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(b) Interpret each one of the slopes in this context. (c) Calculate the residual for the first observation in the data set: a student who is aboriginal, male, a slow learner, and missed 2 days of school. (d) The variance of the residuals is 240.57, and the variance of the number of absent days for all students in the data set is 264.17. Calculate the R and the adjusted Rº. Note that there are 146 observations in the data set.

(b) Interpret each one of the slopes in this context. (c) Calculate the residual for the first observation in the data set: a student who is aboriginal, male, a slow learner, and missed 2 days of school. (d) The variance of the residuals is 240.57, and the variance of the number of absent days for all students in the data set is 264.17. Calculate the R and the adjusted Rº. Note that there are 146 observations in the data set.

A First Course in Probability (10th Edition)
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
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ONLY need for Part B, C , D. Thank you!

8.4 Absenteeism, Part I. Researchers interested in the relationship between absenteeism from school and certain demographic characteristics of children collected data from 146 randomly sam- pled students in rural New South Wales, Australia, in a particular school year. Below are three observations from this data set. Im days 1 eth sex 1 2 1 1 11 146 37 The summary table below shows the results of a linear regression model for predicting the average number of days absent based on ethnic background (eth: 0 - aboriginal, 1 - not aboriginal), sex (sex: 0 - female, 1 - male), and learner status (1rn: 0 - average learner, 1 - slow learner)." t value Pr(>[t|) 7.37 0.0000 Estimate Std. Error (Intercept) 18.93 2.57 eth -9.11 2.60 -3.51 0.0000 sex 3.10 2.64 1.18 0.2411 Im 2.15 2.65 0.81 0.4177 (a) Write the equation of the regression line. (b) Interpret each one of the slopes in this context. (c) Calculate the residual for the first observation in the data set: a student who is aboriginal, male, a slow learner, and missed 2 days of school. (d) The variance of the residuals is 240.57, and the variance of the number of absent days for all students in the data set is 264.17. Calculate the R² and the adjusted R. Note that there are 146 observations in the data set.
Transcribed Image Text:8.4 Absenteeism, Part I. Researchers interested in the relationship between absenteeism from school and certain demographic characteristics of children collected data from 146 randomly sam- pled students in rural New South Wales, Australia, in a particular school year. Below are three observations from this data set. Im days 1 eth sex 1 2 1 1 11 146 37 The summary table below shows the results of a linear regression model for predicting the average number of days absent based on ethnic background (eth: 0 - aboriginal, 1 - not aboriginal), sex (sex: 0 - female, 1 - male), and learner status (1rn: 0 - average learner, 1 - slow learner)." t value Pr(>[t|) 7.37 0.0000 Estimate Std. Error (Intercept) 18.93 2.57 eth -9.11 2.60 -3.51 0.0000 sex 3.10 2.64 1.18 0.2411 Im 2.15 2.65 0.81 0.4177 (a) Write the equation of the regression line. (b) Interpret each one of the slopes in this context. (c) Calculate the residual for the first observation in the data set: a student who is aboriginal, male, a slow learner, and missed 2 days of school. (d) The variance of the residuals is 240.57, and the variance of the number of absent days for all students in the data set is 264.17. Calculate the R² and the adjusted R. Note that there are 146 observations in the data set.
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