The statsmodels ols() method is used on an exam scores dataset to fit a multiple regression model using Exam4 as the response variable. Exam1, Exam2, and Exam3 are used as predictor variables. The general form of this model is:\[ \hat{y} = \beta_0 + \beta_1 \text{Exam1} + \beta_2 \text{Exam2} + \beta_3 \text{Exam3} \]If the level of significance, alpha, is 0.10, based on the output shown, is Exam1 statistically significant in the multiple regression model shown above? Select one.**OLS Regression Results:**- **Dep. Variable:** Exam4- **R-squared:** 0.175- **Model:** OLS- **Adj. R-squared:** 0.126- **Method:** Least Squares- **F-statistic:** 3.576- **Date:** Sun, 19 Nov 2023- **Prob (F-statistic):** 0.0194- **Time:** 10:59:12- **Log-Likelihood:** -189.62- **No. Observations:** 50- **AIC:** 391.2- **Df Residuals:** 46- **BIC:** 398.0- **Df Model:** 3**Covariance Type:** nonrobust| | Coef. | Std Err. | t | P>|t| | [0.025 0.975] ||----------------|-------|---------|-----|-----|------------------|| **Intercept** | 49.5093 | 11.004 | 4.495 | 0.000 | 27.354 71.665 || **Exam1** | -1.8749 | 1.287 | -1.457 | 0.153 | -4.468 0.718 || **Exam2** | -0.0575 | 0.575 | -0.100 | 0.920 | -1.215 1.100 || **Exam3** | 0.9573 | 0.837 | 1.144 | 0.259 |

Regression is the statistical method use to fit a linear relationship between one response variable…

The statsmodels ols() method is used on an exam scores dataset to fit a multiple egression model using Exam4 as the response variable. Exam1, Exam2, and Exam3 are used as predictor variables. The general form of this model is: Ý - Do + P;Exam1 + PzExam2 + PzExam3 f the level of significance, alpha, is 0.10, based on the output shown, is Exam1 statistically significant in the multiple regression model shown above? Select one. A text version of this output is available. OS Regression Results Dep. Vari able: Model: R-squar ed: Adj. R-squared: F-statistic: Prob -statistic): Log-Likelihood: AIC: BIC: 0.178 0.125 3.329 0.0276 169.85 347.7 355.4 Exand OLS Method: Least Squares Sun, 18 Aug 2019 10:59:12 Date: Time: No. Observati ons: Df Residuals: of Model: Covariance Type: 50 46 nanrobust Coef std err P>t| Го.025 0.975) Intercept 46.2612 0.1742 0.1462 10,969 0.120 0.078 0.053 4.217 1.451 1.873 1.085 24. 181 -0.067 0.011 -0,049 68.341 0.416 0,303 0.164 0.000 0.153 Examl Exan2 Exam3 0.067 0.0575 0.284 Onnibus : Prob(Onnibus): skewi Kurtos is: Durbin-Watson: Jarque-Rera (3K): Prob(JB): Cond. No. 0.886 0.642 1.530 0.738 0.691 1.4ler03 0.290 2.868 Null Hypothesis Ho i =0 Alternative Hypothesis H,: B1 =0 P-value - 0.153 Since the P-value is greater than the level of significance, alpha, do not reject the null hypothesis. Conclude that the estimate for the Exam1 parameter is zero. Therefore, Exam1 is not statistically significant in the multiple regression model. ONull Hypothesis H, i =0 Alternative Hypothesis H B, +0 P-value = 0.153 Since the P-value is greater than the level of significance, alpha, reject the null hypothesis. Conclude that the estimate for the Exam1 parameter is non-zero. Therefore, Exam1 is statistically significant in the multiple regression model. ONull Hypothesis H. i =0 Alternative Hypothesis H: Ba +0 P-value = 0.1742 Since the P-value is greater than the level of significance, alpha, reject the null hypothesis and conclude that the estimate for the Exam1 parameter is non-zero. Therefore, Exam1 is statistically significant in the multiple regression model. O Null Hypothesis H. i =0 Alternative Hypothesis H: B, = 0 P-value = 0.1742 Since the P-value is greater than the level of significance, alpha, do not reject the null hypothesis. Conclude that the estimate for the Exam1 parameter is zero. Therefore, Exam1 is not statistically significant in the multiple regression model.