Regression Fundamentals 1 Which ofthe following is NOT true about linear regression? E] Review Later 0 In linear regression, the target variable is a continuous quantity. Linear regression is used to predict new values of the target variable. Linear regression allows us to model how the target variable changes with the independent variables. Linear regression allows us to predict new values ofthe independent variable. Regression Fundamentals 2 The ordinary least squares (OLS) algorithm . Review Later minimizes the sum of square residuals maximizes the square of the sum of residuals maximizes the sum of square residuals minimizes the sum of distances between values and the regression line Regression Fundamentals 3 Overfitting occurs when . Review Later the average of the errors is positive our model becomes too specific to the training data our model does not have enough complexity the sum of square residuals is too large Regression Fundamentals 4 Using multiple linear regression to add in more independentvariables . REViEW Later reduces the overfitting of the data can help explain more variation in the target variable allows us to add more observational data to the model allows us to fit a nonlinear model to the data Regression Fundamentals 5 Multicollinearity is the phenomenon where _ Review Later the independent variables are strongly correlated with the residuals the target variable is strongly correlated with an independent variable the independent variables are strongly correlated with other independent variables the target variable is strongly correlated with the residuals Regression Fundamentals 6 Which of the following is NOT an assumption of ordinary least squares (0L5): REViEW Later Homoscedasticity of Errors Linearity Random Sampling Endogeneity Regression Fundamentals 7 Which assumption of OLS assumes that there is no correlation between the error and the Review Later independent variables? Endogeneity Zero Mean Errors OOOO Multicollinearity Autocorrelation of ErrorsRegression Fundamentals 8 A regression analysis between sales [5) [in $1000) and price (P) (in dollars) resulted in the following REViEW Later equation: S = 50,000 BP The above equation implies that an increase of $1 in price is associated with a decrease of $42,000 in sales increase of $1 in price is associated with a decrease of $8000 in sales increase of $8 in price is associated with an increase of $8,000 in sales increase of $1 in price is associated with a decrease of $8 in sales Regression Fundamentals 9 Which of the following is the formula for the mean square error? Review Later 207i 302 1 A 32W: Yil gm.- 5292 1 _ 20': 902 EON3732 Regression Fundamentals 10 Suppose we build a model to predict a store's sales with three independent variables; customers REViEW Later per day. average daily temperature, and number of products available. If we calculate the pivalues for these variables as below, which variables are significant and should be kept in the model? Select all that apply. Variable pValue Customers per day (I) 0.0 Average daily temperature (II) 0.54 Number of products available (III) 0.03 Variable | Variable || Variable ||| Regression Fundamentals 1 1 Suppose we have produced a simple linear regression model with the following form: REViEW Later y = 0.65); + 2.9 We then calculate the coefcient of determination as 0.92 and a pvalue of 0.1 . Which of the following best describes our model? "he model explains a high amount of variance but the slope is statistically insignificant "he model explains a high amount of variance, and the slope is statistically Significant "he model explains a low amount ofvariance, but the slope is statistically Significant "he model explains a low amount ofvariance but the slope is statistically significant Regression Fundamentals 12 Which of the following evaluation metrics is relative to the total error? ReViEW Later Mean absolute error Mean square error Root mean square error Coefficient of determination Regression Fundamentals 13 Which method of regression produces a probability distribution as opposed to a point estimate? Review Later Logistic regression Poisson regression OOOO LASSO regression Bayesian regressionRegression Fundamentals 14 You are given a dataset of air pollution readings from several locations in an urban setting. The REViEW Later measurements are taken every hour and include information about trafc ow. To perform regression on this longitudinal data, what kind of regression technique would you use? Polynomial regression LogLog regression LASSO regreSSIon Repeated measures regression Regression Fundamentals 15 You are working with customer data from a large videoondemand provider, which contains REVieW Later numerical fields with information such as average number of hours watched per month, number of logins per month, time spent browsing per month etc. In this data. there is a flag that indicates whether 'the customer canceled the service or not (1 for yes, 0 for no). You are looking to build a model from this data to classify what current customers will cancel. What type of model would you use? Bayesian Regression Poisson Regression Logistic Regression Random Effects