In: Statistics and Probability
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.
Lovers5506508
GPA1.71.83.22.22.43.60.8
Find the correlation coefficient: r=r= Round to 2 decimal places.
The null and alternative hypotheses for correlation are:
H0:H0: ? r ρ μ == 0
H1:H1: ? r μ ρ ≠≠ 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 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.
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.
r2r2 = (Round to two decimal places)
Interpret r2r2 :
There is a 86% chance that the regression line will be a good predictor for GPA based on the number of lovers a student has had.
Given any group of students who have all had the same number of lovers, 86% of all of these studetns will have the predicted GPA.
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 86%.
86% of all students will have the average GPA.
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
3 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 0.29.
As x goes up, y goes down.
Interpret the y-intercept in the context of the question:
The best prediction for the GPA of a student who has never had a lover is 3.45.
The average GPA for all students is predicted to be 3.45.
If a student has never had a lover, then that student's GPA will be 3.45.
The y-intercept has no practical meaning for this study.
X | y | (x-xbar)^2 | (y-ybar)^2 | (x-xbar)(y-ybar) |
5 5 0 6 5 0 8 |
1.7 1.8 3.2 2.2 2.4 3.6 0.8 |
0.735 Sum: 54.857 |
0.295 Sum: 5.357 |
-0.465 Sum: -15.943 |
1) correlation coefficient r =
X Values
∑ = 29
Mean = 4.143
∑(X - Mx)2 = SSx = 54.857
Y Values
∑ = 15.7
Mean = 2.243
∑(Y - My)2 = SSy = 5.357
X and Y Combined
N = 7
∑(X - Mx)(Y - My) = -15.943
R Calculation
r = ∑((X - My)(Y - Mx)) / √((SSx)(SSy))
r = -15.943 / √((54.857)(5.357)) = -0.93
2) the null and alternate hypothesis
H0:ρ=0
H1:ρ≠0
Test statistics
t= r√n-2/(1-r^2) . = -0.93×√7-2/(1-(0.93)^2)
= -5.657
P value = 0.0012
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.
r^2 = 0.86
Given any group of students who have all had the same number of lovers, 86% of all of these studetns will have the predicted GPA
Regression equation :
b = SP/SSX = -15.94/54.86 = -0.29
a = MY - bMX = 2.24 - (-0.29*4.14) = 3.45
ŷ = -0.29X + 3.45
When x= 3
Y^ = -0.29×3 + 3.45 = 2.6
Slope
For every additional lover students have, their GPA tends to decrease by 0.29
Intercept
If a student has never had a lover, then that student's GPA will be 3.45.