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Questions:
  1. Analyse and present data graphically usingspreadsheet software (Excel).
  2. Critically evaluate summary statistics against suitable benchmarks.
  3. Apply judgement to select appropriate methods of data analysis drawing on knowledge of regression analysis, probability, probability distributions and sampling distributions.
  4. Select and apply a range of data analysis tools to inform problem solving and decision making.
  5. Conduct quantitative research both individually and as part of a team and articulate and present findings to a wide range of stakeholders, from accounting and nonaccounting backgrounds
1.Analysis of sales and country data, forecasting of sales figures until 2020
  • Provide a statistical overview of the data provided in the excel sheets for each country (you can also do a comparison analysis)
  • Conduct a correlation analysis between sales development and:

(i) advertisements,

(ii) number of stores and

(iii) the survey results

  • Conduct a multi regression time series analysis and use the results to forecast the sales until 2020.

You should use natural logs (ln) for your analysis as discussed in your assignment of Module 5.

For your reasonable assumptions which are required for the forecasting, you should apply hypothesis testing techniques for the GDP, Prices and Population developments.

If you forecast a GDP growth of 3.5%, knowing the past GDP growth figures, is the forecast of 3.5% an acceptable growth figure for the forecast or should that Null hypothesis be rejected?

2.Market place Sweden – Estimate of potential sales figures in 2016 and forecast to 2020
  • Based on your analysis in part 1, which country (Industria, Federated Islands, Nokaragua) is closest related to Sweden? Justify your selection.

Look at average GDP per capita statistics (GDP divided by Population) and Price Index.

  • Once you have identified the country that is closet related to Sweden, use that country’s characteristics of the analysis undertaken in the first part to estimate/forecast the potential sales for the Swedish market.
  1. Other suggestions

Based on your analysis in parts 1 and 2, are there any other suggestions you have for the Board on how Schmeckt Gut can boost sales in the countries?

Answers:
Introduction

The research analysis aims to predict and forecast sales from 2016 to 2020 for the chosen four countries that are 1) Industria, 2) Nokaragua, 3) Federal Island and 4) Sweden. There are seven variables present in the dataset. These are sales figures in US$, GDP data in US$, Average Price Index in percentage, Population in the age-interval 15 to 65 years, Survey score, Number of average advertisements and number of stores. Sales data indicators are presented by Survey score, Advertisement and number of stores. All the data are present for Industria, Nokaragua and Federal Island. The report objective is to determine that which country has highest sales return. In addition, the correlation between prices and sales figures were incorporated. These are absent for Sweden. However, data regarding population, price index, GDP or amount of sales are present for all the four countries. The sales figure of a country whose GDP and price index are close or near to Sweden is used to predict sales of Sweden. Proper forecasting and prediction methodologies were executed for the research analysis. Necessary plots and tables are provided for the development of the research.

Data Analysis and Calculation:-
Industria

Sales US$

 

GDP US$

 

Price index

 

Population 15-65

 

Survey score

 

Advertisement

 

Stores

 

Mean

17372234

Mean

1.3403E+11

Mean

2.4576

Mean

2666183.687

Mean

8.276

Mean

42

Mean

35.04

Standard Error

666600.2

Standard Error

12582412928

Standard Error

0.144786371

Standard Error

47351.61056

Standard Error

0.155057

Standard Error

1.290994

Standard Error

0.80928

Median

17017460

Median

1.35419E+11

Median

2.58

Median

2612371.573

Median

8.5

Median

40

Median

35

Mode

#N/A

Mode

#N/A

Mode

1.88

Mode

#N/A

Mode

8.7

Mode

50

Mode

30

Standard Deviation

3333001

Standard Deviation

62912064642

Standard Deviation

0.723931857

Standard Deviation

236758.0528

Standard Deviation

0.775285

Standard Deviation

6.454972

Standard Deviation

4.046398

Sample Variance

1.11E+13

Sample Variance

3.95793E+21

Sample Variance

0.524077333

Sample Variance

56054375562

Sample Variance

0.601067

Sample Variance

41.66667

Sample Variance

16.37333

Kurtosis

-1.13134

Kurtosis

-1.621997791

Kurtosis

-0.888202357

Kurtosis

-1.661805275

Kurtosis

-0.77112

Kurtosis

-1.68237

Kurtosis

-1.5079

Skewness

-0.05202

Skewness

-0.021350914

Skewness

-0.253889862

Skewness

0.249932565

Skewness

-0.51656

Skewness

0.303103

Skewness

-0.08077

Range

11184328

Range

1.77806E+11

Range

2.43

Range

615466.3689

Range

2.5

Range

15

Range

10

Minimum

11919253

Minimum

47087316500

Minimum

1.22

Minimum

2366696.625

Minimum

6.8

Minimum

35

Minimum

30

Maximum

23103581

Maximum

2.24893E+11

Maximum

3.65

Maximum

2982162.994

Maximum

9.3

Maximum

50

Maximum

40

Sum

4.34E+08

Sum

3.35075E+12

Sum

61.44

Sum

66654592.16

Sum

206.9

Sum

1050

Sum

876

Count

25

Count

25

Count

25

Count

25

Count

25

Count

25

Count

25

Largest(1)

