varl var2 var3 var4 var5 varб Points/Game Rushing Yards/ Passing Yards/ Penalties Interceptions Fumbles 25.2 90.1 259.2 140 18 4 16.2 95.2 208.5 108 16 5 17.2 112.3 187.8 110 17 15.8 112.7 166.4 81 18 5 16.7 114.5 175.5 98 14 20.9 83.2 214.9 114 16 10 23.8 97.5 253.1 93 19 6. 25.1 118.1 232.2 117 17 3 28.4 109.9 251.5 107 19 3 20 122.6 222.8 93 14 21.6 80.4 243.7 103 17 11 27.2 99.2 274.5 116 19 23.7 99.8 235.7 85 11 7 28.1 106.2 256.2 70 22 8 25.7 149.6 207.3 79 20 14.1 78.7 198.4 104 14 1 16.7 98.8 189.6 94 14 1 22.8 164.9 178.2 89 15 6. 36.8 115.4 297.1 81 19 5 23.7 91.3 266.2 71 13 3 23.3 134.2 195.8 80 15 3 16.8 106.1 184.7 66 15 -1 17.7 130.5 163.8 123 18 21 123.8 232.4 86 11 24.6 135.1 191.2 83 11 7 25.8 127.2 182.3 97 30 13.7 92.3 143.6 90 12 3 24.6 101.6 244.5 62 20 7 16.4 95.7 205.8 97 18 2 20.9 117.2 206.3 84 16 12 18.8 131.4 177.2 104 22 5 20.9 116.5 218.1 93 14 3 1. 1.
varl var2 var3 var4 var5 varб Points/Game Rushing Yards/ Passing Yards/ Penalties Interceptions Fumbles 25.2 90.1 259.2 140 18 4 16.2 95.2 208.5 108 16 5 17.2 112.3 187.8 110 17 15.8 112.7 166.4 81 18 5 16.7 114.5 175.5 98 14 20.9 83.2 214.9 114 16 10 23.8 97.5 253.1 93 19 6. 25.1 118.1 232.2 117 17 3 28.4 109.9 251.5 107 19 3 20 122.6 222.8 93 14 21.6 80.4 243.7 103 17 11 27.2 99.2 274.5 116 19 23.7 99.8 235.7 85 11 7 28.1 106.2 256.2 70 22 8 25.7 149.6 207.3 79 20 14.1 78.7 198.4 104 14 1 16.7 98.8 189.6 94 14 1 22.8 164.9 178.2 89 15 6. 36.8 115.4 297.1 81 19 5 23.7 91.3 266.2 71 13 3 23.3 134.2 195.8 80 15 3 16.8 106.1 184.7 66 15 -1 17.7 130.5 163.8 123 18 21 123.8 232.4 86 11 24.6 135.1 191.2 83 11 7 25.8 127.2 182.3 97 30 13.7 92.3 143.6 90 12 3 24.6 101.6 244.5 62 20 7 16.4 95.7 205.8 97 18 2 20.9 117.2 206.3 84 16 12 18.8 131.4 177.2 104 22 5 20.9 116.5 218.1 93 14 3 1. 1.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcribed Image Text:Below is the transcription of a table containing data related to football game statistics, suitable for inclusion on an educational website.
---
**Football Game Statistics**
| Points/Game | Rushing Yards | Passing Yards | Penalties | Interceptions | Fumbles |
|-------------|---------------|---------------|-----------|---------------|---------|
| 25.2 | 90.1 | 259.2 | 140 | 18 | 4 |
| 16.2 | 95.2 | 208.5 | 108 | 16 | 5 |
| 17.2 | 112.3 | 187.8 | 140 | 17 | 4 |
| 15.8 | 112.7 | 166.4 | 81 | 18 | 3 |
| 16.7 | 114.5 | 175.5 | 98 | 14 | 3 |
| 20.9 | 83.1 | 198.1 | 94 | 12 | 9 |
| 23.8 | 97.5 | 253.1 | 93 | 9 | 3 |
| 20.9 | 118.1 | 232.2 | 114 | 11 | 9 |
| 28.4 | 109.9 | 251.5 | 107 | 19 | 3 |
| 20 | 122.6 | 222.8 | 93 | 14 | 6 |
| 21.6 | 80.4 | 243.7 | 103 | 17 | 11 |
| 27.2 | 99.2 | 274.5 | 110 | 15 | 9 |
| 23.7 | 99.8 | 235.7 | 85 | 11 | 7 |
| 28.1 | 106.2 | 256.2 | 70 | 14 | 5 |
| 25.
