ll semesters using the linear trend equation wil be opened in 2 Month New Checki
ID: 3055975 • Letter: L
Question
ll semesters using the linear trend equation wil be opened in 2 Month New Checking Accounts20 144 Martin Guenther, the manager of loy's Savings Bank (fictitious r the next 2 months. He has data tor the past 12 months on new checkneats in Hamburg, Germany, wents to forecast the number of new checkin months on new checking accounts opened which is shown in the tolowing t number of new checking accounts that will be opened in morth 13 uning trend-adjusted eponental snoding with 277 Forecasi us ume that the forecast for month 2 (F) is the naive torecast ano the treno tactor or morth 2is T,- the number of new checking accounts that will be opened in month 13 using a lnertrend equto. e two forecasting methods is more accurate? Use the MAPE to answer the question c. Which of the f Chonnai Inddia the niumhor of enntninnmExplanation / Answer
a)
Actual Values
Forecast
Trend
Adjusted Forecast
% error
120
120
144
120
0
120
16.6666667
178
129.6
4.8
134.4
24.494382
228
148.96
12.08
161.04
29.3684211
245
180.576
21.848
202.424
17.3779592
252
206.3456
23.8088
230.1544
8.66888889
255
224.60736
21.03528
245.64264
3.66955294
262
236.764416
16.596168
253.360584
3.29748702
277
246.85865
13.3452008
260.2038504
6.06359191
282
258.91519
12.70087048
271.6160602
3.68224814
290
268.149114
10.96739729
279.1165111
3.75292719
295
276.889468
9.853875873
286.7433442
2.79886638
MAPE
10.8946356
b)
x
y
Forecast
xy
x^2
y^2
abs error
% error
1
120
153.626
120
1
14400
33.6261
28.0217
2
144
168.542
288
4
20736
24.5422
17.0432
3
178
183.458
534
9
31684
5.45825
3.06643
4
228
198.374
912
16
51984
29.6257
12.9937
5
245
213.29
1225
25
60025
31.7096
12.9427
6
252
228.207
1512
36
63504
23.7935
9.44186
7
255
243.123
1785
49
65025
11.8774
4.65781
8
262
258.039
2096
64
68644
3.96133
1.51196
9
277
272.955
2493
81
76729
4.04524
1.46038
10
282
287.871
2820
100
79524
5.87084
2.08186
11
290
302.787
3190
121
84100
12.7869
4.40928
12
295
317.703
3540
144
87025
22.703
7.69593
MAPE
8.77724
b1= nE(xy)-ExEy/nE(x2)-(Ex2)
= 14.92
b0= Ey-b1Ex/n
= 138.71
y= 138.71+14.92*x
y=138.71+14.92*13
y=138.71+193.96
y=332.67
Excel: -
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.93
R Square
0.86
Adjusted R Square
0.85
Standard Error
22.58
Observations
12
ANOVA
df
SS
MS
F
Significance F
Regression
1
31816.01
31816.01
62.40
0.00
Residual
10
5098.66
509.87
Total
11
36914.67
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
138.71
13.90
9.98
0.00
107.75
169.68
x
14.92
1.89
7.90
0.00
10.71
19.12
c)
Using MAPE, linear trend is a better method
Actual Values
Forecast
Trend
Adjusted Forecast
% error
120
120
144
120
0
120
16.6666667
178
129.6
4.8
134.4
24.494382
228
148.96
12.08
161.04
29.3684211
245
180.576
21.848
202.424
17.3779592
252
206.3456
23.8088
230.1544
8.66888889
255
224.60736
21.03528
245.64264
3.66955294
262
236.764416
16.596168
253.360584
3.29748702
277
246.85865
13.3452008
260.2038504
6.06359191
282
258.91519
12.70087048
271.6160602
3.68224814
290
268.149114
10.96739729
279.1165111
3.75292719
295
276.889468
9.853875873
286.7433442
2.79886638
MAPE
10.8946356
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