Wednesday, February 15, 2017

Decision Science - Assignment Home Work Help

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Mob:  +919741410271
Email: smu.assignment@gmail.com



Decision Science

 

1. Based on the following data develop the forecasting model (adopt Exponential Smoothing

Technique) by considering the following alpha levels. α = 0.2, 0.4, 0.6, 0.8.

Write your conclusion after calculations of Errors (MAD and MSE only).

Forecast FTAs (foreign tourist arrival) for the year 2018.

Data: The following data shows the Foreign Tourist Arrival in the India from 1991 to 2017

Year

FTAs in India (in Million)

1991

1.68

2001

2.54

2002

2.38

2003

2.73

2004

3.46

2005

3.92

2006

4.45

2007

5.08

2008

5.28

2009

5.17

2010

5.78

2011

6.31

2012

6.58

2013

6.97

2014

7.68

2015

8.03

2016

8.8

2017

10.04

 

Data Source: Data.gov.in

Note: You are advised to calculate manually, do not use any software.

Answer:

To apply exponential smoothing, we need to choose an alpha value. The alpha value determines the weight given to the most recent observation compared to the previous forecast. We will try different alpha values (0.2, 0.4, 0.6, and 0.8) to see which one gives the best results.

 

First, let's create a table to calculate the forecasts:

 

Year

FTAs (in Million)

α=0.2

α=0.4

α=0.6

α=0.8

1991

1.68

1.6800

1.6800

1.6800

1.6800

2001

2.54

1.8440

1.8776

1.9120

1.9440

2002

2.38

2.0384

1.9266

1.9312

1.9888

2003

2.73

2.1917

2.0296

1.9797

2.0778

2004

3.46

2.4254

2.2218

2.1379

2.4556

2005

3.92

2.8283

2.4611

2.3747

3.0911

2006

4.45

3.3627

2.7473

2.7779

4.2182

2007

5.08

3.9902

3.0872

3.3252

6.0436

2008

5.28

4.6042

3.4833

3.9441

8.0087

2009

5.17

4.8154

3.8650

4.5916

10.2017

2010

5.78

5.0123

4.2505

5.2426

12.6413

2011

6.31

5.3058

4.6343

5.8717

15.3173

2012

6.58

5.6166

5.0094

6.4687

18.2275

2013

6.97

5.9345

5.3717

7.0295

21.3685

2014

7.68

6.2776

5.7126

7.5502

24.7387

2015

8.03

6.6430

6.0298

8.0291

28.3357

2016

8.80

7.0223

6.3223

8.4643

32.1571

2017

10.04

7.4258

6.5889

8.8557

36.1994

 

To calculate the forecasts, we start with the

 

2. Calculate the Correlations for the following pairs of variables and write your Conclusion.

·       In Migration of Persons from other states (Census of India) & Total MSMEs

·       In Migration of Persons from other states (Census of India) & Active Companies

·       In Migration of Persons from other states (Census of India) & 2017-18 GSDP - CURRENT PRICES (` in Crore)

·       Total MSMEs & Active Companies

Note: Use MS EXCEL, SPSS, SAS etc. for the calculations.

State

Total MSMEs

Active Companies

In Migration of Persons from

other states (Census of India)

2017-18 GSDP - CURRENT

PRICES (` in Crore)

ANDAMAN &

NICOBAR ISLANDS

6,061

319

81,267

7,871

ANDHRA PRADESH

6,41,929

1,16,210

15,91,890

15,46,313

ARUNACHAL

PRADESH

60,932

237

1,36,010

22,432

ASSAM

20,189

6,346

4,95,699

2,88,691

BIHAR

9,02,520

20,867

11,11,954

4,84,740

CHANDIGARH

11,209

7,956

6,33,966

38,760

CHHATTISGARH

69,758

7,109

12,67,668

4,84,740

DELHI

1,63,821

2,16,531

63,30,065

6,86,824

GOA

8,620

4,125

2,69,689

70,494

GUJARAT

8,07,378

62,619

39,16,075

13,28,068

HARYANA

1,85,486

30,868

36,26,318

6,49,592

HIMACHAL

PRADESH

14,674

3,344

3,95,504

1,38,351

JAMMU & KASHMIR

9,399

2,745

1,55,187

1,37,427

JHARKHAND

1,57,813

9,554

21,95,521

2,76,243

KARNATAKA

3,32,872

68,333

32,47,660

13,57,579

KERALA

1,25,934

31,244

6,54,423

7,01,577

MADHYA PRADESH

9,34,293

22,315

27,44,332

7,24,729

MAHARASHTRA

16,92,859

2,31,912

90,87,380

24,11,600

MANIPUR

34,423

434

20,100

23,835

MEGHALAYA

2,208

560

1,07,915

30,790

MIZORAM

3,238

66

41,380

18,737

NAGALAND

1,543

235

1,08,020

24,492

ODISHA

1,19,291

15,204

8,55,096

4,34,769

PUDUCHERRY

10,539

1,282

3,39,967

32,962

PUNJAB

2,03,394

16,909

24,88,299

4,78,636

RAJASTHAN

5,72,546

37,022

26,04,298

8,35,170

SIKKIM

875

2

61,163

23,495

TAMIL NADU

10,32,490

76,675

16,50,771

14,61,841

TRIPURA

5,936

317

87,378

44,219

UTTAR PRADESH

8,87,413

70,863

40,61,933

14,60,443

UTTARAKHAND

40,443

4,792

12,50,575

2,22,836

WEST BENGAL

2,31,190

1,35,844

23,81,045

9,99,585

Data Source

https://data.gov.in/

https://data.gov.in/

https://censusindia.gov.in/

https://data.gov.in/

 

 

3. a. Summarized the following data by calculating the mean and standard deviation.

