Question

Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 10.811 2.987 3.619 0.000296 *** ETHWAR -13.804 4844.876...

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.811 2.987 3.619 0.000296 ***
ETHWAR -13.804 4844.876 -0.003 0.997727
CIVTOT 12.730 4844.877 0.003 0.997903

Can someone please help me interpret these results?

Homework Answers

Answer #1

For interpreting the data, look at the p value corresponding to intercept and two independent variable

P value for ETHWAR is 0.9977, which is greater than 0.05 or 0.10 level of significance. This means that the independent variable ETHWAR is not significant and should not be included in the final model.

P value for CIVTOT is 0.9979, which is greater than 0.05 or 0.10 level of significance. This means that the independent variable CIVTOT is not significant and should not be included in the final model.

Therefore, none of the independent variable is significant and thus, the model is not significant and cant be used for predicting the dependent variable using the two independent variables.

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
Suppose we have a simple linear regression with following printout. Coefficients:                         Estimate Std.    E
Suppose we have a simple linear regression with following printout. Coefficients:                         Estimate Std.    Error    t value Pr(>|t|) (Intercept)        -0.06811     0.08375      -0.813      0.421 X                     0.86818     0.40886      2.1234      0.046 a. What is the p-value for testing the slope H0: β1=0 vs. Ha: β1>0? b. Suppose we had a. F-test for adequacy of this regression. What is the value of the test statistic? What is the p-value? c. Suppose the sample correlation coefficient of x and y is 0. 852.How...
ANOVA df SS Regression 1 0.72 Residual 10 62.6 Total 11 63.32 Coefficients Std Error Intercept...
ANOVA df SS Regression 1 0.72 Residual 10 62.6 Total 11 63.32 Coefficients Std Error Intercept 14.64 146.76 No. of accounts (000) 1.99 5.87 This printout is for data relating the number of ATM withdrawals (in thousands) to the number of accounts (in thousands) at that branch. Predict the number of withdrawals if the number of accounts is 24.97 thousand. State the answer in thousands correct to two decimal places.
Use the following table to answer the questions below. Source Estimate Std. Error t p-value Intercept...
Use the following table to answer the questions below. Source Estimate Std. Error t p-value Intercept 9877.83 829.041 11.915 <0.0001 Freq -163.602 41.901 -3.905 0.0018 A simple linear regression model was fit relating y (total catch of lobsters) and x (frequency). Note that n=8, s=2030.420, ?¯ = 17.867, and ?xx = 1,083.733. (Remember that ?^2 = MSE) Find the 95% confidence interval for ?̂ when ?0=17. ?̂ =   ??/2 =   Confidence interval: (  ,  ) Find the 95% prediction interval for ?̂...
Unstandardized coefficients Standardized coefficients MODEL 1 - B - Std. Error -   BETA - t -...
Unstandardized coefficients Standardized coefficients MODEL 1 - B - Std. Error -   BETA - t - Sig.    x14 - Effort .270 .189    .174 1.433 .157 x1 - Paid fairly .574 .218 .321   2.637 .011 MODEL 2 x14 - Effort - .031 .155 -.020 -.203 .840 x1 - Paid fairly .110 .185 .061 .592 .556 x13 - Loyalty 1.182 .186 .668 6.284 .000 a. Dependent Variable: x21 - Performance QUESTION: (based on the data above) If the overall model is...
betas Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 60 9.22603481 6.503336 0.000187444...
betas Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 60 9.22603481 6.503336 0.000187444 38.72472558 81.27527 X Variable 1 5 0.580265238 8.616749 2.54887E-05 3.661905962 6.338094 please show me the formula on exel how the teacher got thesee results? Here are the following data Restaurant Population as x Sales as y 1 2 58 2 6 105 3 8 88 4 8 118 5 12 117 6 16 137 7 20 157 8 20 169 9 22 149 10...
Model Summary Model R R Square Adjusted R Square      Std. Error of the Estimate 1...
Model Summary Model R R Square Adjusted R Square      Std. Error of the Estimate 1 .816 .666 .629 1.23721 a. Predictors: (Constant),x         ANOVA     Model Sum of Squares df Mean Square F                       Sig Regression Residual Total 27.500 13.776 41.276 1 9 10 27.500 1.531 17.966                 .002b                    a. Dependent Variable: Y                    b. Predictors: (Constant), X Coefficients Model Understand Coefficients B               Std Error Standardized Coefficients      Beta t Sig 1 (Constant)        x 3.001             1.125 .500                 .118 .816 2.667...
Parameter Estimates Parameter DF Estimate Standard Error t Value Pr > |t| Intercept 1 -31.166890 2.880284...
Parameter Estimates Parameter DF Estimate Standard Error t Value Pr > |t| Intercept 1 -31.166890 2.880284 -10.82 <.0001 HouseholdInc 1 0.000097845 0.000027084 3.61 0.0003 DailyPM25 1 2.869205 0.147155 19.50 <.0001 PctSmokers 1 0.671836 0.048104 13.97 <.0001 PctObese 1 0.616837 0.080844 7.63 <.0001 PM25days 1 -0.244056 0.063909 -3.82 0.0001 OzoneDays 1 0.157997 0.035823 4.41 <.0001 PctDiabetic 1 1.164233 0.182226 6.39 <.0001 Regardless of the biological basis of disease or hypotheses, statistically speaking, are any of your variables in your final model...
Residuals:     Min      1Q Median      3Q     Max -6249.5 -382.9 -139.3    25.6 31164.7 Coefficients:         &nbs
Residuals:     Min      1Q Median      3Q     Max -6249.5 -382.9 -139.3    25.6 31164.7 Coefficients:               Estimate Std. Error t value Pr(>|t|)    (Intercept) 1.311e+02 2.219e+02   0.591   0.5550    debt         1.283e-01 3.288e-01   0.390   0.6966    sales        2.942e-01 1.366e-01   2.154   0.0321 * income       1.546e+01 2.697e+00   5.730 2.42e-08 *** assets      -2.390e-05 4.839e-03 -0.005   0.9961    seo          2.973e+02 2.627e+02   1.132   0.2587    --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2019 on 303 degrees of freedom Multiple R-squared: 0.258,   Adjusted...
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .299a...
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .299a .089 .088 11.80775 a. Predictors: (Constant), FIRSTT, LASTT, INCOME, AVGGIFT ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 31353.012 4 7838.253 56.219 .000b Residual 319139.342 2289 139.423 Total 350492.354 2293 a. Dependent Variable: TARGET_D b. Predictors: (Constant), FIRSTT, LASTT, INCOME, AVGGIFT Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .165 1.351 .122 .903 INCOME...
1 test for multicollinearity and discuss possible solutions. Regression output confidence interval variables coefficients std. error...
1 test for multicollinearity and discuss possible solutions. Regression output confidence interval variables coefficients std. error    t (df=148) p-value 95% lower 95% upper VIF Intercept 0.6507 X1 0.00000662 0.00000074 8.910 1.73E-15 0.00000515 0.00000809 3.860 X2 0.00041330 0.00023401 1.766 .0794 -0.00004914 0.00087574 1.132 X3 -0.0006 0.00016086 -3.628 .0004 -0.0009 -0.0003 2.930 X4 -0.00030420 0.00002572 -11.829 3.82E-23 -0.00035502 -0.00025338 2.654 X5 0.0550 0.0346 1.587 .1147 -0.0135 0.1234 1.272 X6 -0.0006 0.00040393 -1.493 .1375 -0.0014 0.0002 3.402 2.542 mean VIF
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT