The estimated regression equation relating university GPA to thestudent's SAT mathematics score and high-school GPA is developed. Aportion of Excel output shows the following.ANOVAdfMSSignificance FRegression1.7621??0.0001Residual?Total1.88CoefficientStandard Errort StatInterceptH_GPASAT_math-1.40530.48480.02350.00870.00490.0011where H_GPA = high-school grade point average, SAT_math = SAT%3Dmathematics score.(a) (4 points) Complete the eight missing entries (indicated by ?) inthe above table.(b) (3 points) At 5% significance level, is the model significant?(c) (3 points) At 5% significance level, is each independent variablesignificant?(d) (2 points) Did the estimated regression equation provide a goodfit to the data? Explain in one or two sentences.

The independent variables are H_GPA and SAT_math. This is multiple regression model. We have to find…

Answered: The estimated regression equation…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 4GP

See similar textbooks

Similar questions

The new manager of an Information Technology company collected data for a sample of 20 computer programmers in the organization to perform a multiple regression analysis on the structure of their salaries. The aim of this manager in this exercise is to determine if the Salary (y) of a hired computer programmer was related to the years of Experience (??) in the organization and also the Score (??) of the programmers during their first interview aptitude test scores. The years of experience, score on the aptitude test and the corresponding annual salary (in thousands of Ghana cedis) for a sample of the 20 programmers is shown in the Regression statistics table below; Experience (??) (in years) Score (??) (out of 100%) Salary (y) (GH¢ 000) 4 78 24 7 100 43 1 86 23.7 5 82 34.3 8 86 35.8 10 84 38 0 75 22.2 1 80 23.1 6 83 30 6 91 33 9 88 38 2 73 26.6 10 75 36.2 5 81 31.6…
The new manager of an Information Technology company collected data for a sample of 20 computer programmers in the organization to perform a multiple regression analysis on the structure of their salaries. The aim of this manager in this exercise is to determine if the Salary (y) of a hired computer programmer was related to the years of Experience (??) in the organization and also the Score (??) of the programmers during their first interview aptitude test scores. The years of experience, score on the aptitude test and the corresponding annual salary (in thousands of Ghana cedis) for a sample of the 20 programmers is shown in the Regression statistics table below; Experience (??) (in years) Score (??) (out of 100%) Salary (y) (GH¢ 000) 4 78 24 7 100 43 1 86 23.7 5 82 34.3 8 86 35.8 10 84 38 0 75 22.2 1 80 23.1 6 83 30 6 91 33 9 88 38 2 73 26.6 10 75 36.2 5 81 31.6…
The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technology was also given a foot length of 14.5 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 14.5 cm, what is the single value that is the best-predicted height for that person? The regression equation is Height=64.8+5.70 Foot Length Predictor Coef SE Coef T P Constant 64.79 11.98 5.41 0.000 Foot Length 5.7004 0.4126 13.82 0.000 S=5.50488 R-Sq=70.6% R-Sq(adj)=69.8% Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 147.446 1.796 (143.130, 151.762) (135.900, 158.992) Values of Predictors for New Observations New Obs Foot Length 1 14.5…
The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technology was also given a foot length of 21.4 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 21.4 cm, what is the single value that is the best-predicted height for that person? The regression equation is Height=52.3+4.58 Foot Length Predictor Coef SE Coef T P Constant 52.33 11.01 4.75 0.000 Foot Length 4.5818 0.4872 9.40 0.000 S=5.50437 R-Sq=70.8% R-Sq(adj)=70.0% Predicted Values for New Observations New Obs Fit SE Fit 95% CI 95% PI 1 150.381 1.722 (146.730, 154.032) (138.758, 162.004) Values of Predictors for New Observations New Obs Foot Length 1 21.4 The…
The issue of multicollinearity impacted the 'vadity and trustworthiness' of a regression model. demonstrate how this issue can be a problem by using an appropriate hypothetical and mathematical example.
A sociologist was hired by a large city hospital to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance (miles) between home and work for the employee. A sample of 10 employees was chosen, and the following data were collected. A. Is the estimated regression equation appropriate and adequate
The Wall Street Journal asked Concur Technologies, Inc., an expense management company, to examine data from 8.3 million expense reports to provide insights regarding business travel expenses. Their analysis of the data showed that New York was the most expensive city. The following table shows the average daily hotel room rate (X) and the average amount spent on entertainment (Y) for a random sample of 9 of the 25 most-visited U.S. cities. These data lead to the estimated regression equation y = 17.49 + 1.0334x. For these data SSE = 1541.4. Use Table 1 of Appendix B. a. Predict the amount spent on entertainment for a particular city that has a daily room rate of $89 (to 2 decimals). b. Develop a 95% confidence interval for the mean amount spent on entertainment for all cities that have a daily room rate of $89 (to 2 decimals). c. The average room rate in Chicago is $128. Develop a 95% prediction interval for the amount spent on entertainment in Chicago (to 2 decimals).
The Wall Street Journal asked Concur Technologies, Inc., an expense management company, to examine data from 8.3 million expense reports to provide insights regarding business travel expenses. Their analysis of the data showed that New York was the most expensive city. The following table shows the average daily hotel room rate (X) and the average amount spent on entertainment (Y) for a random sample of 9 of the 25 most-visited U.S. cities. These data lead to the estimated regression equation y = 17.49 + 1.0334x. For these data SSE = 1541.4. Use Table 1 of Appendix B. (NEED ANSWER FOR A) a. Predict the amount spent on entertainment for a particular city that has a daily room rate of $89 (to 2 decimals).
A multiple linear regression model based on a sample of 13 weeks is developed to predict standby hours based on the total staff present and remote hours. The SSR is 23,638.17 and the SSE is 33,273.99. a. Determine whether there is a significant relationship between standby hours and the two independent variables (total staff present and remote hours) at the 0.05 level of significance. What are the correct hypotheses to test?
Suppose the following data were collected from a sample of 15 houses relating selling price to square footage and the architectural style of the house. Use statistical software to find the following regression equation: PRICEi=b0+b1SQFTi+b2COLONIALi+b3RANCHi+ei . Is there enough evidence to support the claim that on average, houses that are ranch style have lower selling prices than houses that are Victorian style at the 0.05 level of significance? If yes, write the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enough evidence."Selling Price Square Footage Colonial (1 if house is Colonial style, 0 otherwise) Ranch (1 if house is Ranch style, 0 otherwise) Victorian (1 if house is Victorian style, 0 otherwise) 377640 1941 1 0 0 460996 3397 0 1 0 405781 2764 0 0 1 407216 2906 0 0 1 435139 3401 1 0 0 405275 2600 0 0 1 381141 2203 0 1 0 370490 2046 1 0 0 404070 2210 0 0 1 460196 3692 0 1 0 382780 2172 1 0 0 406466 2606 0 1…
The systolic blood pressure dataset (in the third sheet of the spreadsheet linked above) contains the systolic blood pressure and age of 30 randomly selected patients in a medical facility. What is the equation for the least square regression line where the independent or predictor variable is age and the dependent or response variable is systolic blood pressure? Y=__________ X + ______________ Patient 7 is 67 years old and has a systolic blood pressure of 170 mm Hg. What is the residual? __________ mm Hg Is the actual value above, below, or on the line? What is the interpretation of the residual? (difference in actual &predicated bp, difference in age, the amount of systolic changes)
find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. In each case, there is sujficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Altitude and Temperature Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded by the author during Delta Flight 1053 from New Orleans to Atlanta. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet).

SEE MORE QUESTIONS

Recommended textbooks for you

Glencoe Algebra 1, Student Edition, 9780079039897…

Algebra

ISBN:

9780079039897

Author:

Carter

Publisher:

McGraw Hill