Assignment Prompt & Instructions
[>Statistics homework help](https://www.sweetstudy.com/fields/mathematics-statistics)
See attached files.
– a year ago
**files (2)**
– ## ProjectTemplate.docx
– ## crime.csv
**## ProjectTemplate.docx**
### 1. Introduction
_Discuss the statement of the problem in terms of the statistical analyses that are being performed. Be sure to address the following:_
What is the data set that you are exploring?
How will your results be used?
What type of analyses will you be running in this project? Explain.
_ Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R/Excel code in your report._
### 2. Data Preparation
1. What are the variables stored in crime.csv?
2.
**Give an overall description about your data set.**
_ Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R/Excel code in your report._
### 3. Simple Linear Regression: Create a simple linear regression model to predict the murder rate using single.parent.
In general, how is a simple linear regression model used to predict the response variable using the predictor variable?
Why you decide to use linear regression for this data set? Justify your option.
What is the equation for your model?
Report the P-value in a formatted table as shown below:
| | |
| — | — |
| **Statistic** | **Value** |
| P-value | X.XXXX<br>_*Round off to 4 decimal places._ |
What is the predicted murder rate for a state that has single.paren of 25.4? Round your answer down to the nearest integer.
_ Answer the questions in a paragraph response. Remove all questions and this note (but not the table) before submitting! Do not include R/Excel code in your report._
### 3. Multiple Regression: Create a multiple linear regression model to predict the murder rate using all important variables.
In general, how is a multiple linear regression model used to predict the response variable using predictor variables?
Report the P-value in a formatted table as shown below:
| | |
| — | — |
| **Statistic** | **Value** |
| P-value | X.XXXX<br>_*Round off to 4 decimal places._ |
Based on the results of the overall p- value, is at least one of the predictors statistically significant in predicting the murder rate?
List all the important variables to predict the murder rate based on p-value.
Report and interpret the coefficient of determination.
What is the equation for your model?
What is the predicted murder rate based on your final multiple regression model (assume some random values for your variables)?
___Answer the questions in a paragraph response. Remove all questions and this note (but not the table) before submitting! Do not include R/Excel code in your report._
### 4. Conclusion
_Describe the results of the statistical analyses clearly, using proper descriptions of statistical terms and concepts. Fully describe what these results mean for your scenario._
Briefly summarize your findings in plain language.
What is the practical importance of the analyses that were performed?
### 5. Citations
_You were_
_**not**_ _required to use external resources for this report. If you did not use any resources, you should remove this entire section. However, if you did use any resources to help you with your interpretation, you_
_**must**_ _cite them. Use proper APA format for citations._
Insert references here in the following format:
Author's Last Name, First Initial. Middle Initial. (Year of Publication). Title of book: Subtitle of book, edition. Place of Publication: Publisher.
_You must use the following template for your report:_
Section 1. Answers to the specific questions asked
Section 2: Excel code. No points may be given if a brief look at the code does not tell us what it is
doing.
Consider the crime data stored in crime.csv. We would like to understand how murder rate is
related to the other variables in the dataset.
(a) Build a linear regression model murder rate vs single.parent and comment on your model.
(b) Build a multiple linear regression model to predict murder rate based on the other variables.
The model should have all the important variables and it should not have any unimportant
variables. Be sure to explore the interactions as well. Perform model diagnostics to check the
standard model assumptions and perform any transformations needed to obtain a model for
which the assumptions reasonably hold.
(c) Use your final model to predict murder rate.
Context of data
This dataset compiles crime records from 51 states, featuring a condensed set of fields that center around key attributes like the unemployment rate, single parent rate, college-educated people rate, and more. Your role as a data analyst is to compute the crime rate for each state by leveraging the specified attributes in the dataset. Apply your understanding of both simple and multiple linear regression analysis, acquired in our class, to predict the crime rate effectively.
