BONUS QUESTION: Sears, one of the most popular mass merchants in the past, has been experiencing market share losses for a quite long time. What kind of retailing innovations would you suggest to the top management of Sears after you took a course that emphasizes radical innovations? Please suggest a couple of ideas and briefly describe them in few sentences each. (3 points) 

For Xiaomi, positioning with a right pricing strategy is critical given its relatively latecomer status. Especially, understanding the concept and factors of price elasticity in the market is critical in developing a successful pricing strategy.   Please define price elasticity. (4 points) Please discuss at least five factors that decrease price elasticity (or consumer price sensitivity) in general and briefly describe what they are. (10 points)

Costco, one of the warehouse club, is known to carry a brand called “Kirkland”. It is an example of ________________.

Question 8: Wine Data – Prediction (8a) 6 pts – Using model3, all_subsets_model, stepwise_model, and ridge_model, give a binary classification to each of the rows in wine_data_test, with 1 indicating a good quality wine. Use 0.5 as your classification threshold.  (8b) 2 pts – For each model, display its accuracy. Hint: Remember that accuracy is the proportion of all responses in the test set that are correctly classified. (8c) 1 pt – Based on 8b, which model performed the best?  (8d) 1.5 pt – If you were to consider other metrics such as sensitivity or specificity, should sensitivity or specificity matter more in the context of this problem? Explain. Note: Don’t calculate these metrics. Hint: Remember that sensitivity is the proportion of all 1s in the test set that are correctly classified as 1s, while specificity is the proportion of all 0s in the test set that are correctly classified as 0s.

Question 7: Wine Data – Regularized Regression (7a) Using wine_data_train, conduct ridge regression with quality as the binary response variable and all other variables in wine_data_train as the predicting variables. (7a.1) 3 pts – Use 10-fold cross validation on the misclassification error to select the optimal lambda value. What optimal lambda value did you obtain? Hint: Make sure to set type.measure=”class” in order to perform cross validation on the misclassification error. If needed, you can take a look at the help file by typing ?cv.glmnet. (7a.2) 1.5 pts – Fit a glmnet object with nlambda = 100. Call it ridge_model.  (7a.3) 1 pt – Display the estimated coefficients at the optimal lambda value.

the spinal cord is anterior to the bodies of the vertebrae

Question 6: Wine Data – Variable Selection (6a) 3 pts – Using wine_data_train, conduct a complete search to find the submodel with the smallest BIC. Fit this model. Include an intercept. Call it all_subsets_model. Display the summary table for the model.  Note: Remember to set family to binomial. (6a.1) 0.5 pts – Which variables are in your all_subsets_model?(6a.2) 1 pt – What is the BIC of all_subsets_model? (6b) 3 pts – Conduct backward stepwise regression on wine_data_train using AIC. Allow the minimum model to be a logistic model with quality as the response variable and only an intercept, and the full model to be model3. Call it stepwise_model. Display the summary table for the model. Note: Remember to set family to binomial. (6b.1) 0.5 pts – Which variables are in your stepwise_model? (6b.2) 0.5 pts – What is the AIC of stepwise_model?

The bone markings with green stickers is the:        

match each brain structure with the information that best describes it

Match each region with its function