What is the equation for your model? ### OLS Regression Results- **Dependent Variable:** total_wins- **Model:** OLS (Ordinary Least Squares)- **Method:** Least Squares- **Date:** Mon, 12 Oct 2020- **Time:** 15:12:43#### Summary Statistics:- **Number of Observations:** 618- **Degrees of Freedom Residuals:** 615- **Degrees of Freedom Model:** 2- **Covariance Type:** nonrobust#### Model Performance:- **R-squared:** 0.837- **Adjusted R-squared:** 0.837- **F-statistic:** 1580- **Prob (F-statistic):** 4.41e-243- **Log-Likelihood:** -1904.6- **AIC:** 3815- **BIC:** 3829#### Coefficients:| Variable | Coefficient | Standard Error | t-value | P>|t| | [95% Confidence Interval] ||----------|-------------|----------------|----------|------|----------------------------|| Intercept| -152.5736 | 4.500 | -33.903 | 0.000| [-161.411, -143.736] || avg_pts | 0.3497 | 0.048 | 7.297 | 0.000| [0.256, 0.444] || avg_elo_n| 0.1055 | 0.002 | 47.952 | 0.000| [0.101, 0.110] |#### Diagnostic Test Statistics:- **Omnibus:** 89.087- **Prob (Omnibus):** 0.000- **Skew:** -0.869- **Kurtosis:** 4.793- **Durbin-Watson:** 1.203- **Jarque-Bera (JB):** 160.540- **Prob (JB):** 1.38e-35- **Condition Number:** 3.19e+04#### Warnings:1. Standard Errors assume that the covariance matrix of the errors is correctly specified.2. The condition number is large, 3.19e+04. This might indicate that

The regression equation is defined as: Y= β0+β1X1+β2X2 Where, Y = dependent varibale Xi =…

HOTSSAT62 ResulCS total wins R-squared: Adj. R-squared: Dep. Variable: 0.837 Model: OLS 0.837 Method: Least Squares F-statistic: 1580. Date: Mon, 12 Oct 2020 Prob (F-statistic): 4.41e-243 Time: 15:12:43 Log-Likelihood: -1904.6 No. Observations: 618 AIC: 3815. Df Residuals: 615 BIC: 3829. Df Model: 2 Covariance Type: nonrobust ==== ===== ====== ====== ===: ===== == coef std err t P>|t| [0.025 0.975] Intercept -152.5736 4.500 -33.903 0.000 -161.411 -143.736 0.3497 0.048 7.297 0.000 0.256 0.444 avg_pts avg_elo_n 0.1055 0.002 47.952 0.000 0.101 0.110 ======== ===== Omnibus: 89.087 Durbin-Watson: 1.203 Prob (Omnibus): Jarque-Bera (JB): Prob (JB): 0.000 160.540 Skew: -0.869 1.38e-35 Kurtosis: 4.793 Cond. No. 3.19e+04 === === Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specif. [2] The condition number is large, 3.19e+04. This might indicate that there are strong multicollinearity or other numerical problems.

HOTSSAT62 ResulCS total wins R-squared: Adj. R-squared: Dep. Variable: 0.837 Model: OLS 0.837 Method: Least Squares F-statistic: 1580. Date: Mon, 12 Oct 2020 Prob (F-statistic): 4.41e-243 Time: 15:12:43 Log-Likelihood: -1904.6 No. Observations: 618 AIC: 3815. Df Residuals: 615 BIC: 3829. Df Model: 2 Covariance Type: nonrobust ==== ===== ====== ====== ===: ===== == coef std err t P>|t| [0.025 0.975] Intercept -152.5736 4.500 -33.903 0.000 -161.411 -143.736 0.3497 0.048 7.297 0.000 0.256 0.444 avg_pts avg_elo_n 0.1055 0.002 47.952 0.000 0.101 0.110 ======== ===== Omnibus: 89.087 Durbin-Watson: 1.203 Prob (Omnibus): Jarque-Bera (JB): Prob (JB): 0.000 160.540 Skew: -0.869 1.38e-35 Kurtosis: 4.793 Cond. No. 3.19e+04 === === Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specif. [2] The condition number is large, 3.19e+04. This might indicate that there are strong multicollinearity or other numerical problems.

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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Equations and Inequations

Equations and inequalities describe the relationship between two mathematical expressions.

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A linear function can just be a constant, or it can be the constant multiplied with the variable like x or y. If the variables are of the form, x2, x1/2 or y2 it is not linear. The exponent over the variables should always be 1.

Question

What is the equation for your model?

### OLS Regression Results

- **Dependent Variable:** total_wins
- **Model:** OLS (Ordinary Least Squares)
- **Method:** Least Squares
- **Date:** Mon, 12 Oct 2020
- **Time:** 15:12:43

#### Summary Statistics:
- **Number of Observations:** 618
- **Degrees of Freedom Residuals:** 615
- **Degrees of Freedom Model:** 2
- **Covariance Type:** nonrobust

#### Model Performance:
- **R-squared:** 0.837
- **Adjusted R-squared:** 0.837
- **F-statistic:** 1580
- **Prob (F-statistic):** 4.41e-243
- **Log-Likelihood:** -1904.6
- **AIC:** 3815
- **BIC:** 3829

#### Coefficients:
| Variable | Coefficient | Standard Error | t-value | P>|t| | [95% Confidence Interval] |
|----------|-------------|----------------|----------|------|----------------------------|
| Intercept| -152.5736 | 4.500 | -33.903 | 0.000| [-161.411, -143.736] |
| avg_pts | 0.3497 | 0.048 | 7.297 | 0.000| [0.256, 0.444] |
| avg_elo_n| 0.1055 | 0.002 | 47.952 | 0.000| [0.101, 0.110] |

#### Diagnostic Test Statistics:
- **Omnibus:** 89.087
- **Prob (Omnibus):** 0.000
- **Skew:** -0.869
- **Kurtosis:** 4.793
- **Durbin-Watson:** 1.203
- **Jarque-Bera (JB):** 160.540
- **Prob (JB):** 1.38e-35
- **Condition Number:** 3.19e+04

#### Warnings:
1. Standard Errors assume that the covariance matrix of the errors is correctly specified.
2. The condition number is large, 3.19e+04. This might indicate that

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