**image2.png**
**## crime.csv**
| | | | | | | | | |
| — | — | — | — | — | — | — | — | — |
| state | murder.rate | poverty | high.school | college | single.parent | unemployed | metropolitan | region |
| Alabama | 7.4 | 14.7 | 77.5 | 20.4 | 26 | 4.6 | 70.2 | South |
| Alaska | 4.3 | 8.4 | 90.4 | 28.1 | 23.2 | 6.6 | 41.6 | West |
| Arizona | 7 | 13.5 | 85.1 | 24.6 | 23.5 | 3.9 | 87.9 | West |
| Arkansas | 6.3 | 15.8 | 81.7 | 18.4 | 24.7 | 4.4 | 49 | South |
| California | 6.1 | 14 | 81.2 | 27.5 | 21.8 | 4.9 | 96.7 | West |
| Colorado | 3.1 | 8.5 | 89.7 | 34.6 | 20.8 | 2.7 | 84 | West |
| Connecticut | 2.9 | 7.7 | 88.2 | 31.6 | 22.9 | 2.3 | 95.6 | Northeast |
| Delaware | 3.2 | 9.9 | 86.1 | 24 | 25.6 | 4 | 81.4 | South |
| Florida | 5.6 | 12 | 84 | 22.8 | 26.5 | 3.6 | 93 | South |
| Georgia | 8 | 12.5 | 82.6 | 23.1 | 25.5 | 3.7 | 69.1 | South |
| Hawaii | 2.9 | 10.6 | 87.4 | 26.3 | 19.1 | 4.3 | 72.9 | West |
| Idaho | 1.2 | 13.3 | 86.2 | 20 | 17.7 | 4.9 | 38.6 | West |
| Illinois | 7.2 | 10.5 | 85.5 | 27.1 | 21.9 | 4.4 | 84.5 | North Central |
| Indiana | 5.8 | 8.3 | 84.6 | 17.1 | 22.8 | 3.2 | 71.8 | North Central |
| Iowa | 1.6 | 7.9 | 89.7 | 25.5 | 19.8 | 2.6 | 44.9 | North Central |
| Kansas | 6.3 | 10.5 | 88.1 | 27.3 | 20.2 | 3.7 | 56.8 | North Central |
| Kentucky | 4.8 | 12.5 | 78.7 | 20.5 | 23.2 | 4.1 | 48.4 | South |
| Lousiana | 12.5 | 18.5 | 80.8 | 22.5 | 29.3 | 5.5 | 75.2 | South |
| Maine | 1.2 | 9.8 | 89.3 | 24.1 | 23.7 | 3.5 | 36.3 | Northeast |
| Maryland | 8.1 | 7.3 | 85.7 | 32.3 | 24.5 | 3.9 | 92.7 | South |
| Massachusetts | 2 | 10.2 | 85.1 | 32.7 | 22.8 | 2.6 | 92.1 | Northeast |
| Michigan | 6.7 | 10.2 | 86.2 | 23 | 24.5 | 3.6 | 82.5 | North Central |
| Minnesota | 3.1 | 7.9 | 90.8 | 31.2 | 19.6 | 3.3 | 70.3 | North Central |
| Mississippi | 9 | 15.5 | 80.3 | 18.7 | 30 | 5.7 | 36.2 | South |
| Missouri | 6.2 | 9.8 | 86.6 | 26.2 | 24.3 | 3.5 | 68 | North Central |
| Montana | 1.8 | 16 | 89.6 | 23.8 | 21.4 | 4.9 | 33.4 | West |
| Nebraska | 3.7 | 10.7 | 90.4 | 24.6 | 19.6 | 3 | 52.2 | North Central |
| Nevada | 6.5 | 10.1 | 82.8 | 19.3 | 24.2 | 4.1 | 86.6 | West |
| New_Hampshire | 1.8 | 7.6 | 88.1 | 30.1 | 20 | 2.8 | 60.3 | Northeast |
| New_Jersey | 3.4 | 8.1 | 87.3 | 30.1 | 20.2 | 3.8 | 100 | Northeast |
| New_Mexico | 7.4 | 19.3 | 82.2 | 23.6 | 26.6 | 4.9 | 57 | West |
| New_York | 5 | 14.7 | 82.5 | 28.7 | 26 | 4.6 | 91.9 | Northeast |
| North_Carolina | 7 | 13.2 | 79.2 | 23.2 | 24.3 | 3.6 | 67.2 | South |
| North_Dakota | 0.6 | 12.8 | 85.5 | 22.6 | 19.1 | 3 | 43.4 | North Central |
| Ohio | 3.7 | 11.1 | 87 | 24.6 | 24.6 | 4.1 | 80.9 | North Central |
| Oklahoma | 5.3 | 14.1 | 86.1 | 22.5 | 23.5 | 3 | 60.6 | South |
| Oregon | 2 | 12.9 | 88.1 | 27.2 | 22.5 | 4.9 | 72.8 | West |
| Pennsylvania | 4.9 | 9.8 | 85.7 | 24.3 | 22.8 | 4.2 | 84.5 | Northeast |
| Rhode_Island | 4.3 | 10.2 | 81.3 | 26.4 | 27.4 | 4.1 | 93.8 | Northeast |
| South_Carolina | 5.8 | 12 | 83 | 19 | 27.1 | 3.9 | 70.2 | South |
| South_Dakota | 0.9 | 9.4 | 91.8 | 25.7 | 20.7 | 2.3 | 34.5 | North Central |
| Tennessee | 7.2 | 13.4 | 79.9 | 22 | 27.9 | 3.9 | 67.9 | South |
| Texas | 5.9 | 14.9 | 79.2 | 23.9 | 21.5 | 4.2 | 84.6 | South |
