Forecasting Case Analysis
MGT 3332
Spring 2018
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 33 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- Download the data from Course Blackboard site into Excel spreadsheet.
2- 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.
· In this graph, the demand seems to be fluctuating between highs and lows. If the demand increases for a certain period of time, then it comes back down for almost the same period of time.
· In this graph, there is a steady increase in the advertisement done by the company. In other words, Enterprise Industries have invested more and more in advertisement and in promoting their product as time increases.
· In this scatter plot, the price differences compared to the industry’s average has been very inconsistent. Majority of the time, Fresh’s price has been lower than the average industry price.
· The Average Price Industry (API) varies greatly. The trend line shows a slight increase, but it is very inconsistent.
· The scatter plot shows that Fresh’s prices have been consistent; staying between $5 to $7 max.
3- Construct scatter plots of Demand vs. DIFF and Demand vs. ADV, Demand vs. AIP, and Demand vs. Price. Insert fitted line, equation, and R-squared. Observe graphs and provide interpretation. Note that Demand is always on the Y axis.
4- Obtain the correlation matrix for all six variables and list the variables that have strong correlation with Demand. High correlation is r > 0.50. Explain your findings in plain language.
|
|
PERIOD |
PRICE |
AIP |
DIFF |
ADV |
DEMAND |
|
PERIOD |
1 |
|||||
|
PRICE |
-0.38396 |
1 |
||||
|
AIP |
0.290259 |
-0.23374 |
1 |
|||
|
DIFF |
0.426187 |
-0.76244 |
0.807343 |
1 |
||
|
ADV |
0.814258 |
-0.55717 |
0.299438 |
0.537413 |
1 |
|
|
DEMAND |
0.691026 |
-0.64098 |
0.299191 |
0.588114 |
0.783047 |
1 |
5- Use 3-month and 6-month moving averages to predict the demand for March 2018. 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.
|
3m MA |
6m MA |
|
|
17.6666667 |
18.16666667 |
|
|
MAD= |
1.02282051 |
1.353205128 |
6- Use Exponential smoothing forecasts with alpha of 0.1, 0.2, …, 0.9 to predict March 2018 demand. Identify the value of alpha that results in the lowest MAD.
|
Alpha |
Forecast |
MAD |
|
0.1 |
17.2094 |
1.225115 |
|
0.2 |
17.73848 |
1.043501 |
|
0.3 |
17.77822 |
0.946974 |
|
0.4 |
17.67187 |
0.872781 |
|
0.5 |
17.5341 |
0.812573 |
|
0.6 |
17.4067 |
0.74903 |
|
0.7 |
17.30053 |
0.690569 |
|
0.8 |
17.21533 |
0.643283 |
|
0.9 |
17.14885 |
0.604965 |
7- 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 corresponding seasonal indices.
|
Month |
2015 |
2016 |
2017 |
2018 |
Monthly |
Seasonal Indices |
|
Avg. |
||||||
|
Jan |
|
13.9 |
16 |
17.5 |
15.8 |
0.98 |
|
Feb |
|
13.3 |
15.2 |
17.1 |
15.2 |
0.94 |
|
Mar |
|
13.12 |
15.3 |
|
14.2 |
0.88 |
|
Apr |
|
13.8 |
15.9 |
|
14.9 |
0.92 |
|
May |
|
14.8 |
16.2 |
|
15.5 |
0.96 |
|
Jun |
14.4 |
15.3 |
17.5 |
|
15.7 |
0.97 |
|
Jul |
15.3 |
16.3 |
18.4 |
|
16.7 |
1.03 |
|
Aug |
16.5 |
17.5 |
19.4 |
|
17.8 |
1.10 |
|
Sep |
16.1 |
17.4 |
19.1 |
|
17.5 |
1.08 |
|
Oct |
16 |
17.1 |
18.7 |
|
17.3 |
1.07 |
|
Nov |
15.5 |
16.8 |
18.2 |
|
16.8 |
1.04 |
|
Dec |
15.2 |
16.5 |
18.4 |
|
16.7 |
1.03 |
|
|
|
|
|
Grand Avg. = |
16.2 |
12.00 |
8- Use regression to perform trend analysis on the de-seasonalized demand values. Is trend analysis suitable for this data? Find MAD and explain the Excel Regression output (trend equation, r, r-squared, goodness of model).
SUMMARY OUTPUT
|
Regression Statistics |
|
|
Multiple R |
0.944055 |
|
R Square |
0.89124 |
|
Adjusted R Square |
0.887732 |
|
Standard Error |
0.422078 |
|
Observations |
33 |
|
ANOVA |
|||||
|
|
df |
SS |
MS |
F |
Significance F |
|
Regression |
1 |
45.25579 |
45.25579 |
254.032 |
1.7435E-16 |
|
Residual |
31 |
5.522648 |
0.17815 |
||
|
Total |
32 |
50.77844 |
|
|
|
|
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
|
Intercept |
14.08368 |
0.150353 |
93.6705 |
1.34E-39 |
13.77703166 |
|
PERIOD |
0.122986 |
0.007716 |
15.93838 |
1.74E-16 |
0.107248571 |
|
Demand = 14.08 + .1229(Period) |
|
||||
|
RESIDUAL OUTPUT |
MAD= |
0.312893 |
|||
|
Observation |
Predicted Des. Demand |
Residuals |
Abs. Res. |
||
|
1 |
14.20667 |
0.597063 |
0.597063 |
||
|
2 |
14.32965 |
0.518488 |
0.518488 |
||
|
3 |
14.45264 |
0.540527 |
0.540527 |
||
|
4 |
14.57562 |
0.276575 |
0.276575 |
||
|
5 |
14.69861 |
0.289292 |
0.289292 |
||
|
6 |
14.8216 |
0.071704 |
0.071704 |
||
|
7 |
14.94458 |
-0.22293 |
0.222933 |
||
|
8 |
15.06757 |
-0.83815 |
0.838153 |
||
|
9 |
15.19056 |
-1.03792 |
1.037916 |
||
|
10 |
15.31354 |
-0.37978 |
0.379783 |
||
|
11 |
15.43653 |
-0.40573 |
0.405731 |
||
|
12 |
15.55951 |
-0.11553 |
0.115528 |
||
|
13 |
15.6825 |
0.046462 |
0.046462 |
||
|
14 |
15.80549 |
0.013121 |
0.013121 |
||
|
15 |
15.92847 |
-0.02663 |
0.026631 |
||
|
16 |
16.05146 |
-1.30E-05 |
1.34E-05 |
||
|
17 |
16.17444 |
-0.15612 |
0.156124 |
||
|
18 |
16.29743 |
-0.15501 |
0.155015 |
||
|
19 |
16.42042 |
-0.43968 |
0.439678 |
||
|
20 |
16.5434 |
-0.16422 |
0.164219 |
||
|
21 |
16.66639 |
-0.49194 |
0.491945 |
||
|
22 |
16.78938 |
0.625755 |
0.625755 |
||
|
23 |
16.91236 |
0.405731 |
0.405731 |
||
|
24 |
17.03535 |
-0.13044 |
0.130444 |
||
|
25 |
17.15833 |
0.832309 |
0.832309 |
||
|
26 |
17.28132 |
0.575267 |
0.575267 |
||
|
27 |
17.40431 |
0.224021 |
0.224021 |
||
|
28 |
17.52729 |
0.092397 |
0.092397 |
||
|
29 |
17.65028 |
-0.13317 |
0.133168 |
||
|
30 |
17.77326 |
-0.28565 |
0.285648 |
||
|
31 |
17.89625 |
-0.07531 |
0.075306 |
||
|
32 |
18.01924 |
-0.1045 |
0.104504 |
||
|
33 |
18.14222 |
0.054027 |
0.054027 |
9- Find the seasonally adjusted trend forecasts for March through May 2018.
|
Seasonally Adjusted Trend Forecast |
|||
|
Mar. Demand = |
18.26521 |
0.878546 |
16.04683 |
|
Apr. Demand = |
18.3882 |
0.918115 |
16.88248 |
|
May. Demand = |
18.51118 |
0.958302 |
17.7393 |
10- Perform simple linear regression analysis with ADV as the independent variable. Write the complete equation, find MAD and explain the Excel Regression output. Make sure to use the de-seasonalized demand data for this model and all future models.