![**Title: Determining the Best Multiple Regression Model for Predicting NFL Team Scores**
**Introduction:**
This exercise aims to guide you through using the p-value criterion to identify the best model for predicting the average number of points scored per game by football teams based on the available National Football League data.
**Instructions:**
We will use multiple regression analysis to build a predictive model. In this context, several variables are considered:
- **X₁** represents Rushing Yards
- **X₂** represents Passing Yards
- **X₃** represents Penalties
- **X₄** represents Interceptions
- **X₅** represents Fumbles
You must enter the coefficients in the equation and ensure the Xₖ-values are ordered numerically by their subscript.
**Model Options:**
*Select the most suitable equation for "Points/Game" by entering values in the corresponding placeholders.*
A. Points/Game = [ ] + [ ]X₁ + [ ]X₂ + [ ]X₃ + [ ]X₄ + [ ]X₅
B. Points/Game = [ ] + [ ]X₁ + [ ]X₂ + [ ]X₅
C. Points/Game = [ ] + [ ]X₂ + [ ]X₄ + [ ]X₅
D. Points/Game = [ ] + [ ]X₁ + [ ]X₂ + [ ]X₃ + [ ]X₅
E. Points/Game = [ ] + [ ]X₄ + [ ]X₅
**Note:** Round your responses to three decimal places where applicable.
**Conclusion:**
Once the best regression model is selected using statistical software to calculate p-values, analyze whether this model is logically sound for predicting NFL team scores. Consider how each variable may impact scoring based on your football knowledge.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F9e048f0b-5f9a-4230-bf35-655b1c461c20%2F8a77b97f-2e3f-4959-8216-6537ab383d8d%2Fh5yuyi6_processed.png&w=3840&q=75)
Transcribed Image Text:**Title: Determining the Best Multiple Regression Model for Predicting NFL Team Scores**
**Introduction:**
This exercise aims to guide you through using the p-value criterion to identify the best model for predicting the average number of points scored per game by football teams based on the available National Football League data.
**Instructions:**
We will use multiple regression analysis to build a predictive model. In this context, several variables are considered:
- **X₁** represents Rushing Yards
- **X₂** represents Passing Yards
- **X₃** represents Penalties
- **X₄** represents Interceptions
- **X₅** represents Fumbles
You must enter the coefficients in the equation and ensure the Xₖ-values are ordered numerically by their subscript.
**Model Options:**
*Select the most suitable equation for "Points/Game" by entering values in the corresponding placeholders.*
A. Points/Game = [ ] + [ ]X₁ + [ ]X₂ + [ ]X₃ + [ ]X₄ + [ ]X₅
B. Points/Game = [ ] + [ ]X₁ + [ ]X₂ + [ ]X₅
C. Points/Game = [ ] + [ ]X₂ + [ ]X₄ + [ ]X₅
D. Points/Game = [ ] + [ ]X₁ + [ ]X₂ + [ ]X₃ + [ ]X₅
E. Points/Game = [ ] + [ ]X₄ + [ ]X₅
**Note:** Round your responses to three decimal places where applicable.
**Conclusion:**
Once the best regression model is selected using statistical software to calculate p-values, analyze whether this model is logically sound for predicting NFL team scores. Consider how each variable may impact scoring based on your football knowledge.
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