| Utah | 1.9 | 8.1 | 90.7 | 26.4 | 13.6 | 3.2 | 76.4 | West |
| Vermont | 1.5 | 10.3 | 90 | 28.8 | 22.5 | 2.9 | 27.9 | Northeast |
| Virginia | 5.7 | 8.1 | 86.6 | 31.9 | 22.2 | 2.2 | 78.2 | South |
| Washington | 3.3 | 9.5 | 91.8 | 28.6 | 22.1 | 5.2 | 83 | West |
| West_Virginia | 2.5 | 15.8 | 77.1 | 15.3 | 22.3 | 5.5 | 41.9 | South |
| Wisconsin | 3.2 | 9 | 86.7 | 23.8 | 21.7 | 3.5 | 67.8 | North Central |
| Wyoming | 2.4 | 11.1 | 90 | 20.6 | 20.8 | 3.9 | 29.6 | West |
**## crime.csv**
| | | | | | | | | |
| — | — | — | — | — | — | — | — | — |
| state | murder.rate | poverty | high.school | college | single.parent | unemployed | metropolitan | region |
| Alabama | 7.4 | 14.7 | 77.5 | 20.4 | 26 | 4.6 | 70.2 | South |
| Alaska | 4.3 | 8.4 | 90.4 | 28.1 | 23.2 | 6.6 | 41.6 | West |
| Arizona | 7 | 13.5 | 85.1 | 24.6 | 23.5 | 3.9 | 87.9 | West |
| Arkansas | 6.3 | 15.8 | 81.7 | 18.4 | 24.7 | 4.4 | 49 | South |
| California | 6.1 | 14 | 81.2 | 27.5 | 21.8 | 4.9 | 96.7 | West |
| Colorado | 3.1 | 8.5 | 89.7 | 34.6 | 20.8 | 2.7 | 84 | West |
| Connecticut | 2.9 | 7.7 | 88.2 | 31.6 | 22.9 | 2.3 | 95.6 | Northeast |
| Delaware | 3.2 | 9.9 | 86.1 | 24 | 25.6 | 4 | 81.4 | South |
| Florida | 5.6 | 12 | 84 | 22.8 | 26.5 | 3.6 | 93 | South |
| Georgia | 8 | 12.5 | 82.6 | 23.1 | 25.5 | 3.7 | 69.1 | South |
| Hawaii | 2.9 | 10.6 | 87.4 | 26.3 | 19.1 | 4.3 | 72.9 | West |
| Idaho | 1.2 | 13.3 | 86.2 | 20 | 17.7 | 4.9 | 38.6 | West |
| Illinois | 7.2 | 10.5 | 85.5 | 27.1 | 21.9 | 4.4 | 84.5 | North Central |
| Indiana | 5.8 | 8.3 | 84.6 | 17.1 | 22.8 | 3.2 | 71.8 | North Central |
| Iowa | 1.6 | 7.9 | 89.7 | 25.5 | 19.8 | 2.6 | 44.9 | North Central |
| Kansas | 6.3 | 10.5 | 88.1 | 27.3 | 20.2 | 3.7 | 56.8 | North Central |
| Kentucky | 4.8 | 12.5 | 78.7 | 20.5 | 23.2 | 4.1 | 48.4 | South |
| Lousiana | 12.5 | 18.5 | 80.8 | 22.5 | 29.3 | 5.5 | 75.2 | South |
| Maine | 1.2 | 9.8 | 89.3 | 24.1 | 23.7 | 3.5 | 36.3 | Northeast |
| Maryland | 8.1 | 7.3 | 85.7 | 32.3 | 24.5 | 3.9 | 92.7 | South |
| Massachusetts | 2 | 10.2 | 85.1 | 32.7 | 22.8 | 2.6 | 92.1 | Northeast |
| Michigan | 6.7 | 10.2 | 86.2 | 23 | 24.5 | 3.6 | 82.5 | North Central |
| Minnesota | 3.1 | 7.9 | 90.8 | 31.2 | 19.6 | 3.3 | 70.3 | North Central |
| Mississippi | 9 | 15.5 | 80.3 | 18.7 | 30 | 5.7 | 36.2 | South |
| Missouri | 6.2 | 9.8 | 86.6 | 26.2 | 24.3 | 3.5 | 68 | North Central |
| Montana | 1.8 | 16 | 89.6 | 23.8 | 21.4 | 4.9 | 33.4 | West |
| Nebraska | 3.7 | 10.7 | 90.4 | 24.6 | 19.6 | 3 | 52.2 | North Central |
| Nevada | 6.5 | 10.1 | 82.8 | 19.3 | 24.2 | 4.1 | 86.6 | West |
| New_Hampshire | 1.8 | 7.6 | 88.1 | 30.1 | 20 | 2.8 | 60.3 | Northeast |
| New_Jersey | 3.4 | 8.1 | 87.3 | 30.1 | 20.2 | 3.8 | 100 | Northeast |
| New_Mexico | 7.4 | 19.3 | 82.2 | 23.6 | 26.6 | 4.9 | 57 | West |
| New_York | 5 | 14.7 | 82.5 | 28.7 | 26 | 4.6 | 91.9 | Northeast |
| North_Carolina | 7 | 13.2 | 79.2 | 23.2 | 24.3 | 3.6 | 67.2 | South |
| North_Dakota | 0.6 | 12.8 | 85.5 | 22.6 | 19.1 | 3 | 43.4 | North Central |
| Ohio | 3.7 | 11.1 | 87 | 24.6 | 24.6 | 4.1 | 80.9 | North Central |
| Oklahoma | 5.3 | 14.1 | 86.1 | 22.5 | 23.5 | 3 | 60.6 | South |
| Oregon | 2 | 12.9 | 88.1 | 27.2 | 22.5 | 4.9 | 72.8 | West |