SUMMARY OUTPUT
|
Regression Statistics |
|
|
Multiple R |
0.830063 |
|
R Square |
0.689004 |
|
Adjusted R Square |
0.678972 |
|
Standard Error |
0.713734 |
|
Observations |
33 |
|
ANOVA |
||||||
|
|
df |
SS |
MS |
F |
Significance F |
|
|
Regression |
1 |
34.98655 |
34.98655 |
68.67976 |
2.32E-09 |
|
|
Residual |
31 |
15.79189 |
0.509416 |
|||
|
Total |
32 |
50.77844 |
|
|
|
|
|
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
|
|
Intercept |
9.102521 |
0.86234 |
10.55561 |
8.67E-12 |
7.343767 |
|
|
ADV |
0.96082 |
0.115938 |
8.287325 |
2.32E-09 |
0.724362 |
|
|
Demand = 9.10 + .9608(ADV) |
||||||
|
RESIDUAL OUTPUT |
MAD= |
0.575824 |
||||
|
Observation |
Predicted Des. Demand |
Residuals |
Abs. Res. |
|||
|
1 |
14.19486 |
0.608864 |
0.608864 |
|||
|
2 |
15.58805 |
-0.73991 |
0.739913 |
|||
|
3 |
16.06846 |
-1.0753 |
1.075298 |
|||
|
4 |
16.1165 |
-1.26431 |
1.264305 |
|||
|
5 |
16.02042 |
-1.03252 |
1.03252 |
|||
|
6 |
15.34785 |
-0.45455 |
0.454548 |
|||
|
7 |
15.58805 |
-0.8664 |
0.866403 |
|||
|
8 |
15.72257 |
-1.49315 |
1.493152 |
|||
|
9 |
14.67527 |
-0.52264 |
0.522636 |
|||
|
10 |
14.38703 |
0.54673 |
0.54673 |
|||
|
11 |
15.34785 |
-0.31705 |
0.317052 |
|||
|
12 |
15.10764 |
0.336342 |
0.336342 |
|||
|
13 |
15.82826 |
-0.0993 |
0.099296 |
|||
|
14 |
15.73218 |
0.08643 |
0.08643 |
|||
|
15 |
15.63609 |
0.265747 |
0.265747 |
|||
|
16 |
15.63609 |
0.415351 |
0.415351 |
|||
|
17 |
15.92434 |
0.09398 |
0.09398 |
|||
|
18 |
15.82826 |
0.314158 |
0.314158 |
|||
|
19 |
15.63609 |
0.344644 |
0.344644 |
|||
|
20 |
15.34785 |
1.031336 |
1.031336 |
|||
|
21 |
16.88516 |
-0.71072 |
0.710715 |
|||
|
22 |
16.50083 |
0.914298 |
0.914298 |
|||
|
23 |
16.1165 |
1.201588 |
1.201588 |
|||
|
24 |
16.30867 |
0.596235 |
0.596235 |
|||
|
25 |
16.88516 |
1.105483 |
1.105483 |
|||
|
26 |
17.07732 |
0.779263 |
0.779263 |
|||
|
27 |
17.46165 |
0.166675 |
0.166675 |
|||
|
28 |
17.94206 |
-0.32237 |
0.322372 |
|||
|
29 |
17.17341 |
0.343705 |
0.343705 |
|||
|
30 |
17.55773 |
-0.07012 |
0.070116 |
|||
|
31 |
18.23031 |
-0.40936 |
0.409363 |
|||
|
32 |
18.03814 |
-0.12341 |
0.123411 |
|||
|
33 |
17.84598 |
0.350271 |
0.350271 |
11- Repeat part (10) with DIFF as the independent variable.
