Suppose a regression analysis produces an R2 coefficient of .51. What can we conclude from these...
Suppose a regression analysis produces an R2 coefficient of .51. What can we conclude from these results?
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The model is not useful. |
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The model explains a very small amount of the variation in the dependent variable. |
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The model explains most of the variation in the dependent variable. |
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The model is not statistically significant. |
Homework Answers
The R square value is not sufficiently high to call it as a good model... But that doesnt mean that the model is useless.. Because the value is not near to zero... The correlation which is the square root of this r-square is plus or minus 0.71 which is pretty high... Concludes that either a moderately strong positive or negative relation between the variables... So based on this r square value we can say that 51% of the variation in dependent variable can be explained by this model... So the answer is option 2. Nothing can be said about its statistical significance...from r square
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Suppose a regression analysis produces an R2
coefficient of .51. What can we conclude from these...
Suppose we developed the following least squares regression equation: can we conclude? What The dependent variable...
Suppose we developed the following least squares regression equation: can we conclude? What The dependent variable increases 3.5 for each unit increase in X.! The equation crosses the Y-axis at 2.1. If X= 5, then is 14. There is a significant positive relationship between the dependent and independent variables.
SUMMARY OUTPUT Regression Statistics Multiple R 0.9448 R2 0.8927 Adj. R2 0.8853 SY.X...
SUMMARY OUTPUT Regression Statistics Multiple R 0.9448 R2 0.8927 Adj. R2 0.8853 SY.X 133.14 N 32 ANOVA df SS MS F P-value Regression 2 4277160 2138580 120.6511 0.0000 Residual 29 514034.5 17725.33 Total 31 4791194 Coeff. Std. Err. t Stat P-value Lower 95% Upper 95% Intercept -1336.72 173.3561 -7.71084 0.0000 -1691.2753 -982.16877 X1 12.7362 0.90238 14.114 0.0000 10.890623 14.5817752 X2 85.81513 8.705757 9.857286 0.0000 68.009851 103.620414 With respect to the null hypothesis for...
Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very...
Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: multicollinearity. spurious regression. omitted variable bias. serial correlation.
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: spurious regression. omitted variable bias. multicollinearity. serial correlation.
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: omitted variable bias. o serial correlation. spurious regression. o multicollinearity.
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient...
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: O multicollinearity. omitted variable bias. O serial correlation. spurious regression. 3 pts Question 9
A researcher would like to predict the dependent variable YY from the two independent variables X1X1...
A researcher would like to predict the dependent variable YY from the two independent variables X1X1 and X2X2 for a sample of N=12N=12 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test statistics to assess the significance of the regression model and partial slopes. Use a significance level α=0.01α=0.01. X1X1 X2X2 YY 51.1 40.5 48 53.5 41 51.5 53.2 62.8 42.8 52.3 52.7 51.3 64.1 60 48.8 56.8 62.1 50 61.6 88.1 39.2 60.4 62.5...
If the words are too small: Find an INCORRECT answer when interpreting the results from the...
If the words are too small:
Find an INCORRECT answer when interpreting the results
from the following example.
- regression line is useful only if the correlation
coefficient is significant
-none of the above
-27.4% of the variation CANNOT be explained by the
regression line using the independent variable.
-72.6% of the variation IS explained by the regression
line using the independent variable.
Find an incorrect answer when interpreting the results from the following example. Regression Analysis r2 0.726 r...
(a) The following is taken from the output generated by an Excel analysis of expenditure data using multiple regression: Regression Statistics Multiple R 0.9280 0.8611 0.8365 Adjusted R2 Standard Err...
(a) The following is taken from the output generated by an Excel analysis of expenditure data using multiple regression: Regression Statistics Multiple R 0.9280 0.8611 0.8365 Adjusted R2 Standard Error.1488 Observations21 ANOVA Source Regression Residual Total df MS Significance of F 1.66E-07 3 308.68 35.117 102.893 2.930 17 20 358.49 49.81 Coefficient Standard Error 6.2000 0.7260 0.7260 0.9500 t Stat 3.7097 0.2755 -2.0523 0.5158 23.00 0.20 Intercept X2 X3 0.49 (i) Find the limits of the 95 percent confidence interval...
A researcher would like to predict the dependent variable Y from the two independent variables X1...
A researcher would like to predict the dependent variable Y from the two independent variables X1 and X2 for a sample of N=10 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test statistics to assess the significance of the regression model and partial slopes. Use a significance level α=0.02. X1 X2 Y 40.5 62.9 21.8 16.4 51.3 31.8 62.5 44.4 29.6 60.4 53.6 40.6 50.2 54 33.7 39.2 51.5 37 80.9 16.9 58.1 41.6 52.6...
Suppose we developed the following least squares regression equation: can we conclude? What The dependent variable increases 3.5 for each unit increase in X.! The equation crosses the Y-axis at 2.1. If X= 5, then is 14. There is a significant positive relationship between the dependent and independent variables.
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: spurious regression. omitted variable bias. multicollinearity. serial correlation.
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: omitted variable bias. o serial correlation. spurious regression. o multicollinearity.
Question 8 3 pts Suppose you estimate a multiple regression model using OLS and the coefficient of determination is very high (above 0.8), while none of the estimated coefficients are (individually) statistically different from zero at the 5-percent level of significance. The most likely reason for this result is: O multicollinearity. omitted variable bias. O serial correlation. spurious regression. 3 pts Question 9
If the words are too small:
Find an INCORRECT answer when interpreting the results
from the following example.
- regression line is useful only if the correlation
coefficient is significant
-none of the above
-27.4% of the variation CANNOT be explained by the
regression line using the independent variable.
-72.6% of the variation IS explained by the regression
line using the independent variable.
Find an incorrect answer when interpreting the results from the following example. Regression Analysis r2 0.726 r...
(a) The following is taken from the output generated by an Excel analysis of expenditure data using multiple regression: Regression Statistics Multiple R 0.9280 0.8611 0.8365 Adjusted R2 Standard Error.1488 Observations21 ANOVA Source Regression Residual Total df MS Significance of F 1.66E-07 3 308.68 35.117 102.893 2.930 17 20 358.49 49.81 Coefficient Standard Error 6.2000 0.7260 0.7260 0.9500 t Stat 3.7097 0.2755 -2.0523 0.5158 23.00 0.20 Intercept X2 X3 0.49 (i) Find the limits of the 95 percent confidence interval...
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