| Pennsylvania | 4.9 | 9.8 | 85.7 | 24.3 | 22.8 | 4.2 | 84.5 | Northeast |
| Rhode_Island | 4.3 | 10.2 | 81.3 | 26.4 | 27.4 | 4.1 | 93.8 | Northeast |
| South_Carolina | 5.8 | 12 | 83 | 19 | 27.1 | 3.9 | 70.2 | South |
| South_Dakota | 0.9 | 9.4 | 91.8 | 25.7 | 20.7 | 2.3 | 34.5 | North Central |
| Tennessee | 7.2 | 13.4 | 79.9 | 22 | 27.9 | 3.9 | 67.9 | South |
| Texas | 5.9 | 14.9 | 79.2 | 23.9 | 21.5 | 4.2 | 84.6 | South |
| Utah | 1.9 | 8.1 | 90.7 | 26.4 | 13.6 | 3.2 | 76.4 | West |
| Vermont | 1.5 | 10.3 | 90 | 28.8 | 22.5 | 2.9 | 27.9 | Northeast |
| Virginia | 5.7 | 8.1 | 86.6 | 31.9 | 22.2 | 2.2 | 78.2 | South |
| Washington | 3.3 | 9.5 | 91.8 | 28.6 | 22.1 | 5.2 | 83 | West |
| West_Virginia | 2.5 | 15.8 | 77.1 | 15.3 | 22.3 | 5.5 | 41.9 | South |
| Wisconsin | 3.2 | 9 | 86.7 | 23.8 | 21.7 | 3.5 | 67.8 | North Central |
| Wyoming | 2.4 | 11.1 | 90 | 20.6 | 20.8 | 3.9 | 29.6 | West |
**## ProjectTemplate.docx**
### 1. Introduction
_Discuss the statement of the problem in terms of the statistical analyses that are being performed. Be sure to address the following:_
What is the data set that you are exploring?
How will your results be used?
What type of analyses will you be running in this project? Explain.
_ Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R/Excel code in your report._
### 2. Data Preparation
1. What are the variables stored in crime.csv?
2.
**Give an overall description about your data set.**
_ Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R/Excel code in your report._
### 3. Simple Linear Regression: Create a simple linear regression model to predict the murder rate using single.parent.
In general, how is a simple linear regression model used to predict the response variable using the predictor variable?
Why you decide to use linear regression for this data set? Justify your option.
What is the equation for your model?
Report the P-value in a formatted table as shown below:
| | |
| — | — |
| **Statistic** | **Value** |
| P-value | X.XXXX<br>_*Round off to 4 decimal places._ |
What is the predicted murder rate for a state that has single.paren of 25.4? Round your answer down to the nearest integer.
_ Answer the questions in a paragraph response. Remove all questions and this note (but not the table) before submitting! Do not include R/Excel code in your report._
### 3. Multiple Regression: Create a multiple linear regression model to predict the murder rate using all important variables.
In general, how is a multiple linear regression model used to predict the response variable using predictor variables?
Report the P-value in a formatted table as shown below:
| | |
| — | — |
| **Statistic** | **Value** |
| P-value | X.XXXX<br>_*Round off to 4 decimal places._ |
Based on the results of the overall p- value, is at least one of the predictors statistically significant in predicting the murder rate?
List all the important variables to predict the murder rate based on p-value.
Report and interpret the coefficient of determination.
What is the equation for your model?
What is the predicted murder rate based on your final multiple regression model (assume some random values for your variables)?
