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Students a Growing School District Tracks the Student Population Growth =119.53+172.03= 119.53 + 172.03

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Students A growing school district tracks the student population growth over the years
from 2008 to 2013. Here are the regression results and a residual plot. students =119.53+172.03= 119.53 + 172.03 year
Sample size: 6
Rsq=0.987\mathrm { R } - \mathrm { sq } = 0.987
students =119.53+172.03= 119.53 + 172.03 y
Sample size: 6
R-sq =0.987= 0.987

 Students A growing school district tracks the student population growth over the years from 2008 to 2013. Here are the regression results and a residual plot. students  = 119.53 + 172.03  year Sample size: 6  \mathrm { R } - \mathrm { sq } = 0.987  students  = 119.53 + 172.03  y Sample size: 6 R-sq  = 0.987      a. Explain why despite a high R-sq, this regression is not a successful model. To linearize the data, the log (base 10) was taken of the student population. Here are the results.  Dependent Variable: log(students) Sample size: 6  \mathrm { R } - \mathrm { sq } = 0.994   \begin{array} { l r r } \text { Parameter } & \text { Estimate } & \text { Std. Err. } \\ \text { constant } & 2.871 & 0.0162 \\ \text { year } & 0.0389 & 0.00152 \end{array}       b. Describe the success of the linearization. c. Interpret R-sq in the context of this problem. d. Predict the student population in 2014.

a. Explain why despite a high R-sq, this regression is not a successful model.
To linearize the data, the log (base 10) was taken of the student population. Here are the results.
Dependent Variable: log(students) Sample size: 6
Rsq=0.994\mathrm { R } - \mathrm { sq } = 0.994
 Parameter  Estimate  Std. Err.  constant 2.8710.0162 year 0.03890.00152\begin{array} { l r r } \text { Parameter } & \text { Estimate } & \text { Std. Err. } \\ \text { constant } & 2.871 & 0.0162 \\ \text { year } & 0.0389 & 0.00152 \end{array}

 Students A growing school district tracks the student population growth over the years from 2008 to 2013. Here are the regression results and a residual plot. students  = 119.53 + 172.03  year Sample size: 6  \mathrm { R } - \mathrm { sq } = 0.987  students  = 119.53 + 172.03  y Sample size: 6 R-sq  = 0.987      a. Explain why despite a high R-sq, this regression is not a successful model. To linearize the data, the log (base 10) was taken of the student population. Here are the results.  Dependent Variable: log(students) Sample size: 6  \mathrm { R } - \mathrm { sq } = 0.994   \begin{array} { l r r } \text { Parameter } & \text { Estimate } & \text { Std. Err. } \\ \text { constant } & 2.871 & 0.0162 \\ \text { year } & 0.0389 & 0.00152 \end{array}       b. Describe the success of the linearization. c. Interpret R-sq in the context of this problem. d. Predict the student population in 2014.

b. Describe the success of the linearization.
c. Interpret R-sq in the context of this problem.
d. Predict the student population in 2014.

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a. Even though blured image, the residual plot has a...

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