**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.

Please check the solution below Excel > Data > Data Analysis > Regression SUMMARY OUTPUT Regression Statistics Multiple R 0.919314382 R Square 0.845138934 Adjusted R Square 0.815357959 Standard Error 2.145196455 Observations 32 ANOVA df SS MS F Significance F Regression 5 652.9701864 130.5940373 28.37848502 9.4418E-10 Residual 26 119.6485636 4.601867831 Total 31 772.61875 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -20.41537127 4.788918241 -4.263044438 0.000234779 -30.2591336 -10.57160894 -30.2591336 -10.57160894 RushingYards/Game 0.115393374 0.021529477 5.359785162 1.30352E-05 0.071138899 0.159647848 0.071138899 0.159647848 PassingYards/Game 0.118844311 0.011401396 10.42366329 8.87319E-11 0.095408407 0.142280216 0.095408407 0.142280216 Penalties -0.009838997 0.023206285 -0.423979824 0.675067874 -0.057540199 0.037862205 -0.057540199 0.037862205 Interceptions 0.249292636 0.106254322 2.346188193 0.026865741 0.030883751 0.467701521 0.030883751 0.467701521 Fumbles 0.114872927 0.10992988 1.044965452 0.305657507 -0.111091175 0.340837029 -0.111091175 0.340837029 Let alpha = 0.05 Penalties, Fumbles P values are > 0.05 Remove Penalties and Fumbles variables and do the regression again Excel > Data > Data Analysis > Regression SUMMARY OUTPUT Regression Statistics Multiple R 0.914726596 R Square 0.836724745 Adjusted R Square 0.819230968 Standard Error 2.122578647 Observations 32 ANOVA df SS MS F Significance F Regression 3 646.4692268 215.4897423 47.82985009 3.81819E-11 Residual 28 126.1495232 4.505340114 Total 31 772.61875 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -21.78304651 3.888244625 -5.602282936 5.36432E-06 -29.74775447 -13.81833856 -29.74775447 -13.81833856 RushingYards/Game 0.11897428 0.020655989 5.759795799 3.49954E-06 0.076662405 0.161286155 0.076662405 0.161286155 PassingYards/Game 0.120459725 0.011179898 10.77467133 1.80785E-11 0.097558742 0.143360707 0.097558742 0.143360707 Interceptions 0.265477652 0.100789933 2.633969909 0.013590485 0.059018835 0.471936468 0.059018835 0.471936468 All variable P values < 0.05 So, 3 Independent variables statistically significant Points/Game = -21.783+0.119*RushingYards/Game+0.120*PassingYards/Game+0.265*Interceptions Option C is correct