___Answer the questions in a paragraph response. Remove all questions and this note (but not the table) before submitting! Do not include R/Excel code in your report._
### 4. Conclusion
_Describe the results of the statistical analyses clearly, using proper descriptions of statistical terms and concepts. Fully describe what these results mean for your scenario._
Briefly summarize your findings in plain language.
What is the practical importance of the analyses that were performed?
### 5. Citations
_You were_
_**not**_ _required to use external resources for this report. If you did not use any resources, you should remove this entire section. However, if you did use any resources to help you with your interpretation, you_
_**must**_ _cite them. Use proper APA format for citations._
Insert references here in the following format:
Author's Last Name, First Initial. Middle Initial. (Year of Publication). Title of book: Subtitle of book, edition. Place of Publication: Publisher.
_You must use the following template for your report:_
Section 1. Answers to the specific questions asked
Section 2: Excel code. No points may be given if a brief look at the code does not tell us what it is
doing.
Consider the crime data stored in crime.csv. We would like to understand how murder rate is
related to the other variables in the dataset.
(a) Build a linear regression model murder rate vs single.parent and comment on your model.
(b) Build a multiple linear regression model to predict murder rate based on the other variables.
The model should have all the important variables and it should not have any unimportant
variables. Be sure to explore the interactions as well. Perform model diagnostics to check the
standard model assumptions and perform any transformations needed to obtain a model for
which the assumptions reasonably hold.
(c) Use your final model to predict murder rate.
Context of data
This dataset compiles crime records from 51 states, featuring a condensed set of fields that center around key attributes like the unemployment rate, single parent rate, college-educated people rate, and more. Your role as a data analyst is to compute the crime rate for each state by leveraging the specified attributes in the dataset. Apply your understanding of both simple and multiple linear regression analysis, acquired in our class, to predict the crime rate effectively.
**image2.png**
**## crime.csv**
| | | | | | | | | |
| — | — | — | — | — | — | — | — | — |
| state | murder.rate | poverty | high.school | college | single.parent | unemployed | metropolitan | region |
| Alabama | 7.4 | 14.7 | 77.5 | 20.4 | 26 | 4.6 | 70.2 | South |
| Alaska | 4.3 | 8.4 | 90.4 | 28.1 | 23.2 | 6.6 | 41.6 | West |
| Arizona | 7 | 13.5 | 85.1 | 24.6 | 23.5 | 3.9 | 87.9 | West |
| Arkansas | 6.3 | 15.8 | 81.7 | 18.4 | 24.7 | 4.4 | 49 | South |
| California | 6.1 | 14 | 81.2 | 27.5 | 21.8 | 4.9 | 96.7 | West |
| Colorado | 3.1 | 8.5 | 89.7 | 34.6 | 20.8 | 2.7 | 84 | West |
| Connecticut | 2.9 | 7.7 | 88.2 | 31.6 | 22.9 | 2.3 | 95.6 | Northeast |
| Delaware | 3.2 | 9.9 | 86.1 | 24 | 25.6 | 4 | 81.4 | South |
| Florida | 5.6 | 12 | 84 | 22.8 | 26.5 | 3.6 | 93 | South |
| Georgia | 8 | 12.5 | 82.6 | 23.1 | 25.5 | 3.7 | 69.1 | South |
| Hawaii | 2.9 | 10.6 | 87.4 | 26.3 | 19.1 | 4.3 | 72.9 | West |
| Idaho | 1.2 | 13.3 | 86.2 | 20 | 17.7 | 4.9 | 38.6 | West |
| Illinois | 7.2 | 10.5 | 85.5 | 27.1 | 21.9 | 4.4 | 84.5 | North Central |
| Indiana | 5.8 | 8.3 | 84.6 | 17.1 | 22.8 | 3.2 | 71.8 | North Central |
