Assignment 2Chocolate Demand ForecastGeneral Research GoalIn your role as a marketing analyst for Cadbury you are asked to forecast the immediate impact ofCadbury’s pricing decisions on sales in the UK. Your research goal is to estimate a forecasting modelthat is as accurate as possible based on the data that is available.DataIn the dataset rawdata_assignment2.csv you will find information on Cadbury sales in 68 weeks inthe UK. Compared to the dataset we have used in the respective tutorial, the dataset has beenextended to contain more specific information on promotional activities of Cadbury and itscompetitors. The variables contained in the dataset are:• Sales1: for Cadbury (number of units sold in hundreds of thousands)• Price1: $ Price for Cadbury• Price2-4: $ Price for competitors Droste, Baronie, Delicata (in that order)• Feature1: $ Spendings for feature-only for Cadbury in hundreds of thousands• Feature2-4: $ Spendings for feature-only for Droste, Baronie, Delicata (in that order) inhundreds of thousands• Display1: $ Spendings for display-only for Cadbury in hundreds of thousands• Display2-4: $ Spendings for display-only for Droste, Baronie, Delicata (in that order) inhundreds of thousands• Fand1: $ Spendings for feature and display for Cadbury in hundreds of thousands• Fand3 and 4: $ Spendings for feature and display for Baronie and for Delicata (in that order)in hundreds of thousandsNote that there is no variable on feature and display for Droste, as this brand did not use bothpromotional techniques in the same week.A display promotion concerns a built up display in a shop, such as shown in Exhibit 1 below. Afeature promotion concerns features of the products, such as 20% extra for free, a free raffle ticket,etc. Exhibit 2 is an example of a feature promotion. Exhibit 1: Example of a display promotionExhibit 2: Example of a feature promotionTask 1 (5 points)You want to estimate a linear regression but you know that price and promotional activities typicallyhave a nonlinear effect on sales so you decide to estimate elasticities. Thus, your first step is toformulate the log-linear variables for all sales, price, and promotional variables. Please note thattaking logs requires positive values of sales, price and promotional variables; if a variable containszeros you will not be able to formulate log-linear values for them. The standard procedure to dealwith this problem is to identify the variables that contain a 0 and to simply代写Research Goal、代写c/c++程序语言、代做 add .01 to eachobservation of the respective variables. You then have positive values and you can formulate a loglineartransformation of the variables subsequently.Please name all variables that required adding .01 to zeros.Task 2 (30 points)Please formulate a linear regression analysis with ln_sales as the dependent variable and the lnrepresentationsof the chosen independent variables. Please explain why you chose this model overother alternatives. Please report your results, evaluate the model and discuss the effects. Task 3 (35)3a) Please use your model to predict weekly sales for a Cadbury price of $1.60. Please use yourmodel coefficients to do so as we did in the lecture and in the tutorial. Write down three steps: 1)Write down your preferred model (from Task 2). 2) Assume that all other variables are zero (pleaseset all variables other than ln_price1 at zero) and add the price information to your model. 3) Predictsales.Tip: Remember that the model coefficients are elasticities! Also make sure that you providesufficient information so that we can evaluate your prediction so write down every step. In this taskit is not the final number that earns the points but the procedure you have followed.3b) Please compare the price of $1.60 with the actual Cadbury prices in our dataset. Is it high or is itlow for Cadbury standards? How about your sales prediction? Please explain why or why not youtrust your results.Task 4 (30)Your Chief Marketing Office (CMO) sees great value in a sales forecasting model such as yours.However, he prefers to weight all pros and cons before actually making model-based pricingdecision.First, please explain to him what your regression model can do and what it cannot do. Second, pointout to him why you think the model offers a good start for further considerations about pricingdecisions. Third, given that you had to work with the data that is available so far, what could you doto test your model? Fourth, highlight 5 additional variables you would consider useful should you beexpected to further improve the performance of the model in the future; please provide yourreasoning.Assessment- Please use the assignment form provided on Moodle- Please use font size 12, double-spaced.- Please do not submit more than 4 pages (including tables etc., excluding the cover page).Penalties may apply for longer submissions.- Please submit one hardcopy no later than Monday, 28 October, 2pm.- Please submit it in the lecture.转自：http://www.3daixie.com/contents/11/3444.html