than the average model (lazy model), which of the following null hypotheses is correct: a. Ho' b1...
1. In order to test whether the multiple linear regression model y bo +b,x1 + b2X2 is better than the average model (lazy model), which of the following null hypotheses is correct: a. Ho' b1 = b2 = 0 Но: B1 B2-0 с. We have a dataset Company with three variables: Sales, employees and stores. To build a multiple linear regression model using Sales as dependent variable, number of stores and number of employees as independent variables, which of the...
Using the appropriate model, sample size n, and output: Model: Sample: n=8 S=.5561, = 93.1%...
Using the appropriate model, sample size n, and output:
Model:
Sample: n=8 S=.5561,
= 93.1% ,
adj = 90.3%
1. Report SSE,
, and s as shown on the output. Calculate
from SSE and other numbers. Report the total variation,
unexplained variation, and explained variation as shown on the
output. (Round answers to 4 decimal places.)
2. Report
and adjusted
as shown on the output. Calculate the F statistic. (Round your
answer to 3 decimal places.)
3. Find the...
ek-tin Based on the following regression output, what proportion the total variation in Y is explained...
ek-tin
Based on the following regression output, what proportion the total variation in Y is explained by X? Regression Statistics Multiple R 0.917214 R Square 0.841282 Adjusted R Square 0.821442 Standard Error 9.385572 Observations 10 ANOVA di SS MS Significance F 1 Regression 3735.3060 3735.30600 42.40379 0.000186 Residual 8 704.7117 88.08896 9 Total 4440.0170 Coefficients Standard Error t Stat P-value Lower 95% Intercept 31.623780 10.442970 3.028236 0.016353 7.542233 X Variable 1.131661 0.173786 6.511819 0.000186 0.730910 o a. 0.917214 o b.9.385572...
(13 points) Suppose you have a simple linear regression model such that Y; = Bo +...
(13 points) Suppose you have a simple linear regression model such that Y; = Bo + B18: +€4 with and N(0,0%) Call: 1m (formula - y - x) Formula: F=MSR/MSE, R2 = SSR/SSTO ANOVA decomposition: SSTOSSE + SSR Residuals: Min 1Q Modian -2.16313 -0.64507 -0.06586 Max 30 0.62479 3.00517 Coefficients: Estimate Std. Error t value Pr(> It) (Intercept) 8.00967 0.36529 21.93 -0.62009 0.04245 -14.61 <2e-16 ... <2e-16 .. Signif. codes: ****' 0.001 '** 0.01 '* 0.05 0.1'' 1 Residual standard...
In a comprehensive road test on new car models, one variable measured is the time it...
In a comprehensive road test on new car models, one variable measured is the time it takes the car to accelerate from 0 to 60 miles per hour. To model acceleration time, a regression analysis is conducted on a random sample of 129 new cars. TIME60: y = Elapsed time (in seconds) from 0 mph to 60 mph MAX x = Maximum speed attained (miles per hour) The simple linear model E(y) = Bo + Bjx was fit to the...
A researcher uses two regression models to seek answers to two research questions. These models a...
A researcher uses two
regression models to seek answers to two research questions. These
models are:
Y1 = Bo1 + B11X1
Y2 = Bo2 + B12X1 + B22X12
Test the null hypotheses for both models. Use the results of
your analyses to recommend an appropriate model. In each of the
above two cases, state your null and alternative hypotheses,
decision criteria, decision and conclusion.
The level of significance is 5%. The data for this study are
presented in the table...
The ANOVA summary table to the right is for a multiple regression model with five independent...
The ANOVA summary table to the right is for a multiple regression model with five independent variables. Complete parts (a) through (e). Source Degrees of Freedom Sum of Squares Regression 5 270 Error 28 110 Total 33 380 a. Determine the regression mean square (MSR) and the mean square error (MSE). b. Compute the overall FSTAT test statistic. FSTAT=_______________________ (Round to four decimal places as needed.) c. Determine whether there is a significant relationship between Y and the two independent...
Run the following multivariate linear regression models: Model 1: X3 and X4 Model 2: X2,X3,and X4...
Run the following multivariate linear regression models:
Model 1: X3 and X4
Model 2: X2,X3,and X4Model 3: X1, X3 and
X4Discuss the correlation
between each two variables using adjusted R2 and P-value. Write the
estimated equation of Y for each regression model. Briefly comment
of the Residual Plots.
SUMMARY OUTPUT Regression Statistics Tourist arrivals (X3) Residual Plot Mu R Square Adjusted R Square Standard Error Observations 0.77706686 0.60383291 0.58622549 26011267.3 48 ANOVA Significance F 4.6406E 16 2.3203E 16 34.2942181 8.9591E-10...
please help! 19. Аавьсet Аавьсet Аавьс. Аавьсас Аав Normal 1 No Spac. Heading! Heading 2 Tele...
please help!
19. Аавьсet Аавьсet Аавьс. Аавьсас Аав Normal 1 No Spac. Heading! Heading 2 Tele . Font Paragraph SUMMARY OUTPUT Regression Statistics Multiple R 0.944997 R Square 0.89302 Adjusted R Square 0.875903 Standard Error 0.239984 Observations ANOVA Significance SS MIS 52.17191 8.94E-12 Regression Residual Total 4 25 29 12.01878 3.004695 1.439805 0.057592 13,45859 Intercept Price Diff Advex (AdvExp)2 AdvExp Price Diff Coefficients 29.06316 4.309083 -7.4245 0.653344 -0.55003 Standard Error Stat P-value 7.099182 4.093875 0.000389 2.667846 1.615192 0.118821 2.224741 -3.33724...
1. In order to test whether the multiple linear regression model y bo +b,x1 + b2X2 is better than the average model (lazy model), which of the following null hypotheses is correct: a. Ho' b1 = b2 = 0 Но: B1 B2-0 с. We have a dataset Company with three variables: Sales, employees and stores. To build a multiple linear regression model using Sales as dependent variable, number of stores and number of employees as independent variables, which of the...
Using the appropriate model, sample size n, and output:
Model:
Sample: n=8 S=.5561,
= 93.1% ,
adj = 90.3%
1. Report SSE,
, and s as shown on the output. Calculate
from SSE and other numbers. Report the total variation,
unexplained variation, and explained variation as shown on the
output. (Round answers to 4 decimal places.)
2. Report
and adjusted
as shown on the output. Calculate the F statistic. (Round your
answer to 3 decimal places.)
3. Find the...
ek-tin
Based on the following regression output, what proportion the total variation in Y is explained by X? Regression Statistics Multiple R 0.917214 R Square 0.841282 Adjusted R Square 0.821442 Standard Error 9.385572 Observations 10 ANOVA di SS MS Significance F 1 Regression 3735.3060 3735.30600 42.40379 0.000186 Residual 8 704.7117 88.08896 9 Total 4440.0170 Coefficients Standard Error t Stat P-value Lower 95% Intercept 31.623780 10.442970 3.028236 0.016353 7.542233 X Variable 1.131661 0.173786 6.511819 0.000186 0.730910 o a. 0.917214 o b.9.385572...
(13 points) Suppose you have a simple linear regression model such that Y; = Bo + B18: +€4 with and N(0,0%) Call: 1m (formula - y - x) Formula: F=MSR/MSE, R2 = SSR/SSTO ANOVA decomposition: SSTOSSE + SSR Residuals: Min 1Q Modian -2.16313 -0.64507 -0.06586 Max 30 0.62479 3.00517 Coefficients: Estimate Std. Error t value Pr(> It) (Intercept) 8.00967 0.36529 21.93 -0.62009 0.04245 -14.61 <2e-16 ... <2e-16 .. Signif. codes: ****' 0.001 '** 0.01 '* 0.05 0.1'' 1 Residual standard...
In a comprehensive road test on new car models, one variable measured is the time it takes the car to accelerate from 0 to 60 miles per hour. To model acceleration time, a regression analysis is conducted on a random sample of 129 new cars. TIME60: y = Elapsed time (in seconds) from 0 mph to 60 mph MAX x = Maximum speed attained (miles per hour) The simple linear model E(y) = Bo + Bjx was fit to the...
A researcher uses two
regression models to seek answers to two research questions. These
models are:
Y1 = Bo1 + B11X1
Y2 = Bo2 + B12X1 + B22X12
Test the null hypotheses for both models. Use the results of
your analyses to recommend an appropriate model. In each of the
above two cases, state your null and alternative hypotheses,
decision criteria, decision and conclusion.
The level of significance is 5%. The data for this study are
presented in the table...
Run the following multivariate linear regression models:
Model 1: X3 and X4
Model 2: X2,X3,and X4Model 3: X1, X3 and
X4Discuss the correlation
between each two variables using adjusted R2 and P-value. Write the
estimated equation of Y for each regression model. Briefly comment
of the Residual Plots.
SUMMARY OUTPUT Regression Statistics Tourist arrivals (X3) Residual Plot Mu R Square Adjusted R Square Standard Error Observations 0.77706686 0.60383291 0.58622549 26011267.3 48 ANOVA Significance F 4.6406E 16 2.3203E 16 34.2942181 8.9591E-10...
please help!
19. Аавьсet Аавьсet Аавьс. Аавьсас Аав Normal 1 No Spac. Heading! Heading 2 Tele . Font Paragraph SUMMARY OUTPUT Regression Statistics Multiple R 0.944997 R Square 0.89302 Adjusted R Square 0.875903 Standard Error 0.239984 Observations ANOVA Significance SS MIS 52.17191 8.94E-12 Regression Residual Total 4 25 29 12.01878 3.004695 1.439805 0.057592 13,45859 Intercept Price Diff Advex (AdvExp)2 AdvExp Price Diff Coefficients 29.06316 4.309083 -7.4245 0.653344 -0.55003 Standard Error Stat P-value 7.099182 4.093875 0.000389 2.667846 1.615192 0.118821 2.224741 -3.33724...
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