| Iowa | 1.6 | 7.9 | 89.7 | 25.5 | 19.8 | 2.6 | 44.9 | North Central |
| Kansas | 6.3 | 10.5 | 88.1 | 27.3 | 20.2 | 3.7 | 56.8 | North Central |
| Kentucky | 4.8 | 12.5 | 78.7 | 20.5 | 23.2 | 4.1 | 48.4 | South |
| Lousiana | 12.5 | 18.5 | 80.8 | 22.5 | 29.3 | 5.5 | 75.2 | South |
| Maine | 1.2 | 9.8 | 89.3 | 24.1 | 23.7 | 3.5 | 36.3 | Northeast |
| Maryland | 8.1 | 7.3 | 85.7 | 32.3 | 24.5 | 3.9 | 92.7 | South |
| Massachusetts | 2 | 10.2 | 85.1 | 32.7 | 22.8 | 2.6 | 92.1 | Northeast |
| Michigan | 6.7 | 10.2 | 86.2 | 23 | 24.5 | 3.6 | 82.5 | North Central |
| Minnesota | 3.1 | 7.9 | 90.8 | 31.2 | 19.6 | 3.3 | 70.3 | North Central |
| Mississippi | 9 | 15.5 | 80.3 | 18.7 | 30 | 5.7 | 36.2 | South |
| Missouri | 6.2 | 9.8 | 86.6 | 26.2 | 24.3 | 3.5 | 68 | North Central |
| Montana | 1.8 | 16 | 89.6 | 23.8 | 21.4 | 4.9 | 33.4 | West |
| Nebraska | 3.7 | 10.7 | 90.4 | 24.6 | 19.6 | 3 | 52.2 | North Central |
| Nevada | 6.5 | 10.1 | 82.8 | 19.3 | 24.2 | 4.1 | 86.6 | West |
| New_Hampshire | 1.8 | 7.6 | 88.1 | 30.1 | 20 | 2.8 | 60.3 | Northeast |
| New_Jersey | 3.4 | 8.1 | 87.3 | 30.1 | 20.2 | 3.8 | 100 | Northeast |
| New_Mexico | 7.4 | 19.3 | 82.2 | 23.6 | 26.6 | 4.9 | 57 | West |
| New_York | 5 | 14.7 | 82.5 | 28.7 | 26 | 4.6 | 91.9 | Northeast |
| North_Carolina | 7 | 13.2 | 79.2 | 23.2 | 24.3 | 3.6 | 67.2 | South |
| North_Dakota | 0.6 | 12.8 | 85.5 | 22.6 | 19.1 | 3 | 43.4 | North Central |
| Ohio | 3.7 | 11.1 | 87 | 24.6 | 24.6 | 4.1 | 80.9 | North Central |
| Oklahoma | 5.3 | 14.1 | 86.1 | 22.5 | 23.5 | 3 | 60.6 | South |
| Oregon | 2 | 12.9 | 88.1 | 27.2 | 22.5 | 4.9 | 72.8 | West |
| Pennsylvania | 4.9 | 9.8 | 85.7 | 24.3 | 22.8 | 4.2 | 84.5 | Northeast |
| Rhode_Island | 4.3 | 10.2 | 81.3 | 26.4 | 27.4 | 4.1 | 93.8 | Northeast |
| South_Carolina | 5.8 | 12 | 83 | 19 | 27.1 | 3.9 | 70.2 | South |
| South_Dakota | 0.9 | 9.4 | 91.8 | 25.7 | 20.7 | 2.3 | 34.5 | North Central |
| Tennessee | 7.2 | 13.4 | 79.9 | 22 | 27.9 | 3.9 | 67.9 | South |
| Texas | 5.9 | 14.9 | 79.2 | 23.9 | 21.5 | 4.2 | 84.6 | South |
| Utah | 1.9 | 8.1 | 90.7 | 26.4 | 13.6 | 3.2 | 76.4 | West |
| Vermont | 1.5 | 10.3 | 90 | 28.8 | 22.5 | 2.9 | 27.9 | Northeast |
| Virginia | 5.7 | 8.1 | 86.6 | 31.9 | 22.2 | 2.2 | 78.2 | South |
| Washington | 3.3 | 9.5 | 91.8 | 28.6 | 22.1 | 5.2 | 83 | West |
| West_Virginia | 2.5 | 15.8 | 77.1 | 15.3 | 22.3 | 5.5 | 41.9 | South |
| Wisconsin | 3.2 | 9 | 86.7 | 23.8 | 21.7 | 3.5 | 67.8 | North Central |
| Wyoming | 2.4 | 11.1 | 90 | 20.6 | 20.8 | 3.9 | 29.6 | West |
**## ProjectTemplate.docx**
### 1. Introduction
_Discuss the statement of the problem in terms of the statistical analyses that are being performed. Be sure to address the following:_
What is the data set that you are exploring?
How will your results be used?
What type of analyses will you be running in this project? Explain.
_ Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R/Excel code in your report._
### 2. Data Preparation
1. What are the variables stored in crime.csv?
2.
**Give an overall description about your data set.**
_ Answer the questions in a paragraph response. Remove all questions and this note before submitting! Do not include R/Excel code in your report._
### 3. Simple Linear Regression: Create a simple linear regression model to predict the murder rate using single.parent.
In general, how is a simple linear regression model used to predict the response variable using the predictor variable?
Why you decide to use linear regression for this data set? Justify your option.
What is the equation for your model?
