The below table is for Q5 and Q6, which shows the results of a linear regression model: y = Bo + B1X1 + B2X2 + , where y is ETH, X1 is BTC, and X2 is USDT. OLS Regression Results Dep. Variable: Model: Method: Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: ETH OLS Least Squares Sun, 29 Mar 2020 17:42:28 399 396 2 nonrobust R-squared: Adj. R-squared: F-statistic: Prob (F-statistic): Log-Likelihood: 0.687 0.685 434.5 1.34e-100 918.58 -1831. -1819. AIC: BIC: coef std err t P>It (0.025 0.975) const BTC USDT -0.0014 0.9786 0.5199 0.001 0.035 0.391 -1.151 28.011 1.329 0.251 0.000 0.185 -0.004 0.910 -0.249 0.001 1.047 1.289 a. Is y = Bo + B1X1 + B2X2 + a good model? and why? (explain in terms of statistical significance of Bo, B1, B2 with 5% significance level) b. If not, propose a new model of linear regression (exclude some of Bo, B1, B2 from y = Bo + B1X1 + B2X2 + if they are not statistically significant). a. What can we know from the p-value (F-statistic) of 1.34 x 10-10 ? b. What can we know from the R-squared of 0.687? The below table is for Q5 and Q6, which shows the results of a linear regression model: y = Bo + B1X1 + B2X2 + , where y is ETH, X1 is BTC, and X2 is USDT. OLS Regression Results Dep. Variable: Model: Method: Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: ETH OLS Least Squares Sun, 29 Mar 2020 17:42:28 399 396 2 nonrobust R-squared: Adj. R-squared: F-statistic: Prob (F-statistic): Log-Likelihood: 0.687 0.685 434.5 1.34e-100 918.58 -1831. -1819. AIC: BIC: coef std err t P>It (0.025 0.975) const BTC USDT -0.0014 0.9786 0.5199 0.001 0.035 0.391 -1.151 28.011 1.329 0.251 0.000 0.185 -0.004 0.910 -0.249 0.001 1.047 1.289 a. Is y = Bo + B1X1 + B2X2 + a good model? and why? (explain in terms of statistical significance of Bo, B1, B2 with 5% significance level) b. If not, propose a new model of linear regression (exclude some of Bo, B1, B2 from y = Bo + B1X1 + B2X2 + if they are not statistically significant). a. What can we know from the p-value (F-statistic) of 1.34 x 10-10 ? b. What can we know from the R-squared of 0.687