**Title: Applying the P-Value Criterion to Multiple Regression Models in NFL Data****Introduction:**Understanding how to use statistical models to predict outcomes in sports can reveal insights and help make data-driven decisions. This educational segment focuses on using the p-value criterion to select the optimal model for predicting football teams' points per game, specifically with NFL data.**Instructions:**- **Step 1:** Access the "National Football League Data" by clicking the provided link. This dataset will contain the necessary variables related to football performance metrics. - **Step 2:** Determine the best multiple regression model using the variables provided in the dataset.**Variables Explained:**- \( X_1 \): Rushing Yards- \( X_2 \): Passing Yards- \( X_3 \): Penalties- \( X_4 \): Interceptions- \( X_5 \): Fumbles**Objective:**Your task is to enter the terms of the regression equation in a logical sequence so that the \( X \)-values are arranged in ascending order based on their numerical indices. Use the provided options (A to E) to make your choice.**Response Options:**- **A.** \[ \text{Points/Game} = \square + \square \times X_1 + \square \times X_2 + \square \times X_3 + \square \times X_4 + \square \times X_5 \]- **B.** \[ \text{Points/Game} = \square + \square \times X_1 + \square \times X_3 + \square \times X_2 + \square \times X_5 + \square \times X_4 \]- **C.** \[ \text{Points/Game} = \square + \square \times X_1 + \square \times X_5 + \square \times X_4 + \square \times X_2 + \square \times X_3 \]- **D.** \[ \text{Points/Game} = \square + \square \times X_1 + \square \times X_3 + \square \times X_4 + \square \times X_5 + \square \times X_2 \]- **E.** \[ \text{Points/Game} = \square + \square \times X_1 ### Football Statistics DataThis table presents a comprehensive dataset of football team statistics. The table includes various performance metrics per game, represented in rows and columns. Each row corresponds to a different team, while columns categorize specific statistical measures.- **var1 (Points/Game):** Average points scored per game by the team.- **var2 (Rushing Yards/Game):** Average rushing yards gained per game.- **var3 (Passing Yards/Game):** Average passing yards gained per game.- **var4 (Penalties):** Total number of penalties incurred.- **var5 (Interceptions):** Total number of interceptions made.- **var6 (Fumbles):** Total number of fumbles.#### Example Data Points:1. **Team 1:** - Points/Game: 25.2 - Rushing Yards/Game: 90.1 - Passing Yards/Game: 259.2 - Penalties: 140 - Interceptions: 18 - Fumbles: 42. **Team 14:** - Points/Game: 23.7 - Rushing Yards/Game: 99.8 - Passing Yards/Game: 235.7 - Penalties: 85 - Interceptions: 11 - Fumbles: 73. **Team 20:** - Points/Game: 36.8 - Rushing Yards/Game: 115.4 - Passing Yards/Game: 297.1 - Penalties: 81 - Interceptions: 9 - Fumbles: 9### Analysis & Insights:- The data provides insights into team performance and areas for improvement.- Teams with higher penalties may need to focus on discipline during gameplay.- The balance between rushing and passing yards can indicate a team's strategic focus.This dataset is valuable for analyzing team performance patterns and making data-driven decisions in sports management and coaching.

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Answered: Use the p-value criterion to find the…

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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Please answer parts d, e and f.
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