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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 (ZX and XY), add up the squares of your data values (ZX² and ZY²), and add up the products of your data values (ZXY). The following table summarizes your results: ΣΧ 380 ΣΥ 5,820 ⠀ ΣΧΕ ΣΥΖ 2,708 440,838 26,709 ΣΧΥ 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

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 (ZX and XY), add up the squares of your data values (ZX² and ZY²), and add up the products of your data values (ZXY). The following table summarizes your results: ΣΧ 380 ΣΥ 5,820 ⠀ ΣΧΕ ΣΥΖ 2,708 440,838 26,709 ΣΧΥ 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

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 15PPS
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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 (ZX and EY), add up the squares of your data values (EX² and ΣY²), and add up the products of your data values (ΣXY). 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 I 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
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 (ZX and EY), add up the squares of your data values (EX² and ΣY²), and add up the products of your data values (ΣXY). 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 I 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
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 EY), add up the squares of your data values (EX² and ΣY²), 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
Transcribed Image Text: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 EY), add up the squares of your data values (EX² and ΣY²), 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
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