Consider the multiple linear regression given below. How many predictor variables currently look "significant" in the...
Consider the multiple linear regression given below. How many predictor variables currently look "significant" in the model?
Multiple Regression
|
Standard |
T |
|||
|
Parameter |
Estimate |
Error |
Statistic |
P-Value |
|
CONSTANT |
33113.2 |
9684.08 |
3.41934 |
0.0009 |
|
Latitude |
-269.803 |
191.056 |
-1.41216 |
0.1610 |
|
Longitude |
29.9439 |
65.7703 |
0.455279 |
0.6499 |
|
AthleticRevenue |
0.0001411 |
0.0000350024 |
4.03115 |
0.0001 |
|
Endowment |
0.00173455 |
0.00106854 |
1.62329 |
0.1076 |
Analysis of Variance
|
Source |
Sum of Squares |
Df |
Mean Square |
F-Ratio |
P-Value |
|
Model |
3.52982E9 |
4 |
882,455,000 |
0.0000 |
|
|
Residual |
9.8797E9 |
101 |
97,818,800 |
||
|
Total (Corr.) |
1.34095E10 |
105 |
R-squared = 26.3232 percent
Homework Answers
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Variables which have less than .05 p-value in the first table are statistically significant.
So, Only 1 ( AthleticRevenue) is statistically significant, because its the only variable which has a p-value less than .05 ( .0001)
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Consider the multiple linear regression given below. How many
predictor variables currently look "significant" in the...
4. Let’s compare the results you calculated for Q3b with results from a multiple linear regression....
4. Let’s compare the results you calculated for Q3b with results from a multiple linear regression. 4a. Would additionally controlling for ‘depth’ and ‘latitude’ be helpful? In other words, is a model that includes ‘depth’, ‘latitude’ and ‘longitude’ superior in model fit to a model that includes only ‘longitude’? Output for a multiple linear regression which includes longitude, depth, and latitude is provided below. (2 points) 4b. Interpret the parameter estimate for ‘longitude’ from the multiple linear regression output. (1...
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For this assignment I have to analyze the regression (relationship between 2 independent variables and 1 dependent variable). Below is all of my data and values. I need help answering the questions that are at the bottom. Questions regarding the strength of the relationship Model: Median wage (y) = 40.3774 - 2.0614 * Population + 0.0284 * GDP Predictor Coefficient Estimate Standard Error t-statistic p-value Constant B0 40.3774 1.1045 36.558 0 Population B1 -2.0614 0.5221 -3.948 0.0003 GDP B2 0.0284...
was nominated (1976-2015) Based on the output (including the graph) below, about how many nominations are...
was nominated (1976-2015)
Based on the output (including the graph) below, about how many
nominations are needed to have a 20% chance of winning Best
Picture?
------
Logistic Regression - winners
Dependent variable: 1=won 0= loss
Factors: number of categories nominated
Estimated Regression Model (Maximum
Likelihood)
Standard
Estimated
Parameter
Estimate
Error
Odds Ratio
CONSTANT
-5.17048
0.737865
number of categories nominated
0.462202
0.0835516
1.58757
Analysis of Deviance
Source
Deviance
Df
P-Value
Model
38.9351
1
0.0000
Residual
172.834
226
0.9965
Total (corr.)...
Is this the best model? Least Squares Linear Regression of Rent P Predictor Variables Constant Size...
Is this the best model?
Least Squares Linear Regression of Rent P Predictor Variables Constant Size Coefficient 1276.56 0.16486 Std Error 454.843 0.41717 T 2.81 0.40 0.0072 0.6945 Mean Square Error (MSE) Standard Deviation 458532 677.150 Adjusted Rs AICC PRESS 0.0032 -0.0175 656.27 2.34E+07 P DF 1 48 Source Regression Residual Total F 0.16 MS 71610.6 458532 0.6945 SS 71610.6 2.201E+07 2.208E+07 42 20.14 0.0006 Lack of Fit Pure Error 2.185E+07 155000 520346 25833.3 6 Cases Included 50 Missing Cases...
Consider the following Excel multiple regression of output of Total Sales on the (c) other (predictor)...
Consider the following Excel multiple regression of output of Total Sales on the (c) other (predictor) variables. Provide some important arguments about the fitted multiple regression model. (Give one argument about each of the three main outputs.) [4 marks] SUMMARY OUTPUT Regression Statistics Multiple R 0.9870 R Square Adjusted R Square 0.9741 0.9721 Standard Error 116.2766 Observations 43 ANOVA Significance F df SS MS F Regression 19817036.22 6605678.74 488.58 5.82876E-31 Residual 527289.46 39 13520.24 Total 42 20344325.68 P-value Coefficients Standard...
Is this the best model? Least Squares Linear Regression of Rent Predictor Variables Constant Size Location...
Is this the best model?
Least Squares Linear Regression of Rent Predictor Variables Constant Size Location Coefficient 1260.79 0.08977 191.625 Std Error 455.277 0.42423 194.769 T 2.77 0.21 0.98 P 0.0080 0.8333 0.3302 VIF 0.0 1.0 1.0 Mean Square Error (MSE) Standard Deviation 458838 677.376 RS Adjusted R AICC PRESS 0.0234 -0.0182 657.62 2.38E+07 DF F 0.56 P 0.5738 2 Source Regression Residual Total MS 257878 458838 SS 515756 2.157E+07 2.208E+07 47 49 45 M M Lack of Fit Pure...
A multiple regression analysis produced the following tables: Predictor Intercept xi x2 Coefficients 624.5369 8.569122 4.736515...
A multiple regression analysis produced the following tables: Predictor Intercept xi x2 Coefficients 624.5369 8.569122 4.736515 Standard Error 78.49712 1.652255 0.699194 t statistic 7.956176 5.186319 6.774248 p value 6.88E-06 0.000301 3.06E-05 Source Regression Residual Total df 2 11 13 SS 1660914 156637.5 1817552 MS 830457.1 14239.77 F 58.31956 p value 1.4E-06 For x1= 30 and x2 = 100, the predicted value of y is 753.77 O 1,173.00 O 1,355.26 615.13 6153.13
A multiple regression analysis produced the following tables: Predictor Intercept Xi x2 Coefficients 616.6849 -3.33833 1.780075...
A multiple regression analysis produced the following
tables:
Predictor Intercept Xi x2 Coefficients 616.6849 -3.33833 1.780075 Standard Error 154.5534 2.333548 0.335605 t statistic 3.990108 -1.43058 5.30407 p value 0.000947 0.170675 5.83E-05 Source Regression Residual Total df 2 15 17 SS 121783 61876.68 183659.6 MS 60891.48 4125.112 p value 0.000286 F 14.76117 Using a = 0.01 to test the null hypothesis Ho: B1 = B2 = 0, the critical F value is 8.68 6.36 8.40 O 6.11 O 3.36
how would I figure out the best regression model? Least Squares Linear Regression of Rent Predictor...
how would I figure out the best regression model?
Least Squares Linear Regression of Rent Predictor Variables Constant Size Location Coefficient 1260.79 0.08977 191.625 Std Error 455.277 0.42423 194.769 T 2.77 0.21 0.98 P 0.0080 0.8333 0.3302 VIF 0.0 1.0 1.0 Mean Square Error (MSE) Standard Deviation 458838 677.376 RS Adjusted R AICC PRESS 0.0234 -0.0182 657.62 2.38E+07 DF F 0.56 P 0.5738 2 Source Regression Residual Total MS 257878 458838 SS 515756 2.157E+07 2.208E+07 47 49 45 M M...
Least Squares Linear Regression of Rent Predictor Variables Constant Size Coefficient 1276.56 0.16486 Std Error 454.843...
Least Squares Linear Regression of Rent Predictor Variables Constant Size Coefficient 1276.56 0.16486 Std Error 454.843 0.41717 T 2.81 0.40 P 0.0072 0.6945 Mean Square Error (MSE) Standard Deviation 458532 677.150 R2 Adjusted R2 AICC PRESS 0.0032 -0.0175 656.27 2.34E+07 DF F 0.16 P 0.6945 1 Source Regression Residual Total MS 71610.6 458532 SS 71610.6 2.201E+07 2.208E+07 48 49 20.14 0.0006 Lack of Fit Pure Error 42 6 2.185E+07 155000 520346 25833.3 Cases Included 50 Missing Cases 0 7. Identify...
was nominated (1976-2015)
Based on the output (including the graph) below, about how many
nominations are needed to have a 20% chance of winning Best
Picture?
------
Logistic Regression - winners
Dependent variable: 1=won 0= loss
Factors: number of categories nominated
Estimated Regression Model (Maximum
Likelihood)
Standard
Estimated
Parameter
Estimate
Error
Odds Ratio
CONSTANT
-5.17048
0.737865
number of categories nominated
0.462202
0.0835516
1.58757
Analysis of Deviance
Source
Deviance
Df
P-Value
Model
38.9351
1
0.0000
Residual
172.834
226
0.9965
Total (corr.)...
