Month Demand Forecast Forecast Absolute Absolute % MFE = -5
Error Deviation Error MAD = 44
January 1040 1055 -15 15 0.01 MAPE = 0.04
February 990 1052 -62 62 0.06
Month Sales (Y) Temp (X) X^2 X*Y b.
a = -767.7
Forecast
b = 98.5
Apr-14 5310 58 3364 307980
Jun-14 7080 75 5625 531000
Aug-14 7040 82 6724 577280
ANSWER:
a.
Using the formula 9-8:
Using the formula 9-9:
Month Interest Number of X^2 X*Y
Rate (X) Loans (Y)
17% 20 0.0049 1.4
25% 30 0.0025 1.5
34% 35 0.0016 1.4
48% 18 0.0064 1.44
510% 15 0.01 1.5
66% 22 0.0036 1.32
711% 15 0.0121 1.65
89% 20 0.0081 1.8
95% 27 0.0025 1.35
10 12% 10 0.0144 1.2
Total: 77% 212 0.0661 14.56
Average: 8% 21.2
ANSWER:
a.
b.
The number of loans the bank should expect to make if the interest rate is 10%:
Demand = (35,000 + 4.8 * period) seasonal index
Seasonal Indices
Summer 1.25
Fall 0.90
Winter 0.75
Spring 0.90
ANSWER:
a.
b.
c.
Quarter Demand Forecast Seasonal
Index
Winter 285 250 1.14
Spring 315 300 1.05
Summer 300 350 0.86
Fall 400 400 1.00
ANSWER:
The fall quarter season index is 1.00 so the unadjusted forecast will not be changed when
Month Sales ($) Unadjusted Trend Adjusted Table 9.12
a = 0.3
Forecast Factor Forecast values
b = 0.6
Feb-15 $230,000 $223,000 $5,800 $228,800 $233,287
Apr-15 $250,000 $229,570 $4,114 $233,684 $242,937
Jun-15 $250,000 $236,989 $2,903 $239,893 $252,587
Aug-15 $260,000 $249,625 $6,641 $256,265 $262,238
Oct-15 $260,000 $254,916 $3,117 $258,033 $271,888
Month Period Demand Unadjusted Trend Adjusted
a = 0.25
Forecast Factor Forecast
b = 0.4
Jan-15 1 1200 1100 60
Mar-15 3 1450 1194 55 1249
May-15 5 1796 1338 67 1406
Jul-15 7 2152 1615 117 1732
Sep-15 9 1888 1817 101 1919
Nov-15 11 1988 1861 51 1912
Jan-16 13 1684 1879 21 1900
Mar-16 15 1994 1859 71866
May-16 17 2430 1958 37 1995
Jul-16 19 2877 2264 117 2380
Sep-16 21 2492 2485 106 2590
Nov-16 23 2592 2500 44 2544
Unadjusted Demand / Seasonal Adjusted
Month Period Demand Forecast Forecast Index Forecast
Jan-15 1 1200 1457 82.3% 81.1% 1182
Mar-15 3 1450 1566 92.6% 91.3% 1429
May-15 5 1796 1675 107.3% 105.9% 1773
Jul-15 7 2152 1783 120.7% 119.4% 2130
Sep-15 9 1888 1892 99.8% 98.9% 1871
Nov-15 11 1988 2000 99.4% 98.6% 1972
Jan-16 13 1684 2109 79.9% 81.1% 1710
Mar-16 15 1994 2217 89.9% 91.3% 2024
May-16 17 2430 2326 104.5% 105.9% 2462
Jul-16 19 2877 2435 118.2% 119.4% 2908
Sep-16 21 2492 2543 98.0% 98.9% 2515
Nov-16 23 2592 2652 97.7% 98.6% 2614
Jan-17 25 2760 81.1% 2239
Observations
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.85
ANOVA
df SS MS F
Significance F
Regression
1.00 3389243.27 3389243.27 56.64 0.00
Coefficients
Standard Error
t Stat P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Inches of Average Peak Acre-Feet of
Rainfall Daily Temp, Water Used, Forecast Absolute Absolute %
Year March-June July August Forecast Error Deviation Error MFE =
0
2003 11.2 74.9 43700 33904 9796 9796 0.22 MAD =
6295
Observations
2005 10.6 85.1 54500 43623 10877 10877 0.20
2007 11.7 71.0 31500 29714 1786 1786 0.06
2009 13.3 91.4 35800 44241 -8441 8441 0.24
2011 14.9 99.6 48100 48403 -303 303 0.01
2013 12.6 91.6 40300 45639 -5339 5339 0.13
2015 7.1 77.0 34100 42879 -8779 8779 0.26
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.7048242
ANOVA
df SS MS F
Significance F
Coefficients
Standard Error
t Stat P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
-10095.398 21343.9989 -0.4729853 0.644711 -56600 36409.18 -56600 36409.18
Temperatur
0
Comparing a Two-Period Moving Average and an Exponential Smoothing Model
Two-period moving average model Exponential Smoothing Model
Weight on Period t-2: 0.35 Initial Forecast 65
Weight on Period t-1: 0.65 Alpha (a): 0.3
Period Demand Forecast
Forecast
Error
Absolute
Deviation
Forecast
Forecast
Error
Absolute
Deviation
253 63.50 -10.50 10.50
472 60.80 11.20 11.20 61.75 10.26 10.26
674 72.00 2.00 2.00 66.98 7.02 7.02
860 58.40 1.60 1.60 63.36 -3.36 3.36
972 56.50 15.50 15.50 62.35 9.65 9.65
10 53 67.80 -14.80 14.80 65.25 -12.25 12.25
Month Period Bomber Hook King Sir Slice-A-Lot Total demand
Forecast,
total demand
Apr-14 1 1410 377 343 2130 2126
Aug-14 5 1466 396 350 2212 2208
Sep-14 6 1483 400 352 2235 2228
Oct-14 7 1490 403 354 2247 2249
Feb-15 11 1547 423 365 2335 2331
Mar-15 12 1554 426 367 2347 2351
Apr-15 13 1562 431 369 2362 2372
Jul-15 16 1595 445 377 2417 2433
Aug-15 17 1613 454 381 2448 2454
Sep-15 18 1631 461 384 2476 2474
Nov-15 20 1656 471 389 2516 2515
Feb-16 23 1703 485 396 2584 2577
May-16 26 2638
Aug-16 29 2700
Sep-16 30 2720
Dec-16 33 2782
SUMMARY OUTPUT
Regression Statistics
Multiple R 1.00
R Square 1.00
Adjusted R Square 1.00
Standard Error 7.03
ANOVA
df SS MS F Significance F
Regression 1.00 483513.03 483513.03 9789.53 0.00
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
ANSWER:
3.
forecasting under these circumstances makes sense only until reliable information indicates the environment is changing.
1700
2300
2900