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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.


Definitions:

Statistics Canada

The national statistical office of Canada, responsible for producing statistics to help better understand the country, its population, resources, economy, society, and culture.

Mortality Data

Information and statistics on the incidence of death within a specified population.

Importance Gap

The discrepancy between the expected importance of an attribute or factor and its perceived performance, usually in the context of service or product evaluation.

Modern Technology

Contemporary tools, devices, and techniques used to facilitate tasks and solve problems in various domains, including communication, transportation, and healthcare.

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