The output below estimates the following regressionTreat; = Bo + B1 · Female; + e;Based on the results, which statement is most accurate.• reg treat st femalelSourceSdfMSNumber of obs619%3DF(1, 617)0.01%3DModel.0030063511.003006351Prob > F0.9128R-squaredAdj R-squaredResidual154.71428617.2507524790.0000-0.0016Total154.717286618.250351595Root MSE.50075%3DtreatCoefficientStd. err.P>|t|(95% conf. interval]st femalel.0051549.04707890.110.913-.0872993.0976092_cons.5033557.041023212.270.000.4227937.5839177O In the treatment group, the fraction female is slightly lower than in the control groupIn the treatment group, the fraction female is slightly higher than in the control groupIn the treatment group, the fraction female is much lower than in the control groupO In the treatment group, the fraction female is much higher than in the control group

As the regression coefficient corresponding to female is 0.0051549 which much lower. Also from P…

Answered: The output below estimates the…

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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please only answer the last part "Suppose that real income per capita in New Jersey increases by 1% in the next year. Use the results in column (4) to predict the change in the number of traffic fatalities in the next year. "
A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings (x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficients Standard Error intercept 0.0136 x1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920 answer please : 1: Carry out a test to see if x3 and y are significantly related. Use a 5% level of significance.
Following is a portion of the regression output for an application relating maintenance expense (dollars per month) to usage (hours per week) for a particular brand of computer terminal. Excel File: data14-41.xlsx Yes 5.29 ANOVA ▾to usage. Regression Residual Total Intercept Usage df 1 8 9 S.S 1575.76 349.14 1924.90 Coefficients Standard Error 0.9361 0.149 6.1092 0.8951 MS t Stat F If your answer is zero, enter "0". a. Write the estimated regression equation (to 4 decimals). ŷ = 6.1092 +0.8951 € b. Use a t test to determine whether monthly maintenance expense is related to usage at the .05 level of significance (to 2 decimals, if necessary). Use Table 1 of Appendix B. t= p-value = 0.01 Reject the null hypothesis Monthly maintenance expense is related c. Did the estimated regression equation provide a good fit? P-value Significance F
Can you please help with 4.30 sulfur, the ocean and the sun? Only part A which is make a scatter plot that shows how DMS responds to SRD.
Measurements of the electrical resistivity of tungsten at specified temperatures are presented in the table below. The error in the determination of the temperature was negligible. The temperatures are stated as absolute temperatures in °K. The units of resistivity are u Ncm . Carry out a regression analysis for resistivity as a function of temperature. Temperature Resistivity 300 5.54 Include a scatterplot showing the residuals vs temperature. 400 7.95 Do the regression assumptions appear to be valid? 500 11.00 600 12.28 Include a scatterplot showing the raw data points as black circles as well as a continuous blue line representing the regression model. 700 15.95 800 18.46 Find the lower and upper limits for a 99% confidence interval for the slope of the regression model. Add a pair of red lines to the scatterplot: both passing through the centroid of the data, one having the slope corresponding to the lower bound of the confidence interval, and the other having the slope…
Why this type of plot is used in analysing regression models
Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final =B₁ + B₁ atndrte + ß₂hwrte + Â¸priGPA+ Â¹ ACT .reg final atndrte hwrte priGPA ACT Source Model 3094.70776 11929.9465 Residual Total final SS atndrte hwrte priGPA ACT _cons 15024.6543 df 773.676939 4 669 17.832506 MS 673 22.324895 Coef. Std. Err. .0138725 .0183476 .010863 1.906347 .3750236 .3990516 .0535332 9.225908 1.46863 Number of obs F(4, 669) Prob > F R-squared Adj R-squared Root MSE t P>|t| ||||||||||||| 674 [95% Conf. Interval] Approximate the p-value from the null hypothesis that all of the slope parameters are equal to zero.
A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings(x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficients Standard Error Intercept 0.0136 x1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920 Write out the estimated regression equation for the relationship between the variables. Compute coefficient of determination. What can you say about the strength of this relationship? Carry out a test to determine whether y is…
Please do only the last three
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
Question 10. What is the expected value of the elasticity of education on wages for a person ith 15 years of education at its expected wage? (a) 1.5 (b) 150 160 15.15 (c) 150 (d) 1 (e) 160 150

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