In this problem, we apply logistic regression to the prediction of NBA game results. To load the NBA game data, please call the following command: load(url('https://www.dropbox.com/s/iyqdml9si94zxtf/NBA_1718.rdata?dl=1')) The target variable of the logistic regression is whether the home team wins or loses the given game. We then choose three predictors in the model: name of the home team (TEAM), field goals attempted by the home team (FGA), and the number of defense rebounds caught by the home team (DREB). b. [4 points] Call the glm function to estimate the logistic regression model using the training data frame df_NBA_1718_train. Follow the order of TEAM, FGA, and DREB when specifying the model. Then, save the estimated model in object log_reg. According to the estimated coefficients, is the predictor of the home team's field goals attempted positively correlated with the target variable? Save your answer in logical scaler is_FGA_pos, where TRUE means that the correlation is positive and FALSE means that the correlation is negative. Is the correlation between the predictor of the home team's number of defense rebounds caught and the target variable statistically significant? Recall that the estimated correlation is considered to be statistically significant if the p-value of the estimate is less than 0.1. Save your answer in logical scaler is_DREB_sig, where TRUE means that the correlation is statistically significant and FALSE means that it is not