test.docx
Chapter 5 : Chapter 5 - Exam 1
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YOU NEED TO HAVE MINITAB TO COMPLETE THIS ASSIGNMENT
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Eco 309 Exam 1 (Chapter 1 through 5)
You will have 2 and 1/2 hours to complete the 30 multiple choice questions. This exam must be completed. I suggest that you complete the exam within one session to prevent the loss of your answers. You must take this exam since there will be no make-up tests.
The excel data for this test may be downloaded from Doc Sharing under Exam 1 Data and can be copied and pasted directly into Minitab. I suggest that you download the data before you begin the exam.
Be sure to select the best answer for each question and do not leave any questions unanswered.
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1.
You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality.
Quarter Seasonal Index Seasonally Adjusted Data
Q1 .80 295
Q2 .85 299
Q3 1.15 270
Q4 --- 271
(Points : 3)
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325
225
252
271
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2. One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing? (Points : 3)
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Alpha and Delta
Delta and Gamma
Alpha and Gamma
Std Dev and Mean
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3. Why is the residual mean value important to a forecaster? (Points : 3)
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Large mean values indicate nonautoregressiveness.
Small mean values indicate the total amount of error is small.
Large absolute mean values indicate estimate bias.
Large mean values indicate the standard error of the model is small.
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4. When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient. (Points : 3)
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Ho: r = .05 p < .5
Ho: r = 1 p =.05
Ho: r ≠ 0 p≤.05
Ho: r = 0 p≤.05
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5. In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable? (Points : 3)
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The estimated value is 80% of the average monthly seasonal estimate.
The estimate is .80 of the forecasted Y trend value.
The estimated value is .80 of the historical average CMA values.
The estimated value has 20% more variation than the average historical Y data values.
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6. A Burger King franchise owner notes that the sales per store has fallen below the stated national Burger King outlet average of $1,258,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied? (Points : 3)
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H1: u ≥ $1.258,000 A one-tailed t-test to the left.
H1: u = $1.258,000 A two-tailed t-test.
H1: u < $1.258,000 A one-tailed t-test to the left.
H1: p < $1.258,000 A one-tailed test to the right.
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7.
The CEO of Home Depot wants to see if city size has any relationship to the current profit margins of the company stores. What data type will he likely use to determine this?
(Points : 3)
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Time series data of profits by store.
Recent 10 year sample of profits by stores.
Recent cross section of store profits by city.
Trend of a random sample of store profits over time.
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8. Sometimes forecasters get lazy or forgetful and do not check the significance of XY data correlations and use the X variable to forecast Y. What is the result of this? (Points : 3)
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Type 2 error
Autocorrelation error
Type 3 error
Type 1 error
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9. In exponential smoothing what is the weight of the alpha coefficient for a time series data observation from the 3rd previous period if the original alpha value is set at .3? (Points : 3)
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The weight cannot be calculated since the data observation is not given.
The weight is zero since the alpha value is set relatively high.
.125
.103
.084
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10. What is not a characteristic of a random data series? (Points : 3)
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Zero mean with an normal distribution.
ACF LBQ values less than .05.
Non autoregressive observations
Central tendency
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11. What is the major cause of non randomness (autoregressiveness) in business data? (Points : 3)
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Randomness only occurs for short time periods.
Random events such as storms or technologies offset over the long run.
Measurements naturally increase or decrease over time.
Business participant’s decisions and work.
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12. Which form of exponential smoothing can result in a naïve forecast? (Points : 3)
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Winters with a very low seasonal coefficient.
Simple with a very low trend coefficient.
Simple with a very high alpha value.
Double with a very low alpha value.
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13. What statistical characteristic enables forecasters to move from uncertainty to quantifiable low risk in the business forecasting process? (Points : 3)
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Large amounts of available business data naturally create statistical accuracy.
Although business data are not normally distributed the statistics from the data are normally distributed.
Statistical forecasting technology has improved the accuracy of models.
Statistical t and p-values always reflect the data population.
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14. What is used to determine the forecast model confidence level for Exponential Smoothing and Decomposition models? (Points : 3)
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The significance level of the smoothing constants
The error measures
The residual LBQ Chi-Square values
The mean of the residuals
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15. You are responsible for forecasting your company’s revenues for the next 24 months. You have three years of historical monthly data and previous forecasts that indicate that the company revenues with no obvious seasonality have grown significantly over that time. Which forecast method would you apply to the problem?(Points : 3)
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3 period moving average
12 period moving average
Simple exponential smoothing
Double exponential smoothing
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16. You obtained a correlation coefficient from two data series that indicates a p-value of .97. Can you be 95% confident that the correlation is significantly different from zero? (Points : 3)
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Yes, since the p value is above the confidence level.
Yes, since the p value is above 1 minus the confidence level.
