Answer Happy

The most important thing is that you show your workfor NUMBER 1 and you explain in full detail how you got theanswers. please put the graphs separately and explain each one anddo it IN EXCEL for each graph READ QUESTION ONE FOR MORE AND OTHERDETAILS.
FOR QUESTION 2 PLEASE LIST EACH CORRELATION MATRIXFOR THE 6 VARIABLES PLEASE EXPLAIN IN FURTHR DETAILS MUST SHOWWORK
I WILL GIVE YOU A THUMBS UP
The Fresh Detergent Case Enterprise Industries produces Fresh, a brand of liquid detergent. In order to more effectively manage its inventory, the company would like to better predict demand for Fresh. To develop a prediction model, the company has gathered data concerning demand for Fresh over the last 60 sales periods. Each sales period is defined as one month. The variables are as follows: Demand = Y = demand for a large size bottle of Fresh (in 100,000) Price = the price of Fresh as offered by Ent. Industries AIP= the average industry price ADV = Ent. Industries Advertising Expenditure (in $100,000) to Promote Fresh in the sales period. DIFF = AIP - Price = the "price difference" in the sales period 1- Make time series scatter plots of all five variables (five graphs). Insert trend line, equation, and R-squared. Observe graphs and provide interpretation of results. 2- Obtain the correlation matrix for all six variables and list the variables that have strong correlation with Demand. High correlation is r> 0.70. Explain your findings in plain language. 3- Use 3-month and 6-month moving averages to predict the demand for January 2021. Find MAD for both forecasts and identify the preferred one based on each calculation. Is the moving average suitable method for forecasting for this data set? Explain your reasoning.
4- Use Exponential smoothing forecasts with alpha of 0.1, 0.2, ..., 0.9 to predict January 2021 demand. Identify the value of alpha that results in the lowest MAD. 5- Find the monthly seasonal indices for the demand values using Simple Average (SA) method. Find the de-seasonalized demand values by dividing monthly demand by seasonal indices. 6- Use regression to perform trend analysis on the de-seasonalized demand values. Is trend analysis suitable for this data? Find MAD, the seasonally adjusted trend forecasts for January through March 2021 and explain the Excel Regression output (trend equation, r, r- squared, goodness of model).
Month/Yr. Jan. 2016 PERIOD 1 2 3 4 5 6 7 8 9 10 11 12 PRICE AIP DIFF ADV 5.4 5.9 0.5 5.3 5.5 6.6 1.1 6.8 6.0 6.4 0.4 7.3 6.1 6.1 0.0 7.3 5.9 6.4 0.5 7.2 5.9 6.3 0.4 6.5 5.9 6.0 0.1 6.8 6.8 6.0 -0.8 5.0 6.8 5.8 -1.0 5.8 6.4 6.3 -0.1 5.5 6.5 6.3 -0.2 6.5 6.3 6.2 -0.1 6.3 DEMAND 13.9 14.5 14.7 14.9 14.9 14.6 14.1 12.0 14.2 13.9 13.9 13.8
Jan. 2017 13 14 15 16 17 18 19 20 21 22 23 24 6.1 6.1 6.0 6.4 6.2 6.0 6.1 6.4 6.0 6.2 6.1 6.0 6.5 6.6 6.3 6.7 6.5 6.8 6.6 6.1 6.1 6.2 6.0 6.2 0.4 0.5 0.3 0.3 0.3 0.8 0.5 -0.3 0.1 0.0 -0.1 0.2 7.0 7.7 6.8 6.8 7.1 7.0 7.2 7.5 7.8 8.2 8.3 8.4 14.0 14.5 16.0 15.7 15.8 15.2 15.9 16.2 15.0 16.9 17.1 16.9
Jan. 2018 25 26 27 28 29 30 31 32 33 34 35 36 6.1 5.9 6.0 6.3 6.0 5.7 5.6 6.2 6.4 6.5 6.2 6.7 6.7 6.9 5.8 5.8 6.0 6.7 6.4 7.0 7.2 5.9 6.0 6.2 0.6 1.0 -0.2 -0.5 0.0 1.0 0.8 0.8 0.8 -0.6 -0.2 -0.5 8.9 9.1 9.3 9.4 9.3 9.4 9.5 9.6 9.7 9.9 9.8 9.9 17.4 17.7 17.6 18.4 18.6 17.4 18.4 17.6 16.7 18.2 18.5 19.1
Jan. 2019 37 38 39 40 41 42 43 44 45 46 47 48 6.9 6.9 6.7 7.0 7.1 7.2 7.2 7.3 7.2 7.1 6.9 7.2 6.0 6.3 6.5 6.0 6.1 6.3 6.4 6.5 6.0 6.2 5.9 6.0 -0.9 -0.6 -0.2 -1.0 -1.0 -0.9 -0.8 -0.8 -1.2 -0.9 -1.0 -1.2 10.1 10.2 10.5 10.3 9.9 10.5 10.6 10.5 11.6 10.1 10.3 10.7 19.0 19.0 19.8 19.8 20.0 20.9 19.6 19.5 18.4 19.3 19.3 19.9
Jan. 2020 49 50 51 52 53 54 55 56 57 58 59 60 7.3 7.4 7.5 7.0 6.8 7.4 7.3 7.3 7.2 7.5 7.5 7.5 6.4 6.5 6.5 6.2 6.8 6.9 6.5 6.9 7.0 6.8 6.8 6.5 -0.9 -0.9 -1.0 -0.8 0.0 -0.5 -0.8 -0.4 -0.2 -0.7 -0.7 -1.0 10.9 10.8 11.1 11.2 11.6 11.5 11.6 11.9 11.8 11.9 12.0 11.9 20.0 20.1 20.1 20.2 21.1 20.6 20.7 21.3 21.4 21.5 21.8 21.5
Jan. 2021 Feb. 2021 Mar. 2021 61 62 63