Suppose you are interested in seeing whether the total number of days students are absent from high school correlates with their grades. You obtain school records that list the total absences and average grades (on a percentage scale) for 80 graduating seniors. You decide to use the computational formula to calculate the Pearson correlation between the total number of absences and average grades. To do so, you call the total number of absences X and the average grades Y. Then, you add up your data values (EX and Y), add up the squares of your data values (EX² and Y²), and add up the products of your data values (EXY). The following table summarizes your results: ΣΧ 380 ΣΥ 5,820 ΣΧ2 2,708 ΣΥΖ 440,838 ΣΧΥ 26,709 The sum of squares for the total number of absences is SS, = The sum of squares for average grades is SS, = The sum of products for the total number of absences and average grades is SP = The Pearson correlation coefficient is r = Suppose you want to predict average grades from the total number of absences among students. The coefficient of determination is r² = ▼, indicating that of the variability in the average grades can be explained by the relationship between the average grades and the total number of absences. When doing your analysis, suppose that, in addition to having data for the total number of absences for these students, you have data for the total number of days students attended school. You'd expect the correlation between the total number of days students attended school and the total number of absences to be and the correlation between the total number of days students attended school and average grades to be
Suppose you are interested in seeing whether the total number of days students are absent from high school correlates with their grades. You obtain school records that list the total absences and average grades (on a percentage scale) for 80 graduating seniors. You decide to use the computational formula to calculate the Pearson correlation between the total number of absences and average grades. To do so, you call the total number of absences X and the average grades Y. Then, you add up your data values (EX and Y), add up the squares of your data values (EX² and Y²), and add up the products of your data values (EXY). The following table summarizes your results: ΣΧ 380 ΣΥ 5,820 ΣΧ2 2,708 ΣΥΖ 440,838 ΣΧΥ 26,709 The sum of squares for the total number of absences is SS, = The sum of squares for average grades is SS, = The sum of products for the total number of absences and average grades is SP = The Pearson correlation coefficient is r = Suppose you want to predict average grades from the total number of absences among students. The coefficient of determination is r² = ▼, indicating that of the variability in the average grades can be explained by the relationship between the average grades and the total number of absences. When doing your analysis, suppose that, in addition to having data for the total number of absences for these students, you have data for the total number of days students attended school. You'd expect the correlation between the total number of days students attended school and the total number of absences to be and the correlation between the total number of days students attended school and average grades to be
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
100%
Answer correctly and read the questions corretly, take your time do not rush, this is all one quesiton if done correclty and organized I will write a very nice review about you!
Transcribed Image Text:Suppose you are interested in seeing whether the total number of days students are absent from high school correlates with their grades. You
obtain school records that list the total absences and average grades (on a percentage scale) for 80 graduating seniors.
You decide to use the computational formula to calculate the Pearson correlation between the total number of absences and average grades. To
do so, you call the total number of absences X and the average grades Y. Then, you add up your data values (XX and XY), add up the squares of
your data values (EX² and Y²), and add up the products of your data values (ZXY). The following table summarizes your results:
ΣΧ
380
ΣΥ
5,820
ΣΧ2 2,708
ΣΥ2
440,838
ΣΧΥ
26,709
The sum of squares for the total number of absences is SSx =
The sum of squares for average grades is SSy =
The sum of products for the total number of absences and average grades is SP =
The Pearson correlation coefficient is r=
Suppose you want to predict average grades from the total number of absences among students. The coefficient of determination is r² =
▼, indicating that
of the variability in the average grades can be explained by the relationship between the average
grades and the total number of absences.
When doing your analysis, suppose that, in addition to having data for the total number of absences for these students, you have data for the
total number of days students attended school. You'd expect the correlation between the total number of days students attended school and the
total number of absences to be
and the correlation between the total number of days students attended school and average
grades to be
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
This is a popular solution!
Trending now
This is a popular solution!
Step by step
Solved in 3 steps
Recommended textbooks for you
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman