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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 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     a. Explain why despite a high  \mathrm { R } - \mathrm { 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 Rsq\mathrm { R } - \mathrm { sq } , this regression is not a successful model.
To linearize the data, the log\log (base 10 ) was taken of the student population. Here are the results.
Dependent Variable: log\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     a. Explain why despite a high  \mathrm { R } - \mathrm { 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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