What dose the F-statistic and Prob (F-statistic) indacete? **OLS Regression Results**- **Dep. Variable:** y- **Model:** OLS- **Method:** Least Squares- **Date:** Sat, 26 Oct 2019- **Time:** 19:47:09- **No. Observations:** 53- **Df Residuals:** 51- **Df Model:** 1- **Covariance Type:** nonrobust---**Summary Statistics:**- **R-squared:** 0.728- **Adj. R-squared:** 0.723- **F-statistic:** 136.7- **Prob (F-statistic):** 4.84e-16- **Log-Likelihood:** -370.13- **AIC:** 744.3- **BIC:** 748.2---**Coefficients:**| | coef | std err | t | P>|t| | [0.025 | 0.975] ||---------|---------|---------|---------|---------|---------|---------|| **const** | 4233.3797 | 1905.887 | 2.221 | 0.031 | 407.153 | 8059.607 || **x1** | 0.8396 | 0.072 | 11.690 | 0.000 | 0.695 | 0.984 |---**Test Statistics:**- **Omnibus:** 17.469- **Prob(Omnibus):** 0.000- **Skew:** -1.320- **Kurtosis:** 4.589- **Durbin-Watson:** 2.129- **Jarque-Bera (JB):** 20.971- **Prob(JB):** 2.79e-05- **Cond. No.:** 1.38e+06**Explanation:**This is a summary of an Ordinary Least Squares (OLS) regression analysis. The dependent variable is labeled "y," and a single predictor variable "x1" has been used. The R-squared value indicates that approximately 72.8% of the variability in the dependent variable can be explained by the model. The F-statistic and its corresponding probability suggest that

The "F value'' and "Prob(F)'' statistics test the overall significance of the regression model.…

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What dose the F-statistic and Prob (F-statistic) indacete?

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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A new framework of data analysis where the new statistical methods are used for interpreting the results and analyzing the data is known as estimation in statistics.

Question

What dose the F-statistic and Prob (F-statistic) indacete?

**OLS Regression Results**

- **Dep. Variable:** y
- **Model:** OLS
- **Method:** Least Squares
- **Date:** Sat, 26 Oct 2019
- **Time:** 19:47:09
- **No. Observations:** 53
- **Df Residuals:** 51
- **Df Model:** 1
- **Covariance Type:** nonrobust

---

**Summary Statistics:**

- **R-squared:** 0.728
- **Adj. R-squared:** 0.723
- **F-statistic:** 136.7
- **Prob (F-statistic):** 4.84e-16
- **Log-Likelihood:** -370.13
- **AIC:** 744.3
- **BIC:** 748.2

---

**Coefficients:**

| | coef | std err | t | P>|t| | [0.025 | 0.975] |
|---------|---------|---------|---------|---------|---------|---------|
| **const** | 4233.3797 | 1905.887 | 2.221 | 0.031 | 407.153 | 8059.607 |
| **x1** | 0.8396 | 0.072 | 11.690 | 0.000 | 0.695 | 0.984 |

---

**Test Statistics:**

- **Omnibus:** 17.469
- **Prob(Omnibus):** 0.000
- **Skew:** -1.320
- **Kurtosis:** 4.589
- **Durbin-Watson:** 2.129
- **Jarque-Bera (JB):** 20.971
- **Prob(JB):** 2.79e-05
- **Cond. No.:** 1.38e+06

**Explanation:**

This is a summary of an Ordinary Least Squares (OLS) regression analysis. The dependent variable is labeled "y," and a single predictor variable "x1" has been used. The R-squared value indicates that approximately 72.8% of the variability in the dependent variable can be explained by the model.

The F-statistic and its corresponding probability suggest that

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