The admissions officer for Clearwater College developed the following estimated regression equation relating the final college GPA to the student's SAT mathematics score and high- school GPA. y'= -1.41 + .0235x1 + .00486x2 Where x1 = high-school grade point average x2 = SAT mathematics score y = final college grade point average 4. Interpret the coefficients in this estimated regression equation. 5. Estimate the final college GPA for a student who has a high-school average of 84 and a score of 540 on the SAT mathematics test.
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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4** had to resubmit this question because the first time the data was duplicated and reflected incorrectly. The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. 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 to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 1 2 3 4 5 5.5 6 Overall Grades 96 89 87 77 76 68 64 **Please circle the answer for each step so I don't get confused. Thanks in advance for helping me with the breakdown and notes** Step 1 of 6 : Find the…You spilled water on your calculations from (a) and can't remember what your estimated regression parameters are. But you do have two possible estimated errors for each of your initial four observations:
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- For the 2011 season, suppose the average number of passing yards per attempt for a certain NFL team was 6.1. Use the estimated regression equation developed in part (c) to predict the percentage of games won by that NFL team. (Note: For the 2011 season, suppose this NFL team's record was 7 wins and 9 losses. Round your answer to the nearest integer.)Would I use the regression line to predict Y from X ? And what is the pattern of the scatterplot?A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using two independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. With this regression model, the predicted price of a 10-year old home with 2,500 square feet of living area is __________. $205.00 $200,000.00 $205,000.00 $255,000.00
- A car dealer wants to estimate the price of a used car based on the age of the car and the mileage. Based on a sample of 20 cars, she determines the sample regression equation that predicts price taxes on the basis of the age (in years) of the number of miles is Price=21,510-1230Age-0.035 Miles (a) If the age of the car was fixed and the mileage was increased by 10,000, would the price increase or decrease and by how much? (b) Predict the selling price of a five-year-old car with 65,000 miles. (Round your answers to the nearest whole number.)The equation of the regression line is Y^=50000+(−500)X. Where is the observed value in relation to the regression line? A financial education time of 29 hours is found to result in a amount of debt of 35500 dollars. What is the predicted amount of debt? Where is the observed value in relation to the regression line? A financial education time of 31 hours is found to result in a amount of debt of 36551 dollars. What is the predicted amount of debt? Where is the observed value in relation to the regression line? A financial education time of 22 hours is found to result in a amount of debt of 38591 dollars. What is the predicted amount of debt? Where is the observed value in relation to the regression line? dollars dollarsA company has a set of data with employee age (X) and the corresponding number of annual on-the-job-accidents (Y). Analysis on the set finds that the regression equation is Y=60-0.5*X. What can be said of the correspondence (relation) between age and accidents? Are younger workers safer or more prone to accident? What is the likely number of accidents for someone aged 25?