Report the P-value in a formatted table as shown below:
| | |
| — | — |
| **Statistic** | **Value** |
| P-value | X.XXXX<br>_*Round off to 4 decimal places._ |
What is the predicted murder rate for a state that has single.paren of 25.4? Round your answer down to the nearest integer.
_ Answer the questions in a paragraph response. Remove all questions and this note (but not the table) before submitting! Do not include R/Excel code in your report._
### 3. Multiple Regression: Create a multiple linear regression model to predict the murder rate using all important variables.
In general, how is a multiple linear regression model used to predict the response variable using predictor variables?
Report the P-value in a formatted table as shown below:
| | |
| — | — |
| **Statistic** | **Value** |
| P-value | X.XXXX<br>_*Round off to 4 decimal places._ |
Based on the results of the overall p- value, is at least one of the predictors statistically significant in predicting the murder rate?
List all the important variables to predict the murder rate based on p-value.
Report and interpret the coefficient of determination.
What is the equation for your model?
What is the predicted murder rate based on your final multiple regression model (assume some random values for your variables)?
___Answer the questions in a paragraph response. Remove all questions and this note (but not the table) before submitting! Do not include R/Excel code in your report._
### 4. Conclusion
_Describe the results of the statistical analyses clearly, using proper descriptions of statistical terms and concepts. Fully describe what these results mean for your scenario._
Briefly summarize your findings in plain language.
What is the practical importance of the analyses that were performed?
### 5. Citations
_You were_
_**not**_ _required to use external resources for this report. If you did not use any resources, you should remove this entire section. However, if you did use any resources to help you with your interpretation, you_
_**must**_ _cite them. Use proper APA format for citations._
Insert references here in the following format:
Author's Last Name, First Initial. Middle Initial. (Year of Publication). Title of book: Subtitle of book, edition. Place of Publication: Publisher.
_You must use the following template for your report:_
Section 1. Answers to the specific questions asked
Section 2: Excel code. No points may be given if a brief look at the code does not tell us what it is
doing.
Consider the crime data stored in crime.csv. We would like to understand how murder rate is
related to the other variables in the dataset.
(a) Build a linear regression model murder rate vs single.parent and comment on your model.
(b) Build a multiple linear regression model to predict murder rate based on the other variables.
The model should have all the important variables and it should not have any unimportant
variables. Be sure to explore the interactions as well. Perform model diagnostics to check the
standard model assumptions and perform any transformations needed to obtain a model for
which the assumptions reasonably hold.
(c) Use your final model to predict murder rate.
Context of data
This dataset compiles crime records from 51 states, featuring a condensed set of fields that center around key attributes like the unemployment rate, single parent rate, college-educated people rate, and more. Your role as a data analyst is to compute the crime rate for each state by leveraging the specified attributes in the dataset. Apply your understanding of both simple and multiple linear regression analysis, acquired in our class, to predict the crime rate effectively.
