Question

Using this regression model below that I created to, interpret the slope estimates, that is interpret...

Using this regression model below that I created to, interpret the slope estimates, that is interpret the impact that income [per capita gross national income] has on U5MR [No. of deaths of children 0-5 years old, per 1000 live births]. Then interpret the r square [for example what % of the variation can be explained by the other variable]. Lastly calculate the predicted values of U5MR when income = $10,000.

Regression Statistics
Multiple R 0.443388
R Square 0.196593
Adj R Square 0.190413
St. Error 35.12515
Observations 132
ANOVA
df SS MS F Sign F
Regression 1 39247.45 39247.45 31.81083 1.01E-07
Residual 130 160390.9 1233.776
Total 131 199638.4
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 53.50875 3.73253 14.33579 1.53E-28 46.12439 60.893116 46.124387 60.893116
4090 -0.00164 0.00029 -5.64011 1.01E-07 -0.00221 -0.001063 -0.0022118 -0.0010631

Homework Answers

Answer #1

Please go through the answer. Thanks.

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
Use the regression model I created below for U5MR [No. of deaths of children 0-5 yrs...
Use the regression model I created below for U5MR [No. of deaths of children 0-5 yrs old per 1000 live births] and Female Youth LR [Percent of females 15-24 literate] to answer this question. Interpret the slope estimates that is interpret the impact Female Youth LR has on U5MR, then interpret the r square for example what % of what variation can be explained by what variable. Lastly calculate the predicted values U5MR when Female Youth LR= 80. Regression Statistics...
Use this regression model I created to answer to answer this question it has 2 parts:...
Use this regression model I created to answer to answer this question it has 2 parts: 2. (a) Interpret the slope estimates in this regression model, that is interpret the impact Female Youth LR on U5MR, and interpret the R2 [square] respectively. (b) Using the results for this regression model, the predicted value of U5MR(U5MR-No. of deaths of children 0-5 yrs, per 1000 live births) when Female Youth LR =80. (Female Youth LR-Percent of females 15-24 literate) Regression Statistics Multiple...
Discuss the model and interpret the results: report overall model fit (t and significance), report the...
Discuss the model and interpret the results: report overall model fit (t and significance), report the slope coefficient and significance, report and interpret r squared. Regression Statistics Multiple R 0.001989374 R Square 3.95761E-06 Adjusted R Square -0.005046527 Standard Error 8605.170404 Observations 200 ANOVA df SS MS F Significance F Regression 1 58025.4985 58025.4985 0.00078361 0.977695901 Residual 198 14661693620 74048957.68 Total 199 14661751645 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 15668.85874 2390.079111 6.555790838...
Use Excel to develop a regression model for the Hospital Database (using the “Excel Databases.xls” file...
Use Excel to develop a regression model for the Hospital Database (using the “Excel Databases.xls” file on Blackboard) to predict the number of Personnel by the number of Births. Perform a test of the slope. What is the value of the test statistic? Write your answer as a number, round your answer to 2 decimal places. SUMMARY OUTPUT Regression Statistics Multiple R 0.697463374 R Square 0.486455158 Adjusted R Square 0.483861497 Standard Error 590.2581194 Observations 200 ANOVA df SS MS F...
f. Interpret the slope coefficient for one of the dummy variables included in your regression model....
f. Interpret the slope coefficient for one of the dummy variables included in your regression model. g. For the slope coefficient of the variable with the smallest slope coefficient (ignore sign, use absolute value), test to see if the “a priori” expectation from part (a) is confirmed. Use alpha = 0.05. h. Interpret the coefficient of determination in this situation. i. Test the explanatory power of the entire regression model. Please use alpha = 0.01. j. For the variable with...
Compare the two regression models. Does it make sense that spending and household debt could each...
Compare the two regression models. Does it make sense that spending and household debt could each be predicted by annual household income? Why or why not? 1. Predicting spending by household income Regression Statistics Multiple R 0.859343186 R Square 0.738470711 Adjusted R Square 0.737149856 Standard Error 1602.157625 Observations 200 ANOVA df SS MS F Significance F Regression 1 1435121315 1435121315 559.085376 1.42115E-59 Residual 198 508247993.2 2566909.056 Total 199 1943369308 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower...
Solve the missing values in the following regression model. Write down all solutions along with their...
Solve the missing values in the following regression model. Write down all solutions along with their key letter. Regression Statistics Multiple R 0.489538 R Square 0.239648 Adjusted R Square 0.231889 Standard Error 11.76656 Observations 100 ANOVA df SS MS F Significance F Regression 1 4276.457 30.88765 2.35673E-07 Residual 138.452 Total Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99.0% Upper 99.0% Intercept -24.1551 12.83013 -1.88268 0.062709 -49.61605579 1.305895 -57.8589 9.548787 Food 3.167042 0.569851 2.36E-07 2.03619109 4.297893 1.670083...
Discuss the strength and the significance of your regression model by using R-square and significance F...
Discuss the strength and the significance of your regression model by using R-square and significance F where α = 0.05. SUMMARY OUTPUT Regression Statistics Multiple R 0.919011822 R Square 0.844582728 Adjusted R Square 0.834446819 Standard Error 163.953479 Observations 50 ANOVA df SS MS F Significance F Regression 3 6719578.309 2239859.44 83.3257999 1.28754E-18 Residual 46 1236514.191 26880.7433 Total 49 7956092.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 21.7335244 114.2095971 0.19029508 0.84991523 -208.158471 251.62552...
interpret each coefficient estimate and discuss its significance using α = 0.01, α = 0.05 and...
interpret each coefficient estimate and discuss its significance using α = 0.01, α = 0.05 and α = 0.10. Use the concepts of strict and weak significance too SUMMARY OUTPUT Regression Statistics Multiple R 0.820140129 R Square 0.672629832 Adjusted R Square 0.658699186 Standard Error 235.4076294 Observations 50 ANOVA df SS MS F Significance F Regression 2 5351505.158 2675752.58 48.2841827 4.0125E-12 Residual 47 2604587.342 55416.752 Total 49 7956092.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper...
Identify and interpret the F test (one paragraph): Using the p-value approach, is the null hypothesis...
Identify and interpret the F test (one paragraph): Using the p-value approach, is the null hypothesis for the F test rejected or not rejected? Why or why not? Interpret the implications of these findings for the model. SUMMARY OUTPUT Regression Statistics Multiple R 0.60 R Square 0.36 Adjusted R Square 0.26 Standard Error 9.25 Observations 30.00 ANOVA df SS MS F Significance F Regression 4.00 1212.46 303.12 3.54 0.02 Residual 25.00 2139.14 85.57 Total 29.00 3351.60 Coefficients Standard Error t...