No, since the p-value is above the 1 minus the confidence level.
No, since the data is not provided to determine true confidence.
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17. In decomposition the seasonal indices are the period relationships between what two data series? (Points : 3)
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Seasonal moving averages and the trend data series.
Smoothed data from centered moving averaging and the original data series.
Trend data and the cycle factors.
Trend data and the original data series.
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18. If sales growth and market penetration for a new product are expected to occur rapidly due to low product price and “need to have” technology which forecast model would you apply? (Points : 3)
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Logistics S-curve
Gompertz S-curve
3 period Moving Averages
Double Exponential Smoothing
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19. You have forecast the sales for your company for the last 12 months and the forecast residuals are shown below. Are these residuals to be considered random? (This data also appears in the Doc Sharing excel worksheet download for Exam 1 Data under the Problem 19 tab.)
Residuals
-24
-348
-892
-62
-378
-489
-342
34
490
23
578
198 (Points : 4)
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Yes, since the residuals randomly vary in magnitude.
Yes since the residuals are positive and negative and vary in magnitude.
No, since the residuals are stationary and vary in magnitude.
No, since the residuals indicate positive slope.
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20.
Given the data series below for variables Y (Monthly Inventory Balance) and X (Monthly Sales) are they significantly correlated at the 95% confidence level and how can you tell? (This data also appears in the docsharing download for Exam 1 Data excel worksheet under the Problem 20 tab.)
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Ending Inv. Bal. Y
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Monthly Sales X
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1544
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5053
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1913
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5052
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2028
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7507
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1178
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2887
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1554
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3880
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1910
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4454
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1208
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3855
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2467
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8824
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2101
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5716
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(Points : 4)
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Yes. The correlation coefficient is .873 that is greater than .05.
Yes. The correlation p-value is .002 which is less than .05.
No. The correlation coefficient is above the p-value.
No. The correlation p-value is greater than the 95% confidence level.
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21.
From the monthly sales data series below which exponential smoothing model would you apply? (This data also appears in the docsharing excel worksheet download for Exam 1 under the problem 21 through 30 tab.)
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Sales
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6028
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5927
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10515
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32276
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51920
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31294
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23573
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36465
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18959
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13918
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17987
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15294
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16850
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12753
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26901
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61494
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147862
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57990
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51318
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53599
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23038
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41396
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19330
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22707
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15395
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30826
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25589
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103184
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197608
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68600
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39909
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91368
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58781
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59679
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33443
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53719
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27773
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36653
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51157
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217509
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206229
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110081
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102893
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128857
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104776
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111036
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63701
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82657
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31416
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48341
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85651
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242673
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289554
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164373
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160608
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176096
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142363
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114907
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113552
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127042
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51604
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80366
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208938
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263830
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252216
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219566
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149082
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213888
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178947
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133650
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116946
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164154
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58843
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82386
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224803
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354301
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328263
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313647
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214561
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337192
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183482
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144618
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139750
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184546
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71043
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152930
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250559
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409567
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394747
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272874
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230303
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375402
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195409
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173518
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181702
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258713
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(Points : 4)
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Simple
Double
Winters
Moving Averages
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22. Run the data with the exponential smoothing model that applies and obtain the best model by adjusting each of the coefficients. (Make sure that you only use one decimal place for each coefficient – e.g. .1, or .2, or .3 …. to .9.) What coefficient value for Alpha will result in the best exponential smoothing result in the model selected? (Points : 4)
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.1
.5
.9
.2
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23. What is the RMSE for the Fit period for the best exponential smoothing model? (Points : 4)
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22634
38693
12971
20
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24. Use the best exponential smoothing model to generate a forecast for 12 months. What is the forecast value for the 12th month? (Points : 4)
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280762
85095
250981
46840
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25. Are the residuals from the best exponential smoothing model random and how can you tell? (Points : 4)
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No, since they still have significant seasonality.
No, since they still have significant trend.
Yes, since they are normally distributed with a near zero mean.
Yes, since none of the residuals is significantly autoregressive.
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26. Use the same monthly sales data series and run a decomposition model and estimate 12 forecast periods. Which month has the greatest seasonal sales? (Points : 4)
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Month 1
Month 12
Month 4
Month 5
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27. What is the MAPE for the decomposition model? (Points : 3)
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24%
22%
29%
24341
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28. What is the forecast value for the 12th period (last forecast month). Do not adjust if for cycle factors. (Points : 4)
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87838
221239
353622
181473
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29.
Are the decomposition residuals random? Why or why not?
(Points : 4)
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No. They still have seasonality.
No. They still have significant trend.
Yes. They are normally distributed with a near zero mean.
Yes. None of the residuals are significantly autoregressive.
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30. What is the forecast value for the 12th period (last forecast month) adjusted for cycle? (Points : 3)
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223746
353622
665423
282712
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