Question

The accompanying table shows the regression results when estimating y = β0 + β1x1 + β2x2...

The accompanying table shows the regression results when estimating y = β0 + β1x1 + β2x2 + β3x3 + ε.

df SS MS F Significance F
Regression 3 453 151 5.03 0.0030
Residual 85 2,521 30
Total 88 2,974
Coefficients Standard Error t-stat p-value
Intercept 14.96 3.08 4.80 0.0000
x1 0.87 0.29 3.00 0.0035
x2 0.46 0.22 2.09 0.0400
x3 0.04 0.34 0.12 0.9066


At the 5% significance level, which of the following explanatory variable(s) is(are) individually significant?

Multiple Choice:

  • Only x1

  • Only x3

  • x1 and x2

  • x2 and x3

Homework Answers

Answer #1

Solution:

If the p-value is less than value, then it is statistically significant.

Given the value of =5%=0.05

Let us check the above condition to find the result.

p-value Comparison Result
x1 0.0035 0.0035<0.05 Significant
x2 0.04 0.04<0.05 Significant
x3 0.9066 0.9066>0.05 Not significant

From the above table we can see that the explanatory variables x1 and x2 is significant because its p-value is less than value.

So option 3 is the correct answer.

x1 and x2

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
The accompanying table shows the regression results when estimating y = β0 + β1x1 + β2x2...
The accompanying table shows the regression results when estimating y = β0 + β1x1 + β2x2 + β3x3 + ε. df SS MS F Significance F Regression 3 453 151 5.03 0.0030 Residual 85 2,521 30 Total 88 2,974 Coefficients Standard Error t-stat p-value Intercept 14.96 3.08 4.86 0.0000 x1 0.87 0.29 3.00 0.0035 x2 0.46 0.22 2.09 0.0400 x3 0.04 0.34 0.12 0.9066 At the 5% significance level, which of the following explanatory variable(s) is(are) individually significant? Multiple Choice...
In a multiple regression y = β0 + β1x1 + β2x2 + β3x3, if based on...
In a multiple regression y = β0 + β1x1 + β2x2 + β3x3, if based on the sample data, the correlation coefficient between x1 and x3 is -0.8, is it going to cause multicollinearity? If so, how do we deal with it?
In a multiple linear regression y = β0 + β1x1 + β2x2 + β3x3, based on...
In a multiple linear regression y = β0 + β1x1 + β2x2 + β3x3, based on two-tailed t tests, if β1 is not significant, but β2 and β3 are significant, what shall we do the next?
15-12: Consider the following regression model:                       y=β0+β1x1+β2x2+ε where:         &
15-12: Consider the following regression model:                       y=β0+β1x1+β2x2+ε where:             x1=A quantitative variable             x2=1 if x1<20                  0 if x1>20 The following estimate regression equation was obtained from a sample of 30 observations:             y^=24.1+5.8x1+7.9x2 Provide the estimate regression equation for instances in which x1<20. Determine the value of y^ when x1=10. Provide the estimate regression equation for instances in which x1>20. Determine the value of y^ when x1=30. please not handwritten so I can read it
Consider the multiple linear regression model y = β0 +β1x1 +β2x2 +β3x3 +β4x4 +ε Using the...
Consider the multiple linear regression model y = β0 +β1x1 +β2x2 +β3x3 +β4x4 +ε Using the procedure for testing a general linear hypothesis, show how to test a. H 0 : β 1 = β 2 = β 3 = β 4 = β b. H 0 : β 1 = β 2 , β 3 = β 4 c. H0: β1-2β2=4β3           β1+2β2=0
1. Suppose the variable x2 has been omitted from the following regression equation, y = β0...
1. Suppose the variable x2 has been omitted from the following regression equation, y = β0 + β1x1 +β2x2 + u. b1 is the estimator obtained when x2 is omitted from the equation. The bias in b1 is positive if A. β2<0 and x1 and x2 are positive correlated B. β2=0 and x1 and x2 are negative correlated C. β2>0 and x1 and x2 are negative correlated D. β2>0 and x1 and x2 are positive correlated 2. Suppose the true...
Consider the multiple regression model E(Y|X1 X2) = β0 + β1X1 + β2X2 + β3X1X2 Can...
Consider the multiple regression model E(Y|X1 X2) = β0 + β1X1 + β2X2 + β3X1X2 Can we interpret β1 as the change in the conditional mean response for a unit change in X1 holding all the other predictors in the model fixed? Group of answer choices a. Yes, because that is the traditional way of interpreting a regression coefficient. b. Yes, because the response variable is quantitative and thus the partial slopes are interpreted exactly in that manner. c. No,...
Using 20 observations, the multiple regression model y = β0 + β1x1 + β2x2 + ε...
Using 20 observations, the multiple regression model y = β0 + β1x1 + β2x2 + ε was estimated. A portion of the regression results is shown in the accompanying table: df SS MS F Significance F Regression 2 2.12E+12 1.06E+12 55.978 3.31E-08 Residual 17 3.11E+11 1.90E+10 Total 19 2.46E+12 Coefficients Standard Error t Stat p-value Lower 95% Upper 95% Intercept −986,892 130,984 −7.534 0.000 −1,263,244 −710,540 x1 28,968 32,080 0.903 0.379 −38,715 96,651 x2 30,888 32,925 0.938 0.362 −38,578 100,354...
Management of a fast-food chain proposed the following regression model to predict sales at outlets: y...
Management of a fast-food chain proposed the following regression model to predict sales at outlets: y = β0 + β1x1 + β2x2 + β3x3 + ε, where y = sales ($1000s) x1= number of competitors within one mile x2= population (in 1000s) within one mile x3is 1 if a drive-up window is present, 0 otherwise Multiple regression analysis was performed on a random sample of data collected from 25 outlets. Given the following portion of an output of the regression...
1. Management of a fast-food chain proposed the following regression model to predict sales at outlets:...
1. Management of a fast-food chain proposed the following regression model to predict sales at outlets: y = β0 + β1x1 + β2x2 + β3x3 + ε, where y = sales ($1000s) x1= number of competitors within one mile x2= population (in 1000s) within one mile x3is 1 if a drive-up window is present, 0 otherwise The following estimated regression equation was developed after 20 outlets were surveyed: = 12.6 − 3.6x1+ 7.0x2+ 14.1x3 Use this equation to predict sales...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT