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, ŷ= bo + 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.
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, ŷ= bo + 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.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
![Step 1. Find the estimated slope. Round your answer to three decimal places.
Answer:
Step 2. Find the estimated y-intercept. Round your answer to three decimal places.
Answer:
Step 3. Find the estimated value of y when x = 0.5. Round your answer to three
decimal places.
Answer:
Step 4. Determine if the statement "All points predicted by the linear model fall on the
same line" is true or false.
A) True
B) False
Step 5. Substitute the values you found in steps 1 and 2 into the equation for the
regression line to find the estimated linear model. According to this model, if the value
of the independent variable is increased by one unit, then find the change in the
dependent variable ŷ.
Answer:
Step 6. Find the value of the coefficient of determination. Round your answer to three
decimal places.
Answer:](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fccb49400-0448-4b2d-b928-b5450f54ba57%2F60dde27c-2d0e-4400-b872-66f760528c22%2Fma7wtv_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Step 1. Find the estimated slope. Round your answer to three decimal places.
Answer:
Step 2. Find the estimated y-intercept. Round your answer to three decimal places.
Answer:
Step 3. Find the estimated value of y when x = 0.5. Round your answer to three
decimal places.
Answer:
Step 4. Determine if the statement "All points predicted by the linear model fall on the
same line" is true or false.
A) True
B) False
Step 5. Substitute the values you found in steps 1 and 2 into the equation for the
regression line to find the estimated linear model. According to this model, if the value
of the independent variable is increased by one unit, then find the change in the
dependent variable ŷ.
Answer:
Step 6. Find the value of the coefficient of determination. Round your answer to three
decimal places.
Answer:
![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, ŷ = bo + 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 0 0.5 1 1.5 2 3.5 4
Overall Grades 89 81 73 72 69 67 63
Summation Table
xy x²
y²
0.0 0.00 7921
40.5 0.25 6561
X
y
Student 1 0.0 89
0.5
Student 2
81
Student 3 1.0
Student 4 1.5
72 108.0 2.25
Student 5 2.0
Student 6 3.5
73 73.0 1.00 5329
5184
69 138.0 4.00 4761
67 234.5 12.25 4489
Student 7 4.0 63 252.0 16.00 3969
Sum 12.5 514 846.0 35.75 38214](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fccb49400-0448-4b2d-b928-b5450f54ba57%2F60dde27c-2d0e-4400-b872-66f760528c22%2Fmhb5pc3_processed.jpeg&w=3840&q=75)
Transcribed Image Text: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, ŷ = bo + 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 0 0.5 1 1.5 2 3.5 4
Overall Grades 89 81 73 72 69 67 63
Summation Table
xy x²
y²
0.0 0.00 7921
40.5 0.25 6561
X
y
Student 1 0.0 89
0.5
Student 2
81
Student 3 1.0
Student 4 1.5
72 108.0 2.25
Student 5 2.0
Student 6 3.5
73 73.0 1.00 5329
5184
69 138.0 4.00 4761
67 234.5 12.25 4489
Student 7 4.0 63 252.0 16.00 3969
Sum 12.5 514 846.0 35.75 38214
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