**image2.png**
**## crime.csv**
| | | | | | | | | |
| — | — | — | — | — | — | — | — | — |
| state | murder.rate | poverty | high.school | college | single.parent | unemployed | metropolitan | region |
| Alabama | 7.4 | 14.7 | 77.5 | 20.4 | 26 | 4.6 | 70.2 | South |
| Alaska | 4.3 | 8.4 | 90.4 | 28.1 | 23.2 | 6.6 | 41.6 | West |
| Arizona | 7 | 13.5 | 85.1 | 24.6 | 23.5 | 3.9 | 87.9 | West |
| Arkansas | 6.3 | 15.8 | 81.7 | 18.4 | 24.7 | 4.4 | 49 | South |
| California | 6.1 | 14 | 81.2 | 27.5 | 21.8 | 4.9 | 96.7 | West |
| Colorado | 3.1 | 8.5 | 89.7 | 34.6 | 20.8 | 2.7 | 84 | West |
| Connecticut | 2.9 | 7.7 | 88.2 | 31.6 | 22.9 | 2.3 | 95.6 | Northeast |
| Delaware | 3.2 | 9.9 | 86.1 | 24 | 25.6 | 4 | 81.4 | South |
| Florida | 5.6 | 12 | 84 | 22.8 | 26.5 | 3.6 | 93 | South |
| Georgia | 8 | 12.5 | 82.6 | 23.1 | 25.5 | 3.7 | 69.1 | South |
| Hawaii | 2.9 | 10.6 | 87.4 | 26.3 | 19.1 | 4.3 | 72.9 | West |
| Idaho | 1.2 | 13.3 | 86.2 | 20 | 17.7 | 4.9 | 38.6 | West |
| Illinois | 7.2 | 10.5 | 85.5 | 27.1 | 21.9 | 4.4 | 84.5 | North Central |
| Indiana | 5.8 | 8.3 | 84.6 | 17.1 | 22.8 | 3.2 | 71.8 | North Central |
| Iowa | 1.6 | 7.9 | 89.7 | 25.5 | 19.8 | 2.6 | 44.9 | North Central |
| Kansas | 6.3 | 10.5 | 88.1 | 27.3 | 20.2 | 3.7 | 56.8 | North Central |
| Kentucky | 4.8 | 12.5 | 78.7 | 20.5 | 23.2 | 4.1 | 48.4 | South |
| Lousiana | 12.5 | 18.5 | 80.8 | 22.5 | 29.3 | 5.5 | 75.2 | South |
| Maine | 1.2 | 9.8 | 89.3 | 24.1 | 23.7 | 3.5 | 36.3 | Northeast |
| Maryland | 8.1 | 7.3 | 85.7 | 32.3 | 24.5 | 3.9 | 92.7 | South |
| Massachusetts | 2 | 10.2 | 85.1 | 32.7 | 22.8 | 2.6 | 92.1 | Northeast |
| Michigan | 6.7 | 10.2 | 86.2 | 23 | 24.5 | 3.6 | 82.5 | North Central |
| Minnesota | 3.1 | 7.9 | 90.8 | 31.2 | 19.6 | 3.3 | 70.3 | North Central |
| Mississippi | 9 | 15.5 | 80.3 | 18.7 | 30 | 5.7 | 36.2 | South |
| Missouri | 6.2 | 9.8 | 86.6 | 26.2 | 24.3 | 3.5 | 68 | North Central |
| Montana | 1.8 | 16 | 89.6 | 23.8 | 21.4 | 4.9 | 33.4 | West |
| Nebraska | 3.7 | 10.7 | 90.4 | 24.6 | 19.6 | 3 | 52.2 | North Central |
| Nevada | 6.5 | 10.1 | 82.8 | 19.3 | 24.2 | 4.1 | 86.6 | West |
| New_Hampshire | 1.8 | 7.6 | 88.1 | 30.1 | 20 | 2.8 | 60.3 | Northeast |
| New_Jersey | 3.4 | 8.1 | 87.3 | 30.1 | 20.2 | 3.8 | 100 | Northeast |
| New_Mexico | 7.4 | 19.3 | 82.2 | 23.6 | 26.6 | 4.9 | 57 | West |
| New_York | 5 | 14.7 | 82.5 | 28.7 | 26 | 4.6 | 91.9 | Northeast |
| North_Carolina | 7 | 13.2 | 79.2 | 23.2 | 24.3 | 3.6 | 67.2 | South |
| North_Dakota | 0.6 | 12.8 | 85.5 | 22.6 | 19.1 | 3 | 43.4 | North Central |
| Ohio | 3.7 | 11.1 | 87 | 24.6 | 24.6 | 4.1 | 80.9 | North Central |
| Oklahoma | 5.3 | 14.1 | 86.1 | 22.5 | 23.5 | 3 | 60.6 | South |
| Oregon | 2 | 12.9 | 88.1 | 27.2 | 22.5 | 4.9 | 72.8 | West |
| Pennsylvania | 4.9 | 9.8 | 85.7 | 24.3 | 22.8 | 4.2 | 84.5 | Northeast |
| Rhode_Island | 4.3 | 10.2 | 81.3 | 26.4 | 27.4 | 4.1 | 93.8 | Northeast |
| South_Carolina | 5.8 | 12 | 83 | 19 | 27.1 | 3.9 | 70.2 | South |
| South_Dakota | 0.9 | 9.4 | 91.8 | 25.7 | 20.7 | 2.3 | 34.5 | North Central |
| Tennessee | 7.2 | 13.4 | 79.9 | 22 | 27.9 | 3.9 | 67.9 | South |
| Texas | 5.9 | 14.9 | 79.2 | 23.9 | 21.5 | 4.2 | 84.6 | South |
| Utah | 1.9 | 8.1 | 90.7 | 26.4 | 13.6 | 3.2 | 76.4 | West |
| Vermont | 1.5 | 10.3 | 90 | 28.8 | 22.5 | 2.9 | 27.9 | Northeast |
| Virginia | 5.7 | 8.1 | 86.6 | 31.9 | 22.2 | 2.2 | 78.2 | South |
| Washington | 3.3 | 9.5 | 91.8 | 28.6 | 22.1 | 5.2 | 83 | West |
| West_Virginia | 2.5 | 15.8 | 77.1 | 15.3 | 22.3 | 5.5 | 41.9 | South |
| Wisconsin | 3.2 | 9 | 86.7 | 23.8 | 21.7 | 3.5 | 67.8 | North Central |
| Wyoming | 2.4 | 11.1 | 90 | 20.6 | 20.8 | 3.9 | 29.6 | West |