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• Example 1: A group of teachers is interested in knowing whether a relationship exists between the average number of hours studied per week and high school cumulative grade point average (G.P.A.). The teachers obtain a random sample of students and determine the average number of hours each student studies along with the student's cumulative high school G.P.A. Construct a 95% confidence interval for the true slope of the regression line to help answer the teachers' question. Figure 10.1 presents a data table containing the average number of hours studied per week and the corresponding G.P.A for the 20 high-school students in the sample, along with a scatterplot of the data. GPA 2 1 0 Scatter Plot 000 0 2 4 6 8 10 12 Ave_Hrs 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Ave Hrs 10.5 3.0 6.5 8.0 8.5 2.5 9.5 1.0 4.6 3.5 2.2 6.0 8.0 6.0 5.0 3.0 5.0 4.0 9.0 7.0 GPA 4.571 2.800 3.888 4.055 3.920 3.134 4.445 1.777 2.770 3.112 2.308 3.665 4.500 3.333 3.100 2.723 3.888 3.500 4.334 3.388 Figure 10.1 A scatterplot of the data appears roughly linear with no apparent outliers.

• Example 1: A group of teachers is interested in knowing whether a relationship exists between the average number of hours studied per week and high school cumulative grade point average (G.P.A.). The teachers obtain a random sample of students and determine the average number of hours each student studies along with the student's cumulative high school G.P.A. Construct a 95% confidence interval for the true slope of the regression line to help answer the teachers' question. Figure 10.1 presents a data table containing the average number of hours studied per week and the corresponding G.P.A for the 20 high-school students in the sample, along with a scatterplot of the data. GPA 2 1 0 Scatter Plot 000 0 2 4 6 8 10 12 Ave_Hrs 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Ave Hrs 10.5 3.0 6.5 8.0 8.5 2.5 9.5 1.0 4.6 3.5 2.2 6.0 8.0 6.0 5.0 3.0 5.0 4.0 9.0 7.0 GPA 4.571 2.800 3.888 4.055 3.920 3.134 4.445 1.777 2.770 3.112 2.308 3.665 4.500 3.333 3.100 2.723 3.888 3.500 4.334 3.388 Figure 10.1 A scatterplot of the data appears roughly linear with no apparent outliers.

Calculus For The Life Sciences
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.2: The Least Square Line
Problem 8E
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• Example 1: A group of teachers is interested in knowing whether a relationship exists between the average number of hours studied per week and high school cumulative grade point average (G.P.A.). The teachers obtain a random sample of students and determine the average number of hours each student studies along with the student's cumulative high school G.P.A. Construct a 95% confidence interval for the true slope of the regression line to help answer the teachers' question. Figure 10.1 presents a data table containing the average number of hours studied per week and the corresponding G.P.A for the 20 high-school students in the sample, along with a scatterplot of the data. GPA 2 1 0 Scatter Plot 000 0 2 4 6 8 10 12 Ave_Hrs 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Ave Hrs 10.5 3.0 6.5 8.0 8.5 2.5 9.5 1.0 4.6 3.5 2.2 6.0 8.0 6.0 5.0 3.0 5.0 4.0 9.0 7.0 GPA 4.571 2.800 3.888 4.055 3.920 3.134 4.445 1.777 2.770 3.112 2.308 3.665 4.500 3.333 3.100 2.723 3.888 3.500 4.334 3.388 Figure 10.1 A scatterplot of the data appears roughly linear with no apparent outliers.
Transcribed Image Text:• Example 1: A group of teachers is interested in knowing whether a relationship exists between the average number of hours studied per week and high school cumulative grade point average (G.P.A.). The teachers obtain a random sample of students and determine the average number of hours each student studies along with the student's cumulative high school G.P.A. Construct a 95% confidence interval for the true slope of the regression line to help answer the teachers' question. Figure 10.1 presents a data table containing the average number of hours studied per week and the corresponding G.P.A for the 20 high-school students in the sample, along with a scatterplot of the data. GPA 2 1 0 Scatter Plot 000 0 2 4 6 8 10 12 Ave_Hrs 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Ave Hrs 10.5 3.0 6.5 8.0 8.5 2.5 9.5 1.0 4.6 3.5 2.2 6.0 8.0 6.0 5.0 3.0 5.0 4.0 9.0 7.0 GPA 4.571 2.800 3.888 4.055 3.920 3.134 4.445 1.777 2.770 3.112 2.308 3.665 4.500 3.333 3.100 2.723 3.888 3.500 4.334 3.388 Figure 10.1 A scatterplot of the data appears roughly linear with no apparent outliers.
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