SUMMARY OUTPUT
|
Regression Statistics |
|||||
|
Multiple R |
0.506745 |
||||
|
R Square |
0.25679 |
||||
|
Adjusted R Square |
0.232816 |
||||
|
Standard Error |
1.103353 |
||||
|
Observations |
33 |
||||
|
ANOVA |
|||||
|
|
df |
SS |
MS |
Significance F |
|
|
Regression |
1 |
13.0394 |
13.0394 |
10.71096 |
|
|
Residual |
31 |
37.73904 |
1.217388 |
||
|
Total |
32 |
50.77844 |
|
|
|
|
|
Coefficients |
Standard Error |
t Stat |
P-value |
|
|
Intercept |
15.79168 |
0.224876 |
70.22392 |
9.70E-36 |
|
|
DIFF |
1.730323 |
0.528704 |
3.272761 |
0.002618 |
|
|
Demand = 15.79+1.730(DIFF) |
|||||
|
RESIDUAL OUTPUT |
MAD = |
0.94626 |
|||
|
Observation |
Predicted Des. Demand |
Residuals |
Abs. Res. |
||
|
1 |
15.27258 |
-0.46885 |
0.46885 |
||
|
2 |
16.22426 |
-1.37612 |
1.376117 |
||
|
3 |
16.82987 |
-1.83671 |
1.836705 |
||
|
4 |
15.79168 |
-0.93948 |
0.939477 |
||
|
5 |
16.22426 |
-1.23635 |
1.236355 |
||
|
6 |
16.13774 |
-1.24444 |
1.24444 |
||
|
7 |
16.05122 |
-1.32957 |
1.329574 |
||
|
8 |
15.01303 |
-0.78361 |
0.783614 |
||
|
9 |
14.49393 |
-0.34129 |
0.341295 |
||
|
10 |
15.44561 |
-0.51185 |
0.511853 |
||
|
11 |
16.13774 |
-1.10694 |
1.106944 |
||
|
12 |
15.96471 |
-0.52072 |
0.520723 |
||
|
13 |
16.48381 |
-0.75484 |
0.754843 |
||
|
14 |
16.57032 |
-0.75171 |
0.751715 |
||
|
15 |
16.39729 |
-0.49545 |
0.495448 |
||
|
16 |
16.31077 |
-0.25933 |
0.259328 |
||
|
17 |
16.65684 |
-0.63852 |
0.638517 |
||
|
18 |
16.65684 |
-0.51442 |
0.514422 |
||
|
19 |
16.48381 |
-0.50307 |
0.503067 |
||
|
20 |
15.70516 |
0.674024 |
0.674024 |
||
|
21 |
15.70516 |
0.469285 |
0.469285 |
||
|
22 |
15.61864 |
1.796486 |
1.796486 |
||
|
23 |
16.13774 |
1.180351 |
1.180351 |
||
|
24 |
15.96471 |
0.940195 |
0.940195 |
||
|
25 |
16.65684 |
1.333805 |
1.333805 |
||
|
26 |
16.82987 |
1.026717 |
1.026717 |
||
|
27 |
15.70516 |
1.923167 |
1.923167 |
||
|
28 |
15.79168 |
1.828013 |
1.828013 |
||
|
29 |
15.87819 |
1.638919 |
1.638919 |
||
|
30 |
17.43548 |
0.052134 |
0.052134 |
||
|
31 |
17.34897 |
0.471978 |
0.471978 |
||
|
32 |
17.0029 |
0.911831 |
0.911831 |
||
|
33 |
16.82987 |
1.36638 |
1.36638 |
12- Construct multiple linear regression model with Period, AIP, DIFF, and ADV as independent variables. Formulate the equation, find MAD, and explain the output. Rank variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain.
