1 Which of the following is NOT true about linear regression?…

Question Answered step-by-step 1 Which of the following is NOT true about linear regression?… 1 Which of the following is NOT true about linear regression?Review LaterLinear regression allows us to predict new values of the independent variable.Linear regression allows us to model how the target variable changes with the independent variables.In linear regression, the target variable is a continuous quantity.Linear regression is used to predict new values of the target variable. 2 The ordinary least squares (OLS) algorithm ________________ .Review LaterMaximizes the sum of square residualsMinimizes the sum of square residualsMinimizes the square of the sum of residualsMaximizes the square of the sum of residuals 3 Overfitting occurs when _____________.Review LaterThe sum of square residuals is too largeOur model does not have enough complexityThe average of the errors is positiveOur model becomes too specific to the training data 4. Using multiple linear regression to add in more independent variables ___________.Review Later can help explain more variation in the target variableallows us to fit a non-linear model to the dataallows us to add more observational data to the modelreduces the overfitting of the data5. Multicollinearity is the phenomenon where _________________.Review Laterthe independent variables are strongly correlated with the residualsthe target variable is strongly correlated with the residualsthe independent variables are strongly correlated with other independent variablesthe target variable is strongly correlated with an independent variable 6 Which of the following is NOT an assumption of ordinary least squares (OLS):Review LaterHomoscedasticity of ErrorsEndogeneityRandom SamplingLinearity 7 Which assumption of OLS assumes that there is no correlation between the error and the independent variables?Review LaterZero Mean ErrorsMulticollinearityEndogeneityAutocorrelation of Errors 8 A regression analysis between sales (S) (in $1000) and price (P) (in dollars) resulted in the following equation:S = 50,000 – 8P The above equation implies that an ___________. Review Laterincrease of $1 in price is associated with a decrease of $8 in salesincrease of $1 in price is associated with a decrease of $8000 in salesincrease of $1 in price is associated with a decrease of $42,000 in salesincrease of $8 in price is associated with an increase of $8,000 in sales9 Which of the following is the formula for the mean square error?Review Later Image transcription textN Elyi – yil O E(vi- yi) 2 1 – E(vi-y)2 O LE(Vi – y 2 O E(vi – y)2… Show more10 Suppose we build a model to predict a store’s sales with three independent variables; customers per day, average daily temperature, and number of products available. If we calculate the p-values for these variables as below, which variables are significant and should be kept in the model? Select all that apply. Variablep-ValueCustomers per day (I)0.0Average daily temperature (II)0.54Number of products available (III)0.03 Review LaterVariable IVariable IIVariable III 11 Suppose we have produced a simple linear regression model with the following form: y = 0.65x + 2.9We then calculate the coefficient of determination as 0.92 and a p-value of 0.1. Which of the following best describes our model? Review LaterThe model explains a high amount of variance, and the slope is statistically significantThe model explains a high amount of variance but the slope is statistically insignificantThe model explains a low amount of variance, but the slope is statistically significantThe model explains a low amount of variance but the slope is statistically significant 12 Which of the following evaluation metrics is relative to the total error?Review LaterMean absolute errorMean square errorRoot mean square errorCoefficient of determination 13 Which method of regression produces a probability distribution as opposed to a point estimate?Review Later Bayesian RegressionPoisson RegressionLASSO RegressionLogistic Regression 14 You are given a dataset of air pollution readings from several locations in an urban setting. The measurements are taken every hour and include information about traffic flow. To perform regression on this longitudinal data, what kind of regression technique would you use?Review Later Repeated Measures RegressionLASSO RegressionLog-Log RegressionPolynomial Regression 15 You are working with customer data from a large video-on-demand provider, which contains 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?Review Later Random EffectsPoisson RegressionLogistic RegressionBayesian Regression  Math Statistics and Probability DATA SCIEN PH12.7X Share QuestionEmailCopy link Comments (0)