The product manager for a brand of all-natural herbal shampoo has compiled 15 weeks of data on the weekly sales of the brand (in units), the level of media advertising (in thousands of dollars), the price (in dollars), and the use of displays (in number of stores with the brand on an end-aisle display).  She then carried out a multiple regression analysis on these data in order to calculate a price elasticity.  Her data and the results of the regression analysis can be seen below. Name each of the variables that were used in this multiple regression analysis.  For each of these variables, indicate whether it was an independent variable or a dependent variable in this regression analysis. Use these data and the results of the regression analysis to calculate the price elasticity for this shampoo brand.  Show your work. Based on the course material, describe two issues that should lead the product manager to not entirely trust the price elasticity calculated from this analysis.

A major brokerage company has an office in Miami, Florida. The manager of the office is evaluated based on the number of new clients generated each quarter. Data were collected that show the number of new customers added during each quarter between 2015 and 2018. A multiple regression model was developed with the number of new customers as the dependent and the following four independent variables: Period (1, …, 16): A variable that measures the trend; Q1 = 1 for first quarter, Q1 = 0 otherwise; Q2 = 1 for second quarter, Q2 = 0 otherwise; Q3 = 1 for third quarter, Q3 = 0 otherwise. Questions:   1. Explain each of the four slopes (Period, Q1, Q2, Q3).   2. How many new customers would you expect in the second quarter of the following year (2019)?

A logistic regression was used to investigate obesity and poor physical health while controlling for the following variables: age, gender, race, income, health status, education, current smoker, and diet/exercise status.  Justify the use of a logistic regression.

During the 1950's and 1960's the average weight of vehicles sold in the U.S. was well over 4,000 pounds. There was a dip in the average weights in the 1970's and 1980's, due possibly to both higher demand for better gas mileage and a world-wide shortage of crude oil. Then in the 1990's and early 2000's the average weight of vehicles had a steady increase. A regression analysis was completed on the average weight of the 10 most commonly sold vehicles in the U.S. from the years 2012 through the year 2020 and yielded the following results, where the independent variable is the year and the predicted variable is the average weight of the 10 most popular vehicles.             Correlation of "Average Weight" and "Year" = r = 0.9283             The regression equation is "Average Weight"  =  –124,960.73 + 63.82(Year)   Predict the Average Weight to the nearest pound for the 2022 Year. Group of answer choices A. 4057 pounds B. 4015 pounds C. This value of Year is beyond the scope of the…