Report-3: Quantitative Analysis of Business and Economic Data to Inform Business Decisions

Name of the student:

Student ID number:

Name of the selected country: Switzerland

Name of the selected product: Apple

1. Executive summery

2. Introduction

3. Application of Analytical Techniques for Quantitative Analysis

(a) Application of basic statistical techniques

Table 1: GDP and sectoral contribution in (Switzerland) (in million dollars): 2000-2014

Year

GDP

HH Consumption

Export

Import

2000

271600

274821

264052

231024

2001

278629

280675

264012

233282

2002

301128

281606

258691

227865

2003

351983

282807

256174

228800

2004

393541

287938

280630

237795

2005

407536

292196

299010

261156

2006

429196

296524

317799

269448

2007

477408

303442

353915

285057

2008

551547

308032

367582

299052

2009

539528

311890

330912

287733

2010

581209

316992

428189

346155

2011

696279

319582

373421

311078

2012

665054

327903

391750

395932

2013

684535

335125

395932

330973

2014

702706

339200

456338

375510

Source: World Development Indicators 2016, the World Bank

(i) Descriptive statistics of data and interpretation

Formula Arithmetic Mean

 (Talukder,2017a).

Mean: the average number

Median: the middle number

The median of GDP is :477408

Nariance

 (Talukder,2017b).

Standard Deviation

 

(Talukder,2017c).

Table 2: Descriptive statistics of data from Table 1, 2000-2014

 

GDP

Household (HH) Consumption

Export (X)

Import (M)

Mean

488，791.9

303，915.5

335，893.8

288,057.3

Median

477，408

303，442

330，912

285,057

Variance

24132，338，818

433，484，305

4，278，386，061

3,010,423,206

Standard deviation

155，346

20，820

65,409

54,867

Source: Author's calculation based on table 1.

Interpretation of data to inform business decisions

(ii) Coefficient of variation (CV) and coefficient of correlation

Coefficient of variation (CV):

 

(Talukder,2017d).

Coefficient of correlation:

 

(Talukder,2017e).

Interpretation of data to inform business decisions

(b) Simple linear regression

Regression model: GDP as dependent variable and HH consumtiotion as independent variable

 

 (Talukder,2017e).

(ii) Write formula and calculate the following terms and interprete results

Formula

The following is taken from Dr. Tulakder,2017f

 

 

 

 

Table 3: Simple linear regression results

(dependent variable: GDP, independent variable: HH consumpotion)

(1)

y-intercept (constant)

-1729，794

(2)

Regression coefficient (slope of regression line)

7.3

(3)

Random variable (error term)

33381.42

(4)

R-square

0.6

Source: Author's calculation based on table 1.

Draw a regression line:

Regression line

 

Source: Author's calculation based on table 1.

Interpretation of data to inform business decisions

(c) Multiple linear regression model

(i) Regression model: HH consumption as dependent variable and exports and imports as independent variables

(ii) Write formula and calculate the following terms and interprete results

Formula

The following is taken from Dr.Talukder 2017g

 

 

 

 

 

 

 

 

 

 

Table 4: multiple linear regression results

(dependent variable: HH comsumption, independent variables: exports and imports)

(1)

y-intercept (constant)

Exports

Imports

(2)

Regression coefficient 2 (exports)

146007

146007

(3)

Random variable (error term)

0.23

0.28

(3)

R-square

 

Interpretation of data to inform business decisions

4. Conclusion

5. References

6. Appendix

Talukder,D. (2017) 8220 Economics and Quantitative Analysis (week13).

[power point sliders] ICL Business School Auckland.

Talukder,D. (2017) 8220 Economics and Quantitative Analysis (week14).

[power point sliders] ICL Business School Auckland.

Talukder,D. (2017) 8220 Economics and Quantitative Analysis (week15).

[power point sliders] ICL Business School Auckland.

Lecturer: Dr. Dayal Talukder Page 3 of 3