om- sandeep raina b54-27-06_om-i_week1_s10-11_conceptual exercise.xlsx

7/25/2019 om- sandeep raina b54-27-06_OM-I_Week1_s10-11_Conceptual Exercise.xlsx

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om- sandeep raina b54-27-06_OM-I_Week1_s10-11_Conceptual !ercise

Question No 7 : C"ase#ood $partments%%%%

Semester

1 7&2 2'1

2 6&( 22)

( 6&7 252

4 7&0 265

5 6&' 270

6 6&4 240

7 7&1 2))

) 6&7 246

FORECASTING MODEL

*e!t semester enrolments e!pected 6&6

a& +,67&2()!-1'6&() 247&('0) 247

b&

2,0&'27 '2&7.

UniversitEnro!ments

"t#ous$n%s&

No' o( unitsLe$se%

/sin eression uation #e can nd t"enumber o3 apartments leased + 3or 6&6ke!pected enrolments

2 e!plains t"e ariation in + #&r&t& to ariation in + #&r&t& ariationin !& alue 0&058 "ence t"e model is accurate and not random

6&2 6&4 6&6 6&) 7&00

50

100

150

200

250

(00

(50

39!: , 67&2()421'554! - 1'6&()07)'022(; , 0&'270(50)04

*o& o3 units


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=ariation. in t"e Model 7&(0.

=ariation in *o o3 units 1)&0(1 )*+, units

$ctual units leased can ar+ 3rom 22-*2./ units

c&

>ence t"e 3orecastin model #ill predictupto '2&7. t"e output

?"e model is uite accurate #it" saure,0&'27 and alue0&05& >e is also no# a#are t"at "e "as su@cient (00 unitsAmore t"an ma! 265 reuired in t"e rane and t"ere #ill not bean+ s"ortae&>ence t"e model can be use3ul 3or t"e Manaer to estimate t"eno o3 units to be kept read+ 3or lease and associated #orksinoled&


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B/MM$

RegressioMultiple

Buare

$dDusted

Btandard

Obseratio

$*O=$

eression

esidual?otal

Intercept

/niersit+

sa+

7&2 7&4


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O/?/?

Statistics0&'62)27

0&'270(5

0&'14)74

6&57415)

)

d SS MS F gnicance F

1 (2'4&6)( (2'4&6)( 76&2(1( 0&000125

6 25'&(17( 4(&21'557 (554

oefcient andard Err t Stat P-value ower 95%!!er 95%ower 95"# !!er 95"#

-1'6&()0) 52&(2264 -(&75(266 0&00'47( -(24&40'7 -6)&(51' -(24&40'7 -6)&(51'

67&2()42 7&701066 )&7(1054 0&000125 4)&('45' )6&0)225 4)&('45' )6&0)225


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Q- $ !$r0e #e$!t# m$int or0$nis$tion "1MO&

$& Deve!o3 $ movin0 $ver$0e (ore$st (or t#e 3$st +5 mont#s

Mont# No o( L$9suits A62mont#s1 16

2 25

( 16

4 24

5 ()

6 46

7 54

) 52

' 51

10 5611 67

12 45

1( 5(

14 61

15 55 57&0

16 6' 5)&0

17 6( 62&0

1) 57 66&0

1' 4) 60&0

20 55 5(&021 61 52&0

22 51 5)&0

2( 56 56&0

24 5( 54&0

;#i# A6 resu!ts in t#e me$n $so!ute (ore$stin0 error

A62mont#s

Mont# No o( L$9suits 3orecast

15 55 57&0

16 6' 5)&0

17 6( 62&0

1) 57 66&0

1' 4) 60&0

20 55 52&5

21 61 51&5

22 51 5)&0


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2( 56 56&0

24 5( 5(&5

total absolute deiation

mean absolute deiation

$,6mont"s results in lo#est 3orecastin error as M$E is least co

>ence t"is $,2 is recommended as 3orecastin #ill be more accu

Usin0 our reommen%e% (ore$st'

FOC$B?I*G FO MO*?> 25 WI


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"Mont# +/*24& (or A62848. $n% ,

3orecasts

A64mont#s A6.mont#s A6,mont#s

57&0 56&0 55&0

54&0 57&0 55&0

60&0 5'&0 5)&0

62&0 5)&0 5'&0

61&0 60&0 5'&0

60&0 5'&0 57&056&0 5)&0 5)&0

56&0 5'&0 5'&0

54&0 56&0 5)&0

56&0 55&0 5)&0

A64mont#s A6.mont#s A6,mont#s

abs deiation 3orecast abs deiation 3orecast abs deiati 3orecast

2&0 56&5 1&5 55&5 0&5 54&'

11&0 5(&5 15&5 56&2 12&) 55&0

1&0 5'&5 (&5 5)&( 4&7 57&1

'&0 62&0 5&0 57&7 0&7 5)&6

12&0 61&0 1(&0 5'&7 11&7 5)&)

2&5 5'&( 4&( 5)&) (&) 56&4

'&5 55&) 5&( 57&) (&2 57&6

7&0 55&( 4&( 5)&) 7&) 5)&6

1 2 ( 4 5 6 7 )0

10

20

(0

40

50

60

70

)0

*o o3


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0&0 5(&) 2&( 55&) 0&2 57&4

0&5 55&) 2&) 54&7 1&7 57&5

/4'/ /7'< 47'5

/'/ /'7 4'7

pared to ot"er $ps& 92A4A):

ateA deiation #ill be least 3rom t"e actual outcomeA so t"at Cind+ is better prepare


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abs deiation

0&1

14&0

5&'

1&6

10&)

1&4

(&4

7&6

' 10 11 12 1( 14 15 16 17 1) 1' 20 21 22 2( 24

C"art ?itle

,2mont"s $,4mont"s $,6mont"s $,)mont"s


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1&4

4&5

/5'.

/'+


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Ques no +

om- sandeep raina b54-27-06_om-i_week1_s10-11_conceptual exercise.xlsx

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