University Level Statistics and Quantitative Methods

Please help by providing a clear and detailed explanation of all the solutions. I want to understand why the answers are what they are. Thank you.

Q1) In Classification models, the best solution is the one that

a) Has the maximum R-square.

b) Has minimum Standard Error.

c) Either of the above is commonly used.

d) Neither of a. and b. is applicable.

Q2) Evaluation is

a) Measuring the accuracy of predictions.

b) Evaluating the process to see what could have been done better.

c) Determining whether we have met the business objectives.

d) All of the above.

Q3) For Value Estimation models, evaluation may include

a) Determining the size of standard errors.

b) Evaluating which variables have the largest impact.

c) Understanding whether the model is too complicated for the client to use.

d) All of the above.

Q4) Accuracy is defined as

a) Percentage of cases predicted correctly.

b) Percentage of positive outcomes that are correctly predicted.

c) Percentage of positive predictions that are correct.

d) None of the above.

Q5) A tabular summary of the frequency or relative frequency of predicted outcomes versus true outcomes is commonly known as

a) A Pivot Table

b) A Contingency Table

c) A Confusion Matrix

d) A Prediction Matrix

Q6) Suppose we are trying to determine whether a patient has cancer and have a test for it. Most of the time, someone with cancer will get a positive test result and someone without cancer will get a negative test result. If we are interested in the proportion of patients with cancer that we correctly identify, then we should look at

a) The Accuracy Rate.

b) The Sensitivity Rate.

c) The Specificity Rate.

d) The Discovery Rate.