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ASSIGNMENT : INSTRUCTIONSPl submit 1 solutioTo submit by 28th
The assignment coASSIGNMENT PART 1 1. ForecASSIGNMENT PART 2 2. A gue
Select any 1 situation from Guesstimation sheet, g
ASSIGNMENT PART 3 3. A On
ALLOCATION FOR FORECASTING1
A2A10B5
B16
FT153079FT152001FT154061FT153097
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set per group ec
sists of 3 partsast for the data set allotted to your group as specifisstimation exerciseuesstimate the number & explain the rationale behind arrivin
page note on your learning out of the Goal book
2 3 4 5 6 7A3 A4 A5 A6 A9 A7
A11 A12 A13 A15 A17 A18B7 B9 B1 B3 B4 B6
B13 B14 B10 B17 B11 B12
Section A
Souvik DeySourav DeyNISHIT MUKESH CHANDARANARAJENDRA SINGH NAYAL
DATASET ALLOTMENT TO GROUPS
Grp No 12
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d 10 MARKS5 MARKS
at the number
5 MARKS
8 9A1 A8
A16 A14B8 B2
B15 B18
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SUMMARY OUTPUT
Regression StatisticsMultiple R 0.940358R Square 0.884273Adjusted R Squar 0.88262
Standard Error 14.91703Observations 72
ANOVAdf SS MS F ignificance F
Regression 1 119018.9 119018.9 534.8733 1.67E-34Residual 70 15576.25 222.5178Total 71 134595.1
Coefficientsandard Err t Stat P-value Lower 95% Upper 95% ower 95.0 pper 95.0Intercept 252.0166 3.552925 70.93216 6.17E-67 244.9305 259.1027 244.9305 259.1027X Variable 1 -1.95633 0.084589 -23.1273 1.67E-34 -2.12504 -1.78762 -2.12504 -1.78762
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B3: The data below pertains to monthly sales of Black and white TV. Project t
month actual salesmonthlyaverage
quarter basisQuarters
Globalaverage sales
based ongiven sales
data
Average ofquarters
Seasonalfactors
1.00 229.00 220.33 Q1 182.90 168.56 0.922.00 203.00 284.67 Q2 204.33 1.123.00 229.00 241.67 Q3 193.83 1.064.00 259.00 209.00 Q4 173.33 0.955.00 293.00 197.33 Q16.00 302.00 257.67 Q27.00 264.00 245.33 Q38.00 234.00 222.33 Q49.00 227.00 187.33 Q1
10.00 235.00 214.00 Q2
11.00 188.00 210.33 Q312.00 204.00 184.67 Q413.00 201.00 161.00 Q114.00 180.00 181.67 Q215.00 211.00 183.00 Q316.00 237.00 163.67 Q417.00 272.00 133.00 Q118.00 264.00 153.67 Q219.00 243.00 148.00 Q320.00 229.00 134.00 Q421.00 235.00 112.33 Q122.00 236.00 134.33 Q223.00 196.00 134.67 Q324.00 196.00 126.33 Q425.00 194.0026.00 172.0027.00 191.0028.00 209.0029.00 242.0030.00 230.0031.00 218.00
32.00 183.0033.00 181.0034.00 193.0035.00 180.0036.00 171.0037.00 166.0038.00 146.0039.00 162.0040.00 184.00
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41.00 199.0042.00 199.0043.00 178.0044.00 172.0045.00 163.0046.00 178.00
47.00 150.0048.00 145.0049.00 140.0050.00 114.0051.00 133.0052.00 158.0053.00 170.0054.00 170.0055.00 149.0056.00 125.0057.00 135.00
58.00 141.0059.00 126.0060.00 115.0061.00 118.0062.00 104.0063.00 119.0064.00 136.0065.00 148.0066.00 148.0067.00 135.00
68.00 121.0069.00 129.0070.00 132.0071.00 118.0072.00 112.00
73.0074.0075.0076.0077.00
78.0079.0080.0081.0082.0083.0084.00
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229203
229259293302264234227235188204201
180211237272264243229235236196
196194172191209242230218183181193
180171166146162184199199178
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172163178150145140
114133158170170149125135141126115
118104119136148148135121129132
118112101
9997
115113111103101
99
878583
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he sales for the next year
Deseasonalisedsales
Forcastedseasonalised
sales
248.49220.28248.49231.84262.27270.33249.11220.80 Regression quation= y = -1.983x + 255.28214.20247.97
198.38215.26218.11195.32228.96212.14243.47236.31229.30216.09221.75249.03206.82206.82210.51186.64207.26187.08216.62205.88205.71
172.68170.79203.66189.94180.44180.13158.43175.79164.70
Note : Initial given data shows that seasonality & declining trfirst deseasonalised the given sales data, then calculate th
deasonalised sales data.Based on the equation, by changdeseasonalised sales forcast for next 1 year. then we convert
seasonal ones by multipling season
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
1 11 21 31 41 51 61 71
Sales graph from given data
sales
Line
0.00
50.00
100.00
150.00
Graph from forcasted salesdata
Series1
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178.13178.13167.96162.30153.81187.83
158.28153.01151.92123.70144.32141.43152.17152.17140.60117.95127.39148.78132.96121.35128.04112.85129.13121.74132.48132.48127.39
114.18121.73139.29124.51118.18
109.21 100.64107.25 98.84105.30 97.04103.34 115.45101.38 113.26
99.43 111.0897.47 103.3095.52 101.2293.56 99.1591.60 86.8189.65 84.9687.69 83.10
next 1 yrsalesforcast
73.00 76.00 79.00 82.00
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0
50
100
150
200
250
300
350
1 3 5 7 9 11131517192123252729313335373941434547495153555759616365676971737
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end prevalent in the data.So, weregression equation from the
ing the X variable, we get thethe deseasonalise sales forcast tol factor.
ar (sales)
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y = -1.9829x + 255.27R = 0.8289
577798183
Series1
Linear (Series1)