A high school guidance counselor wonders if it is possible to predict

Study Session Week of 2/20 Objectives: I will apply previous knowledge to solve problems involving linear regression. Agenda: MC practice problems FR practice problems

A high school guidance counselor wonders if it is possible to predict a student s GPA in their senior year from their GPA in the first marking period of their freshman year. She selects a random sample of 15 seniors from the graduating class of 468 and records both full-year GPA in their senior year ( Senior ) and first=marking-period GPA in their freshman year ( Fresh ). A computer regression analysis and a residual plot of these data are given below 1. Is this a linear model a good fit for this data? A. Yes, the linear regression analysis was completed. B. Yes, the residual plot has no clear pattern. C. Yes, the residual plot has a clear pattern. D. No, the residual plot has a clear pattern. E. No, the residual plot has no clear pattern.

A high school guidance counselor wonders if it is possible to predict a student s GPA in their senior year from their GPA in the first marking period of their freshman year. She selects a random sample of 15 seniors from the graduating class of 468 and records both full-year GPA in their senior year ( Senior ) and first=marking-period GPA in their freshman year ( Fresh ). A computer regression analysis and a residual plot of these data are given below 2. The equation for the least squares regression line from this sample is: A. B. C. D. E. Fresh = 1.6310Senior + 0.5304 Fresh = 1.6310 + 0.5304Senior Senior = 1.6310Fresh + 0.5304 Senior = 1.6310 + 0.5304Fresh Senior = 0.5304Fresh + 1.6310

A high school guidance counselor wonders if it is possible to predict a student s GPA in their senior year from their GPA in the first marking period of their freshman year. She selects a random sample of 15 seniors from the graduating class of 468 and records both full-year GPA in their senior year ( Senior ) and first=marking-period GPA in their freshman year ( Fresh ). A computer regression analysis and a residual plot of these data are given below 3. One student who had a freshman GPA of 2.5 had a senior GPA of 3.2. What is their residual? A. 2.957 B. -2.937 C. 0.243 D. -0.243 E. 0.403 Senior = 0.5304Fresh + 1.6310

A high school guidance counselor wonders if it is possible to predict a student s GPA in their senior year from their GPA in the first marking period of their freshman year. She selects a random sample of 15 seniors from the graduating class of 468 and records both full-year GPA in their senior year ( Senior ) and first=marking-period GPA in their freshman year ( Fresh ). A computer regression analysis and a residual plot of these data are given below 4. Another student had a residual of -0.5 with a freshman year GPA of 2.5. What was their senior year GPA? A. 2.957 B. 2.457 C. 3.457 D. 2.00 E. 3.00 Senior = 0.5304Fresh + 1.6310

A high school guidance counselor wonders if it is possible to predict a student s GPA in their senior year from their GPA in the first marking period of their freshman year. She selects a random sample of 15 seniors from the graduating class of 468 and records both full-year GPA in their senior year ( Senior ) and first=marking-period GPA in their freshman year ( Fresh ). A computer regression analysis and a residual plot of these data are given below 5. What does the quantity R-Sq = 40.3% represent? A. The correlation between freshman GPA and senior GPA a measure of the strength of the linear relationship between the two variables. B. The average deviation of observed senior GPA from the predicted senior GPA ratings, expressed as a percentage of the predicted history rating. C. The average deviation of observed freshman GPA from the predicted senior GPA ratings, expressed as a percentage of the predicted history rating. D. The percentage of variation in freshman gpa that can be explained by the regression equation. E. The percentage of variation in senior gpa that can be explained by the regression equation.

2002 #4