|
SUMMARY OUTPUT |
||||||
|
Regression Statistics |
||||||
|
Multiple R |
0.957267 |
|||||
|
R Square |
0.91636 |
|||||
|
Adjusted R Square |
0.904412 |
|||||
|
Standard Error |
0.389463 |
|||||
|
Observations |
33 |
|||||
|
ANOVA |
||||||
|
|
df |
SS |
MS |
F |
Significance F |
|
|
Regression |
4 |
46.53136 |
11.63284 |
76.6925 |
1.13E-14 |
|
|
Residual |
28 |
4.247084 |
0.151682 |
|||
|
Total |
32 |
50.77844 |
|
|
|
|
|
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
|
|
Intercept |
18.14138 |
3.245942 |
5.588941 |
5.56E-06 |
11.49237 |
|
|
PERIOD |
0.107414 |
0.012455 |
8.624202 |
2.27E-09 |
0.081901 |
|
|
AIP |
-0.77289 |
0.501085 |
-1.54243 |
0.134197 |
-1.79931 |
|
|
DIFF |
0.803895 |
0.376051 |
2.137726 |
0.04141 |
0.033589 |
|
|
ADV |
0.089443 |
0.122669 |
0.729143 |
0.47197 |
-0.16183 |
|
|
Demand = 18.14 + .107(Period) – .772(AIP) + .803(DIFF) + .089(ADV) |
||||||
|
RESIDUAL OUTPUT |
MAD = |
0.270967 |
||||
|
Observation |
Predicted Des. Demand |
Residuals |
Abs. Res. |
|||
|
1 |
13.99892 |
0.804809 |
0.804809 |
|||
|
2 |
14.52359 |
0.324549 |
0.324549 |
|||
|
3 |
14.72522 |
0.267942 |
0.267942 |
|||
|
4 |
14.81851 |
0.033693 |
0.033693 |
|||
|
5 |
15.00202 |
-0.01411 |
0.014114 |
|||
|
6 |
15.04527 |
-0.15197 |
0.151969 |
|||
|
7 |
15.17349 |
-0.45184 |
0.451844 |
|||
|
8 |
14.7338 |
-0.50439 |
0.504388 |
|||
|
9 |
14.65713 |
-0.5045 |
0.504496 |
|||
|
10 |
14.90935 |
0.024412 |
0.024412 |
|||
|
11 |
15.35047 |
-0.31968 |
0.319676 |
|||
|
12 |
15.43243 |
0.01156 |
0.01156 |
|||
|
13 |
15.7708 |
-0.04184 |
0.04184 |
|||
|
14 |
15.83218 |
-0.01357 |
0.013571 |
|||
|
15 |
15.92755 |
-0.02571 |
0.025705 |
|||
|
16 |
15.99477 |
0.056679 |
0.056679 |
|||
|
17 |
16.2125 |
-0.19418 |
0.194183 |
|||
|
18 |
16.23368 |
-0.09127 |
0.091268 |
|||
|
19 |
16.3974 |
-0.41666 |
0.416659 |
|||
|
20 |
16.38674 |
-0.00755 |
0.007553 |
|||
|
21 |
16.63726 |
-0.46282 |
0.462816 |
|||
|
22 |
16.74599 |
0.669139 |
0.669139 |
|||
|
23 |
16.86557 |
0.452518 |
0.452518 |
|||
|
24 |
17.10371 |
-0.19881 |
0.198807 |
|||
|
25 |
17.23855 |
0.752095 |
0.752095 |
|||
|
26 |
17.32831 |
0.52828 |
0.52828 |
|||
|
27 |
17.4127 |
0.215627 |
0.215627 |
|||
|
28 |
17.52774 |
0.091948 |
0.091948 |
|||
|
29 |
17.52651 |
-0.0094 |
0.009396 |
|||
|
30 |
18.08405 |
-0.59643 |
0.596431 |
|||
|
31 |
18.17523 |
-0.35429 |
0.354288 |
|||
|
32 |
18.02669 |
-0.11196 |
0.111958 |
|||
|
33 |
17.95854 |
0.237712 |
0.237712 |
13- Perform multiple linear regression analysis with Period, DIFF, and ADV as independent variables. Formulate the equation and find MAD. Which variable is the most significant predictor of demand? Rank the independent variables based on their degree of contribution to the model. Observe significant F, R-squared, and p-values and explain.
|
SUMMARY OUTPUT |
||||||
|
Regression Statistics |
||||||
|
Multiple R |
0.953548 |
|||||
|
R Square |
0.909254 |
|||||
|
Adjusted R Square |
0.899866 |
|||||
|
Standard Error |
0.398616 |
|||||
|
Observations |
33 |
|||||
|
ANOVA |
||||||
|
|
df |
SS |
MS |
F |
Significance F |
|
|
Regression |
3 |
46.17049 |
15.39016 |
96.85759 |
3.26E-15 |
|
|
Residual |
29 |
4.607949 |
0.158895 |
|||
|
Total |
32 |
50.77844 |
|
|
|
|
|
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
|
|
Intercept |
13.25152 |
0.71337 |
18.57595 |
1.20E-17 |
11.79252 |
|
|
PERIOD |
0.104104 |
0.012557 |
8.290507 |
3.86E-09 |
0.078422 |
|
|
DIFF |
0.334995 |
0.226558 |
1.47863 |
0.15002 |
-0.12837 |
|
|
ADV |
0.146603 |
0.119685 |
1.224904 |
0.230468 |
-0.09818 |
|
|
Demand = 13.25 + .104(Period) + .335 (Diff) + .147 (ADV) |
||||||
|
RESIDUAL OUTPUT |
MAD = |
0.278648 |
||||
|
Observation |
Predicted Des. Demand |
Residuals |
Abs. Res. |
|||
|
1 |
14.03213 |
0.771603 |
0.771603 |
|||
|
2 |
14.53305 |
0.315088 |
0.315088 |
|||
|
3 |
14.82771 |
0.165459 |
0.165459 |
|||
|
4 |
14.73814 |
0.114056 |
0.114056 |
|||
|
5 |
14.91134 |
0.076566 |
0.076566 |
|||
|
6 |
14.89607 |
-0.00277 |
0.002768 |
|||
|
7 |
15.02007 |
-0.29842 |
0.298424 |
|||
|
8 |
14.94371 |
-0.71429 |
0.714289 |
|||
|
9 |
14.78751 |
-0.63487 |
0.634874 |
|||
|
10 |
15.03188 |
-0.09813 |
0.098125 |
|||
|
11 |
15.41659 |
-0.38579 |
0.385793 |
|||
|
12 |
15.45054 |
-0.00656 |
0.006558 |
|||
|
13 |
15.7651 |
-0.03614 |
0.036137 |
|||
|
14 |
15.87129 |
-0.05269 |
0.052686 |
|||
|
15 |
15.92724 |
-0.0254 |
0.025396 |
|||
|
16 |
16.01459 |
0.036853 |
0.036853 |
|||
|
17 |
16.22968 |
-0.21136 |
0.211355 |
|||
|
18 |
16.31912 |
-0.1767 |
0.176704 |
|||
|
19 |
16.3604 |
-0.37967 |
0.379665 |
|||
|
20 |
16.26978 |
0.109405 |
0.109405 |
|||
|
21 |
16.60845 |
-0.434 |
0.434004 |
|||
|
22 |
16.63716 |
0.777968 |
0.777968 |
|||
|
23 |
16.78312 |
0.534969 |
0.534969 |
|||
|
24 |
16.88305 |
0.021854 |
0.021854 |
|||
|
25 |
17.20911 |
0.781529 |
0.781529 |
|||
|
26 |
17.37604 |
0.480549 |
0.480549 |
|||
|
27 |
17.32104 |
0.307291 |
0.307291 |
|||
|
28 |
17.51519 |
0.104498 |
0.104498 |
|||
|
29 |
17.51876 |
-0.00165 |
0.001653 |
|||
|
30 |
17.983 |
-0.49539 |
0.495388 |
|||
|
31 |
18.17298 |
-0.35204 |
0.352037 |
|||
|
32 |
18.18077 |
-0.26603 |
0.266033 |
|||
|
33 |
18.22205 |
-0.0258 |
0.0258 |
14- Use the model obtained in parts 13 and make forecasts for the following months. Make sure to seasonalize final forecasts.
