2Uthe following cash flow data to estirmate cash forecast using various techniqu
ID: 3053115 • Letter: 2
Question
2Uthe following cash flow data to estirmate cash forecast using various techniques Period INet Cash Flow Period iNet CashFlow 4821 4962 9889 8128 3018 6570 4787 6142 16 17 18 19 20 21 349 8485 4033 4848122472 6752 3494 5766 6762 8277 5301 6160 6866 23 24 25 26 27 28 29 30 4474 8295 3373 3509 8654 5404 2332 1922 10 12 13 14 15 a Compare a 3-day moving average forecast to a S-day moving average forecast. Which is better? (Hint Period 4 will be the first possible forecast for the 3-day and period 6 wil be the first possible for the s- day) b. Graph a scatter plot of the data. Does it appear to have a linear relation? c. Use regression analysis to develop the linear trend equation. Statistically speaking, would this be a good forecast equation? Why or why file) ?. "Back" Test the equation from part c. by using to estimating forecast for periods 1-30. than either of the methods in part a c "Backe Test the equation from part c by using to estimatins torecasts for periods 1-30 it better Is it better e. Produce the Descriptive Statistics for Net Cash FlowExplanation / Answer
Answer:
(a) 3 day Moving Average Table:
Period Net Cash Flow 3 Days Moving Average Forecast value 5 Days Moving Average forecast value
1 4787 #N/A #N/A #N/A #N/A
2 6142 #N/A #N/A #N/A #N/A
3 7571 #N/A #N/A #N/A #N/A
4 7349 6462.25 #N/A #N/A #N/A
5 8485 7386.75 #N/A #N/A #N/A
6 4033 6859.5 #N/A 6394.5 #N/A
7 4848 6178.75 1714.094253 6404.667 #N/A
8 6752 6029.5 1694.709203 6506.333 #N/A
9 3494 4781.75 1727.737258 5826.833 #N/A
10 5766 5215 1031.358676 5563 #N/A
11 6762 5693.5 952.0503614 5275.833 1620.301
12 8277 6074.75 1410.09737 5983.167 1604.018
13 5301 6526.5 1396.159783 6058.667 1504.886
14 6160 6625 1388.314856 5960 1503.759
15 6866 6651 1285.904867 6522 1172.175
16 4821 5787 821.1967258 6364.5 1328.229
17 4962 5702.25 660.2227015 6064.5 1264.382
18 9889 6634.5 1740.626418 6333.167 1681.975
19 8128 6950 1834.433176 6804.333 1739.362
20 3018 6499.25 2482.263376 6280.667 2189.264
21 6570 6901.25 2460.095647 6231.333 2189.126
22 2472 5047 2249.840946 5839.833 2507.108
23 4474 4133.5 2178.037728 5758.5 2521.507
24 8295 5452.75 1932.270483 5492.833 2357.83
25 3373 4653.5 2028.831595 4700.333 2358.173
26 3509 4912.75 1717.885301 4782.167 2014.193
27 8654 5957.75 2177.058993 5129.5 2471.479
28 5404 5235 1651.402387 5618.167 2055.595
29 2332 4974.75 2015.748415 5261.167 2319.587
30 1922 4578 2309.588092 4199 2221.692
(b) Graph the Scatter Plot of the data:
For seeing the plot we can conclude that there is there is no linear relation between the given data.
(c) If we use Linear Regression Analysis of the data then we get following results:
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.300275142
R Square 0.090165161
Adjusted R Square 0.05767106
Standard Error 8.54578802
Observations 30
ANOVA
df SS MS F Significance F
Regression 1 202.6461995 202.6461995 2.774816265 0.106908486
Residual 28 2044.853801 73.03049288
Total 29 2247.5
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 22.5926149 4.534704523 4.982158106 2.912E-05 13.30369389 31.88153591 13.30369389 31.88153591
Net Cash Flow -0.001248583 0.000749549 -1.665777976 0.106908486 -0.002783965 0.000286799 -0.002783965 0.000286799
Using the above able we get Linear trend equation as:
Y= 22.5926+0.0011, where Y is the Period and X is the Net Cash Flow.
Statistically the above equation is not a forecast model, because if we compared it by Moving average Model then its provided good forecate value as compared to Linear Trend.
(d) If we compared Moving Average Method to Regression then always Moving Average method gave good forecast value. In this example also the same obtained.
(e) Descriptive Statistics for Net Flow data:
Net Cash Flow
Mean 5680.533333
Standard Error 386.5381269
Median 5585
Standard Deviation 2117.156514
Sample Variance 4482351.706
Kurtosis -0.858441573
Skewness 0.02050375
Range 7967
Minimum 1922
Maximum 9889
Sum 170416
Count 30
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