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Suppose we have a data set with five predictors, X1 =GPA, X2 = SAT, X3 = Gender (1 for Male and 0 for Female), X4 = Interaction between GPA and SAT, and X5 = Interaction between GPA and Gender. The response variable is starting salary after graduation (in thousands of dollars). We fit the model using least squares and obtain the following estimated coefficients: Y ^ = β 0 + β 1 X 1 + β 2 X 2 + β 3 X 3 + β 4 X 4 + β 5 X 5 {“version”:”1.1″,”math”:”\hat{Y} = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \beta_3 X_3 + \beta_4 X_4 + \beta_5 X_5″} with estimates β0=40, β1=15, β2=0.05, β3 =-20, β4=0.002, β5 = 8. (Note: Interpret the following questions strictly based on the fitted model. We are not making claims about real-world gender differences.) i.  For a fixed value of SAT and GPA, males earn more on average than females.  _______ (true or false) ii. For a fixed value of SAT and GPA < 2.5, females earn more on average than males. _______ (true or false) iii. For each additional point of GPA, the increase in predicted salary is larger for males than for females. _______ (true or false) iv.  For a fixed value of IQ and GPA, females earn more on average than males provided that the GPA is high enough. _______ (true or false) v.  Since the coefficient for the GPA/IQ interaction term is very small, there is very little evidence of an interaction effect. _______ (true or false) vi. Predict the salary of a female with SAT of 110 and a GPA of 4.0. The salary is (keep one decimal place) _______

What is the primary goal of the bootstrap compared to cross-validation?

A binary classifier was tested on 100 patients for detecting a disease. The results are shown in the confusion matrix below:                                Actual Positive      Actual Negative   Predicted Positive             30                     10Predicted Negative           20                     40 Please calculate the following metrics, rounding each result to the nearest whole percentage:(a) The Accuracy of the classifier is _______ % (b) The Recall (Sensitivity) for the positive class is _______ %(c) The Precision for the positive class is _______ %

Which of the following is an example of a non-parametric method?

This problem mainly converts odds to some related statistics.i. On average, what fraction of people with an odds of 0.37 of defaulting on their credit card payment will in fact default?  _______ ____ (keep two decimal place) ii. Suppose that an individual has a 16% chance of defaulting on her credit card payment. What are the odds that she will default? _______ _____(keep two decimal place)

I collect a set of data (n  = 100 observations) containing a single predictor and a quantitative response. I then fit a linear regression model to the data, as well as a separate cubic regression, i.e. Y  = β0  + β1 X  + β2 X^2  + β3 X^3  + ε. Suppose that the true relationship between X and Y is not linear, but we don’t know how far it is from linear. Now consider the training residual sum of squares (RSS) for these two models. Which of the following statements is most accurate?

A binary classifier was tested on 100 patients for detecting a disease. The results are shown in the confusion matrix below:                                Actual Positive      Actual Negative   Predicted Positive             25                    15Predicted Negative           20                     40 Please calculate the following metrics, rounding each result to the nearest whole percentage:(a) The Accuracy of the classifier is _______ % (b) The Recall (Sensitivity) for the positive class is _______ %(c) The Precision for the positive class is _______ %

Which of the following statements about Recall (Sensitivity) is correct?

Which of the following statements concerning the impacts of plate tectonics or mass extinctions on the Mesozoic world is FALSE?

Which of the following statements concerning past or present mass extinctions is FALSE?