Demand = 13.25 + .104(Period) + .335 (Diff) + .147 (ADV)
Period Price AIP ADV Demand Seasonal Forecast Index Final Forecast
March 2018 $8.10 $8.50 $11.3 18.5811 .878546 16.3244
April 2018 $8.15 $8.60 $11.7 18.76065 .918114 17.2244
May 2018 $8.30 $8.90 $12.0 18.959 .958301 18.1684
15- Provide a case conclusion based on above analysis.
Grading Criteria
Completeness/Correctness 70%
Quality of Interpretations/Analysis 20%
General Quality of Report/graphs/writing 10%
Due Date: Sunday March 4, 2018
DIFF vs DEMAND
DEMAND
y = 2.6166x + 15.716 R² = 0.3459
-0.29999999999999982 0.25 0.59999999999999964 0 0.25 0.20000000000000018 0.15000000000000036 -0.45000000000000018 -0.75 -0.20000000000000018 0.19999999999999929 9.9999999999999645E-2 0.39999999999999947 0.45000000000000018 0.34999999999999964 0.29999999999999982 0.5 0.5 0.39999999999999947 -4.9999999999999822E-2 -4.9999999999999822E-2 -9.9999999999999645E-2 0.20000000000000018 0.10000000000000053 0.5 0.59999999999999964 -4.9999999999999822E-2 0 4.9999999999999822E-2 0.95000000000000018 0.89999999999999947 0.70000000000000018 0.59999999999999964 14.4 15.3 16.5 16.100000000000001 16 15.5 15.2 13.9 13.3 13.12 13.8 14.8 15.3 16.3 17.5 17.399999999999999 17.100000000000001 16.8 16.5 16 15.2 15.3 15.9 16.2 17.5 18.399999999999999 19.399999999999999 19.100000000000001 18.7 18.2 18.399999999999999 17.5 17.100000000000001
DIFF
Demand
ADV. vs. DEMAND
y = 1.181x + 7.6018 R² = 0.6132
5.3 6.75 7.25 7.3 7.2 6.5 6.75 6.89 5.8 5.5 6.5 6.25 7 6.9 6.8 6.8 7.1 7 6.8 6.5 8.1 7.7 7.3 7.5 8.1 8.3000000000000007 8.6999999999999993 9.1999999999999993 8.4 8.8000000000000007 9.5 9.3000000000000007 9.1 14.4 15.3 16.5 16.100000000000001 16 15.5 15.2 13.9 13.3 13.12 13.8 14.8 15.3 16.3 17.5 17.399999999999999 17.100000000000001 16.8 16.5 16 15.2 15.3 15.9 16.2 17.5 18.399999999999999 19.399999999999999 19.100000000000001 18.7 18.2 18.399999999999999 17.5 17.100000000000001
ADV
Demand
AIP vs. DEMAND
DEMAND
y = 2.0003x + 4.3142 R² = 0.0895
5.8 6 6.3 5.7 5.85 5.8 5.75 5.85 5.65 6 6.1 6 6.1 6.2 6.1 6.1 6.2 6.3 6.1 5.75 5.75 5.65 5.9 5.65 6.1 6.25 5.65 5.75 5.85 6.25 6.3 6.4 6.5 14.4 15.3 16.5 16.100000000000001 16 15.5 15.2 13.9 13.3 13.12 13.8 14.8 15.3 16.3 17.5 17.399999999999999 17.100000000000001 16.8 16.5 16 15.2 15.3 15.9 16.2 17.5 18.399999999999999 19.399999999999999 19.100000000000001 18.7 18.2 18.399999999999999 17.5 17.100000000000001
AIP
Demand
Price vs. DEMAND
DEMAND
y = -4.6991x + 43.4 R² = 0.4109
6.1 5.75 5.7 5.7 5.6 5.6 5.6 6.3 6.4 6.2 5.9 5.9 5.7 5.75 5.75 5.8 5.7 5.8 5.7 5.8 5.8 5.75 5.7 5.55 5.6 5.65 5.7 5.75 5.8 5.3 5.4 5.7 5.9 14.4 15.3 16.5 16.100000000000001 16 15.5 15.2 13.9 13.3 13.12 13.8 14.8 15.3 16.3 17.5 17.399999999999999 17.100000000000001 16.8 16.5 16 15.2 15.3 15.9 16.2 17.5 18.399999999999999 19.399999999999999 19.100000000000001 18.7 18.2 18.399999999999999 17.5 17.100000000000001
Price
Demand
Time Series Plot of DEMAND
DEMAND
y = 0.1173x + 14.3 R² = 0.4775
14.4 15.3 16.5 16.100000000000001 16 15.5 15.2 13.9 13.3 13.12 13.8 14.8 15.3 16.3 17.5 17.399999999999999 17.100000000000001 16.8 16.5 16 15.2 15.3 15.9 16.2 17.5 18.399999999999999 19.399999999999999 19.100000000000001 18.7 18.2 18.399999999999999 17.5 17.100000000000001
Time Period
Demand
Time Series Plot of ADVERTISING
ADV
y = 0.0916x + 5.8024 R² = 0.663
5.3 6.75 7.25 7.3 7.2 6.5 6.75 6.89 5.8 5.5 6.5 6.25 7 6.9 6.8 6.8 7.1 7 6.8 6.5 8.1 7.7 7.3 7.5 8.1 8.3000000000000007 8.6999999999999993 9.1999999999999993 8.4 8.8000000000000007 9.5 9.3000000000000007 9.1
Time Period
Advertising
Time Series Plot of Price Difference
DIFF
y = 0.0163x – 0.0552 R² = 0.1816
-0.29999999999999982 0.25 0.59999999999999964 0 0.25 0.20000000000000018 0.15000000000000036 -0.45000000000000018 -0.75 -0.20000000000000018 0.19999999999999929 9.9999999999999645E-2 0.39999999999999947 0.45000000000000018 0.34999999999999964 0.29999999999999982 0.5 0.5 0.39999999999999947 -4.9999999999999822E-2 -4.9999999999999822E-2 -9.9999999999999645E-2 0.20000000000000018 0.10000000000000053 0.5 0.59999999999999964 -4.9999999999999822E-2 0 4.9999999999999822E-2 0.95000000000000018 0.89999999999999947 0.70000000000000018 0.59999999999999964
Time Period
Difference
Time Series Plot of AIP
AIP
y = 0.0074x + 5.8641 R² = 0.0843
5.8 6 6.3 5.7 5.85 5.8 5.75 5.85 5.65 6 6.1 6 6.1 6.2 6.1 6.1 6.2 6.3 6.1 5.75 5.75 5.65 5.9 5.65 6.1 6.25 5.65 5.75 5.85 6.25 6.3 6.4 6.5
Time Period
AIP
Time Series Plot of PRICE
PRICE
y = -0.0089x + 5.9193 R² = 0.1474
6.1 5.75 5.7 5.7 5.6 5.6 5.6 6.3 6.4 6.2 5.9 5.9 5.7 5.75 5.75 5.8 5.7 5.8 5.7 5.8 5.8 5.75 5.7 5.55 5.6 5.65 5.7 5.75 5.8 5.3 5.4 5.7 5.9
Time Period
Price