Consider the following data regarding students' college GPAs and
high school GPAs. The estimated regression equation is
Estimated College GPA=3.26+(−0.0361)(High School GPA). Estimated College
GPAs |
|
College GPA |
High School GPA |
2.00 |
3.49 |
3.45 |
3.77 |
3.38 |
4.64 |
3.59 |
2.23 |
2.90 |
3.45 |
3.45 |
3.75 |
Step 1 of 3 :
Compute the sum of squared errors (SSE) for the model. Round your answer to four decimal places.
Consider the following data regarding students' college GPAs and high school GPAs. The estimated regression equation...
Consider the following data regarding students' college GPAs and high school GPAs. The estimated regression equation is Estimated College GPA=3.26+(−0.0361) (High School GPA). GPAs College GPA High School GPA 2.00 3.49 3.45 3.77 3.38 4.64 3.59 2.23 2.90 3.45 3.45 3.75 Step 3 of 3 : Compute the standard error (s e ) of the model. Round your answer to four decimal places.
Estimated College GPA=3.26+(−0.0361)(High School GPA). GPAs College GPA High School GPA 2.00 3.49 3.45 3.77 3.38 4.64 3.59 2.23 2.90 3.45 3.45 3.75 Step 2 of 3 : Compute the mean square error (s 2 e ) for the model. Round your answer to four decimal places.
Consider the following data regarding students' college GPAs and high school GPAs. The estimated regression equation is Estimated College GPA=3.12+0.0110(High School GPA). GPAs College GPA High School GPA 2.44 2.39 3.05 3.63 3.82 2.76 2.37 3.00 3.35 2.44 3.88 2.88 Step 1 of 3 : Compute the sum of squared errors (SSE) for the model. Round your answer to four decimal places.
Consider the following data regarding students' college GPAs and high school GPAs. The estimated regression equation is Estimated College GPA=2.91+0.1374(High School GPA). GPAs College GPA High School GPA 3.28 4.68 3.36 4.69 3.10 2.15 3.81 4.38 3.86 3.44 3.07 2.59 Step 1 of 3 : Compute the sum of squared errors (SSE) for the model. Round your answer to four decimal places.
Consider the following data regarding students' college GPAs and high school GPAs. The estimated regression equation is Estimated College GPA=2.22+0.3649(High School GPA).Estimated College GPA=2.22+0.3649(High School GPA). GPAs College GPA High School GPA 2.78 3.34 3.70 2.14 2.27 2.09 3.47 2.93 3.14 2.26 3.95 3.66 Step 2 of 3 : Compute the mean square error (S2e) for the model. Round your answer to four decimal places.
A study of 427 college students was conducted to test whether high school GPA is a predictor of first-year college GPA. school GPA are expected to do better in college. Students with higher High colgpa-Grade point average in college (Range 0.85 -3.97) hsgpaHigh school GPA (Range 2.29-4.5) Model 1: OLS, N-427 Dependent variable: colgpa coefficient std. error const hsgpa 0.5 0.15 R-squared: 0.854880 a. (3%) Write the equation for the least-squares regression line: y- b. (396) The null hypothesis is:...
The admissions officer for a college developed the following estimated regression equation relating the final GPA to the student's SAT mathematics score and high- school average ge-1.41 +0.0235x4 +0.004862 Where xy high-school average, Xy SAT mathematics score, and y final college GPA a) Interpret B, in this estimated regression equation b) Interpret B, in this estimated regression equation c) Estimate the final GPA for a student who has a high-school average of 84 and a score of 540 on the...
1. The regression equation relating high school GPA (x) and college GPA (y) for 100 randomly selected FAU students is y = 0.57x + 0.82. Use the equation to determine the college GPA of a student whose high school GPA is 2.5. Round your answer to two decimal places. a. 3.89 b. 2.62 c. 2.25 d. 1.17
Problem 7 A study of 427 college students was conducted to test whether high school GPA is a predictor of first-year college GPA. Students with higher High school GPA are expected to do better in college colgpa Grade point average in college (Range 0.85 3.97) hsgpa High school GPA (Range 2.29-4.5) Model 1: OLS, N -427 Dependent variable: colgpa coefficient 0.9 0.4 std. error const hsgpa 0.15 R-squared: 0.854880 a. (3%) Write the equation for the least-squares regression line: y-...
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