forecast assignment _grp 12_seca

8/10/2019 Forecast Assignment _Grp 12_secA

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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)

forecast assignment _grp 12_seca

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