Is this the best model?
Least Squares Linear Regression of Rent P Predictor Variables Constant Size Coefficient 1276.56 0.16486 Std Error 454.843 0.41717 T 2.81 0.40 0.0072 0.6945 Mean Square Error (MSE) Standard Deviation 458532 677.150 Adjusted Rs AICC PRESS 0.0032 -0.0175 656.27 2.34E+07 P DF 1 48 Source Regression Residual Total F 0.16 MS 71610.6 458532 0.6945 SS 71610.6 2.201E+07 2.208E+07 42 20.14 0.0006 Lack of Fit Pure Error 2.185E+07 155000 520346 25833.3 6 Cases Included 50 Missing Cases...
Consider the following Excel multiple regression of output of Total Sales on the (c) other (predictor) variables. Provide some important arguments about the fitted multiple regression model. (Give one argument about each of the three main outputs.) [4 marks] SUMMARY OUTPUT Regression Statistics Multiple R 0.9870 R Square Adjusted R Square 0.9741 0.9721 Standard Error 116.2766 Observations 43 ANOVA Significance F df SS MS F Regression 19817036.22 6605678.74 488.58 5.82876E-31 Residual 527289.46 39 13520.24 Total 42 20344325.68 P-value Coefficients Standard...
Is this the best model?
Least Squares Linear Regression of Rent Predictor Variables Constant Size Location Coefficient 1260.79 0.08977 191.625 Std Error 455.277 0.42423 194.769 T 2.77 0.21 0.98 P 0.0080 0.8333 0.3302 VIF 0.0 1.0 1.0 Mean Square Error (MSE) Standard Deviation 458838 677.376 RS Adjusted R AICC PRESS 0.0234 -0.0182 657.62 2.38E+07 DF F 0.56 P 0.5738 2 Source Regression Residual Total MS 257878 458838 SS 515756 2.157E+07 2.208E+07 47 49 45 M M Lack of Fit Pure...
A multiple regression analysis produced the following tables: Predictor Intercept xi x2 Coefficients 624.5369 8.569122 4.736515 Standard Error 78.49712 1.652255 0.699194 t statistic 7.956176 5.186319 6.774248 p value 6.88E-06 0.000301 3.06E-05 Source Regression Residual Total df 2 11 13 SS 1660914 156637.5 1817552 MS 830457.1 14239.77 F 58.31956 p value 1.4E-06 For x1= 30 and x2 = 100, the predicted value of y is 753.77 O 1,173.00 O 1,355.26 615.13 6153.13
A multiple regression analysis produced the following
tables:
Predictor Intercept Xi x2 Coefficients 616.6849 -3.33833 1.780075 Standard Error 154.5534 2.333548 0.335605 t statistic 3.990108 -1.43058 5.30407 p value 0.000947 0.170675 5.83E-05 Source Regression Residual Total df 2 15 17 SS 121783 61876.68 183659.6 MS 60891.48 4125.112 p value 0.000286 F 14.76117 Using a = 0.01 to test the null hypothesis Ho: B1 = B2 = 0, the critical F value is 8.68 6.36 8.40 O 6.11 O 3.36
how would I figure out the best regression model?
Least Squares Linear Regression of Rent Predictor Variables Constant Size Location Coefficient 1260.79 0.08977 191.625 Std Error 455.277 0.42423 194.769 T 2.77 0.21 0.98 P 0.0080 0.8333 0.3302 VIF 0.0 1.0 1.0 Mean Square Error (MSE) Standard Deviation 458838 677.376 RS Adjusted R AICC PRESS 0.0234 -0.0182 657.62 2.38E+07 DF F 0.56 P 0.5738 2 Source Regression Residual Total MS 257878 458838 SS 515756 2.157E+07 2.208E+07 47 49 45 M M...
Least Squares Linear Regression of Rent Predictor Variables Constant Size Coefficient 1276.56 0.16486 Std Error 454.843 0.41717 T 2.81 0.40 P 0.0072 0.6945 Mean Square Error (MSE) Standard Deviation 458532 677.150 R2 Adjusted R2 AICC PRESS 0.0032 -0.0175 656.27 2.34E+07 DF F 0.16 P 0.6945 1 Source Regression Residual Total MS 71610.6 458532 SS 71610.6 2.201E+07 2.208E+07 48 49 20.14 0.0006 Lack of Fit Pure Error 42 6 2.185E+07 155000 520346 25833.3 Cases Included 50 Missing Cases 0 7. Identify...
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