Suppose we have the following information on the examination scores and weekly employment hours of eight students:
Exam Score Employment Hours
80 10
80 5
68 15
95 5
75 21
60 40
90 0
100 0
Assuming Exam Score is the dependent variable (Y) and Employment Hours is the independent variable (X), calculate the simple linear regression equation by hand. Using this, test to see whether the slope on X is significant at the 0.05 level (do this by hand, as well). Make sure to show your work.
Using the model for Question 1, determine the simple linear regression equation using Excel. Using p-values, comment on the goodness of fit for this regression model.
Suppose we have the following information on the examination scores and weekly employment hours of eight...
If I have exam scores and employment hours and I am going to use simple linear regression, which would be my dependent/independent variable? I believe the dependent variable would be exam scores? would this have to be my X variable then?
In Professor Friedman's economics course the correlation between the students' total scores before the final examination and their final examination scores is r-0.56. The pre-exam totals for all students in the course have mean 286 and standard deviation 28, The final exam scores have mean 90 and standard deviation 9. Professor Friedman has lost Julie's final exam but knows that her total before the exam was 320, He decides to predict Julie's final exam score from her pre exam total. Question...
In Professor Friedman's economics course the correlation between the students' total scores before the final examination and their final examination scores is r = 0.52. The pre-exam totals for all students in the course have mean 276 and standard deviation 21. The final exam scores have mean 50 and standard deviation 9. Professor Friedman has lost Julie's final exam but knows that her total before the exam was 318. He decides to predict Julie's final exam score from her pre-exam...
In Professor Friedman's economics course the correlation between the students' total scores before the final examination and their final examination scores is r = 0.66. The pre-exam totals for all students in the course have mean 265 and standard deviation 39. The final exam scores have mean 90 and standard deviation 11. Professor Friedman has lost Julie's final exam but knows that her total before the exam was 320. He decides to predict Julie's final exam score from her pre-exam...
The table below gives the number of hours five randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, y = bo + b x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate...
The table below gives the number of hours ten randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line...
The table below gives the number of hours ten randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line...
You are attempting to link weekly hours of exercise (x) to blood pressure (y) using simple linear regression and the following data: x y exercise (hours) Blood pressure 6 70 4 80 8 40 12 50 In applying the least squares criterion, the slope (b) and the intercept (a) for the best-fitting line are b = 90 and a = -4. Produce the 95% confidence interval estimate of the...
Coefficientsa Standardized Coefficients Beta Unstandardized Coefficients Model Std. Error Be Consant00 56 174 6.204 1.095 863 469 2.695 169 593 680 120 ACTMathScore1.051 649 279 GPA a. Dependent Variable: ACTcompositescore 24. Write out the regression equation in the form of - a + math Xmath +bGPA XGPA (You can use the equation editor or write it out by hand) a. The a is the value on the "Coefficients" table in the (Constant) row and B Unstandardized Coefficients column. This is...
3.3 Table 3.10 shows the scores in the final examination F and the scores in two preliminary examinations P1 and P2 for 22 students in a statistics course. The data can be found in the book's Web site. (a) Fit each of the following models to the data: Model 1 F Bo BiP Model 2 F- Model 3 : F-k) + AP,+AP, + ε Table 3.10 Examination Data: Scores in the Final (F), First Preliminary (Pi), and Second Preliminary (P2)...