Note: You are advisable to use EXCEL. You are supposed to Copy the numbers (final calculations) from EXCEL and paste them into your word document.

State

District

Number Of Indigenous (Desi) Total Cattle

Total Buffalo

UTTARAKHAND

Uttarkashi

82991

25945

UTTARAKHAND

Chamoli

132165

37922

UTTARAKHAND

Rudraprayag

71277

31115

UTTARAKHAND

Tehri Garhwal

80833

79394

UTTARAKHAND

Dehradun

87753

52185

UTTARAKHAND

Garhwal

235412

30076

UTTARAKHAND

Pithoragarh

108417

37056

UTTARAKHAND

Bageshwar

70863

31432

UTTARAKHAND

Almora

132038

77444

UTTARAKHAND

Champawat

45506

18599

UTTARAKHAND

Nainital

101009

77759

UTTARAKHAND

Udham Singh Nagar

63311

152911

UTTARAKHAND

Hardwar

63728

214480

 

Data Source: Ministry of agriculture and farmers’ welfare.

 

3. b. Write about the general pattern (here, time series component) across the given years using an appropriate graph.

Note: You may use EXCEL, Tableau, Power BI Etc. for creating Graph

PLEASE CHECK THE TABLE BELOW

 

Year

Pro duc tivi ty Gro und nut (Kg

./he ctar

e)

Prod uctiv ity Rape seed & Must ard (Kg./ hecta

re)

Year

Prod uctiv ity Grou ndnu t (Kg./ hecta re)

Prod uctiv ity Rape seed & Must ard (Kg./ hecta

re)

Year

Prod uctiv ity Grou ndnu t (Kg./ hecta re)

Prod uctiv ity Rape seed & Must ard (Kg./ hecta

re)

Year

Produ ctivit y Grou ndnut (Kg./ hectar e)

Produ ctivit y Rapes eed & Musta rd (Kg./ hectar e)

1950-51

775

368

1968-69

653

469

1986-87

841

700

2003-04

1357

1159

1951-52

649

393

1969-70

720

493

1987-88

855

748

2004-05

1020

1038

1952-53

611

408

1970-71

834

594

1988-89

1132

906

2005-06

1187

1117

1953-54

811

389

1971-72

823

396

1989-90

930

831

2006-07

866

1095

1954-55

766

425

1972-73

585

545

1990-91

904

904

2007-08

1459

1001

1955-56

752

336

1973-74

845

493

1991-92

818

895

2008-09

1163

1143

1956-57

783

411

1974-75

724

612

1992-93

1049

776

2009-10

991

1183

1957-58

734

387

1975-76

935

580

1993-94

941

847

2010-11

1411

1185

1958-59

828

426

1976-77

747

496

1994-95

1027

958

2011-12

1323

1121

1959-60

708

365

1977-78

866

460

1995-96

1007

916

2012-13

996

1262

1960-61

745

467

1978-79

835

525

1996-97

1138

1017

2013-14

1750

1188

1961-62

725

425

1979-80

805

411

1997-98

1040

668

2014-15

1400

1089

1962-63

695

417

1980-81

736

560

1998-99

1214

869

2015-16

1465

1183

1963-64

769

300

1981-82

972

541

1999-00

766

960

2016-17

1398

1304

1964-65

814

507

1982-83

732

577

2000-01

977

935

2017-18

1893

1410

1965-66

554

446

1983-84

940

673

2001-02

1127

1002

2018-19

1422

1511

1966-67

604

408

1984-85

898

771

2002-03

694

854

2019-20

2063

1331

1967-68

759

483

1985-86

719

674

 

 

 

2020-21

1676

1511

 



Business Statistics
1. Suppose you are the Operations Manager of an e-commerce company. Explain with examples how you can use descriptive statistics to make better managerial decisions.
2. In the “Master Chef 2015” Competition 10 finalists were ranked by three judges in the following orders:
First Judge: 1, 6, 5, 10, 3, 2, 4, 9, 7, 8
Second Judge: 2, 5, 8, 4, 7, 10, 3, 1, 6, 9
Third Judge: 5, 4, 9, 8, 1, 2, 3, 10, 6, 7
Use the method of rank correlation to determine which pair of judges has the nearest approach in ranking the competitors.
3. A particular branch of a bank has 30 full-time operating staff that includes branch manager, assistant managers, loan officers, clerks and security personnel. Because of the demonetarization issue banks are running 7 days a week and the bank does not have full complement of staff to handle the increased customer crowd. In addition there are times when staff are absent as they are getting sick because of the increased stress levels. The branch manager conducted an audit to determine if there is any kind of strong relationship between the number of staff absent and the average time that the customer had to stand in the queues inside the bank. The following was his observation from the past 10 days information:
No. of staff absent: 1 3 8 0 4 2 3 5 9 7
Avg. waiting Time (in mins): 5 12 30 6 16 15 20 22 27 24
a) Develop the linear regression equation that best describes the relationship. What is the estimate of the time delay per employee absent?
b) When the bank has full complement of staff, what is the average waiting time in the queue as predicted by the linear regression? If there are six employees absent, estimate the average waiting time as predicted by the linear regression?

CONTACT: PRAKASH
Mob:  +919741410271
Email: smu.assignment@gmail.com



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