A study was done to look at the relationship between number of lovers college students have had in their lifetimes and their GPAs. The results of the survey are shown below. Lovers 4 8 3 7 0 6 5 6 GPA 2.7 2.3 3.4 2.4 3.5 2.9 2.7 2.8 Find the correlation coefficient: r=r= Round to 2 decimal places. The null and alternative hypotheses for correlation are: H0:H0: ? μ r ρ == 0 H1:H1: ? ρ r μ ≠≠ 0 The p-value is: (Round to four decimal places) Use a level of significance of α=0.05α=0.05 to state the conclusion of the hypothesis test in the context of the study. There is statistically insignificant evidence to conclude that there is a correlation between the number of lovers students have had in their lifetimes and their GPA. Thus, the use of the regression line is not appropriate. There is statistically insignificant evidence to conclude that a student who has had more lovers will have a lower GPA than a student who has had fewer lovers. There is statistically significant evidence to conclude that a student who has had more lovers will have a lower GPA than a student who has had fewer lovers. There is statistically significant evidence to conclude that there is a correlation between the number of lovers students have had in their lifetimes and their GPA. Thus, the regression line is useful. r2 = (Round to two decimal places) Interpret r2 : There is a large variation in students' GPAs, but if you only look at students who have had a fixed number of lovers, this variation on average is reduced by 80%. Given any group of students who have all had the same number of lovers, 80% of all of these studetns will have the predicted GPA. 80% of all students will have the average GPA. There is a 80% chance that the regression line will be a good predictor for GPA based on the number of lovers a student has had. The equation of the linear regression line is: ˆyy^ = + xx (Please show your answers to two decimal places) Use the model to predict the GPA of a college student who as had 7 lovers. GPA = (Please round your answer to one decimal place.) Interpret the slope of the regression line in the context of the question: The slope has no practical meaning since a GPA cannot be negative. For every additional lover students have, their GPA tends to decrease by 0.15. As x goes up, y goes down. Interpret the y-intercept in the context of the question: If a student has never had a lover, then that student's GPA will be 3.57. The best prediction for the GPA of a student who has never had a lover is 3.57. The y-intercept has no practical meaning for this study. The average GPA for all students is predicted to be 3.57.

A study was done to look at the relationship between number of lovers college students have had in their lifetimes and their GPAs. The results of the survey are shown below. Lovers 4 8 3 7 0 6 5 6 GPA 2.7 2.3 3.4 2.4 3.5 2.9 2.7 2.8 Find the correlation coefficient: r=r= Round to 2 decimal places. The null and alternative hypotheses for correlation are: H0:H0: ? μ r ρ == 0 H1:H1: ? ρ r μ ≠≠ 0 The p-value is: (Round to four decimal places) Use a level of significance of α=0.05α=0.05 to state the conclusion of the hypothesis test in the context of the study. There is statistically insignificant evidence to conclude that there is a correlation between the number of lovers students have had in their lifetimes and their GPA. Thus, the use of the regression line is not appropriate. There is statistically insignificant evidence to conclude that a student who has had more lovers will have a lower GPA than a student who has had fewer lovers. There is statistically significant evidence to conclude that a student who has had more lovers will have a lower GPA than a student who has had fewer lovers. There is statistically significant evidence to conclude that there is a correlation between the number of lovers students have had in their lifetimes and their GPA. Thus, the regression line is useful. r2 = (Round to two decimal places) Interpret r2 : There is a large variation in students' GPAs, but if you only look at students who have had a fixed number of lovers, this variation on average is reduced by 80%. Given any group of students who have all had the same number of lovers, 80% of all of these studetns will have the predicted GPA. 80% of all students will have the average GPA. There is a 80% chance that the regression line will be a good predictor for GPA based on the number of lovers a student has had. The equation of the linear regression line is: ˆyy^ = + xx (Please show your answers to two decimal places) Use the model to predict the GPA of a college student who as had 7 lovers. GPA = (Please round your answer to one decimal place.) Interpret the slope of the regression line in the context of the question: The slope has no practical meaning since a GPA cannot be negative. For every additional lover students have, their GPA tends to decrease by 0.15. As x goes up, y goes down. Interpret the y-intercept in the context of the question: If a student has never had a lover, then that student's GPA will be 3.57. The best prediction for the GPA of a student who has never had a lover is 3.57. The y-intercept has no practical meaning for this study. The average GPA for all students is predicted to be 3.57.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A study was done to look at the relationship between number of lovers college students have had in their lifetimes and their GPAs. The results of the survey are shown below.

Lovers	4	8	3	7	0	6	5	6
GPA	2.7	2.3	3.4	2.4	3.5	2.9	2.7	2.8

Find the correlation coefficient: r=r= Round to 2 decimal places.

The null and alternative hypotheses for correlation are:
H0:H0: ? μ r ρ  == 0
H1:H1: ? ρ r μ   ≠≠ 0
The p-value is:    (Round to four decimal places)

Use a level of significance of α=0.05α=0.05 to state the conclusion of the hypothesis test in the context of the study.

There is statistically insignificant evidence to conclude that there is a correlation between the number of lovers students have had in their lifetimes and their GPA. Thus, the use of the regression line is not appropriate.
There is statistically insignificant evidence to conclude that a student who has had more lovers will have a lower GPA than a student who has had fewer lovers.
There is statistically significant evidence to conclude that a student who has had more lovers will have a lower GPA than a student who has had fewer lovers.
There is statistically significant evidence to conclude that there is a correlation between the number of lovers students have had in their lifetimes and their GPA. Thus, the regression line is useful.

r2 = (Round to two decimal places)

Interpret r2 :

There is a large variation in students' GPAs, but if you only look at students who have had a fixed number of lovers, this variation on average is reduced by 80%.
Given any group of students who have all had the same number of lovers, 80% of all of these studetns will have the predicted GPA.
80% of all students will have the average GPA.
There is a 80% chance that the regression line will be a good predictor for GPA based on the number of lovers a student has had.

The equation of the linear regression line is:
ˆyy^ = + xx (Please show your answers to two decimal places)

Use the model to predict the GPA of a college student who as had 7 lovers.
GPA = (Please round your answer to one decimal place.)

Interpret the slope of the regression line in the context of the question:

The slope has no practical meaning since a GPA cannot be negative.
For every additional lover students have, their GPA tends to decrease by 0.15.
As x goes up, y goes down.

Interpret the y-intercept in the context of the question:

If a student has never had a lover, then that student's GPA will be 3.57.
The best prediction for the GPA of a student who has never had a lover is 3.57.
The y-intercept has no practical meaning for this study.
The average GPA for all students is predicted to be 3.57.

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