23103581

Largest(1)

2.24893E+11

Largest(1)

3.65

Largest(1)

2982162.994

Largest(1)

9.3

Largest(1)

50

Largest(1)

40

Smallest(1)

11919253

Smallest(1)

47087316500

Smallest(1)

1.22

Smallest(1)

2366696.625

Smallest(1)

6.8

Smallest(1)

35

Smallest(1)

30

Confidence Level(95.0%)

1375795

Confidence Level(95.0%)

25968823765

Confidence Level(95.0%)

0.298824382

Confidence Level(95.0%)

97728.92024

Confidence Level(95.0%)

0.320022

Confidence Level(95.0%)

2.664482

Confidence Level(95.0%)

1.670271

Descriptive statistics of Industria indicates that it has average sales $17372234 with standard deviation $3333001. The standard deviation is significantly large. It indicates a major variation of the observations from sample mean. The highest sales is $23103581 with a total amount of sale $4.34E+08. The distribution of Sales of Industria is slightly negatively skewed and almost close to normal as the skewness is (-0.05202).

Nokaragua

Sales US$

 

GDP US$

 

Price index

 

Population 15-65

 

Survey score

 

Advertisement

 

Stores

 

Mean

7859678.624

Mean

1.7188E+11

Mean

2.040354523

Mean

3565573

Mean

8.08

Mean

20.2

Mean

33.8

Standard Error

372279.5945

Standard Error

10251840162

Standard Error

0.130326805

Standard Error

10426.74

Standard Error

0.151438

Standard Error

0.757188

Standard Error

1.899123

Median

7949044.454

Median

1.68541E+11

Median

2.101981293

Median

3571749

Median

8

Median

20

Median

35

Mode

#N/A

Mode

#N/A

Mode

#N/A

Mode

#N/A

Mode

8.7

Mode

20

Mode

35

Standard Deviation

1861397.972

Standard Deviation

51259200812

Standard Deviation

0.651634025

Standard Deviation

52133.69

Standard Deviation

0.757188

Standard Deviation

3.785939

Standard Deviation

9.495613

Sample Variance

3.4648E+12

Sample Variance

2.62751E+21

Sample Variance

0.424626902

Sample Variance

2.72E+09

Sample Variance

0.573333

Sample Variance

14.33333

Sample Variance

90.16667

Kurtosis

-1.37078818

Kurtosis

-1.340749889

Kurtosis

0.455705039

Kurtosis

-0.71196

Kurtosis

-0.90156

Kurtosis

-1.41007

Kurtosis

-1.43369

Skewness

0.019442565

Skewness

0.134381337

Skewness

-0.445018304

Skewness

-0.42759

Skewness

0.073737

Skewness

0.132201

Skewness

-0.14104

Range

5391289.833

Range

1.56684E+11

Range

2.834720556

Range

171277.6

Range

2.6

Range

10

Range

25

Minimum

5162753.748

Minimum

96625482040

Minimum

0.564754204

Minimum

3462474

Minimum

6.8

Minimum

15

Minimum

20

Maximum

10554043.58

Maximum

2.53309E+11

Maximum

3.399474759

Maximum

3633751

Maximum

9.4

Maximum

25

Maximum

45

Sum

196491965.6

Sum

4.297E+12

Sum

51.00886307

Sum

89139334

Sum

202

Sum

505

Sum

845

Count

25

Count

25

Count

25

Count

25

Count

25

Count

25

Count

25

Largest(1)

10554043.58

Largest(1)

2.53309E+11

Largest(1)

3.399474759

Largest(1)

3633751

Largest(1)

9.4

Largest(1)

25

Largest(1)

45

Smallest(1)

5162753.748

Smallest(1)

96625482040

Smallest(1)

0.564754204

Smallest(1)

3462474

Smallest(1)

6.8

Smallest(1)

15

Smallest(1)

20

Confidence Level(95.0%)

768347.3142

Confidence Level(95.0%)

21158758019

Confidence Level(95.0%)

0.268981303

Confidence Level(95.0%)

21519.73

Confidence Level(95.0%)

0.312552

Confidence Level(95.0%)

1.562759

Confidence Level(95.0%)

3.919596

Initial descriptive statistics indicates that Nokaragua has average sales $7859678.624 with standard deviation $97.972. It indicates a minor variation of the observations from sample mean. The highest sales is $85162753.74 with a total amount of sale $196491965.6. The distribution of Sales of Nokaragua is slightly positively skewed and almost close to normal as the skewness is (0.01944256).

Sweden:

GDP US$

 

Price index

 

Population 15-65

 

Mean

2.89924E+11

Mean

2.144886317

Mean

5983560.03

Standard Error

18178738939

Standard Error

0.136793592

Standard Error

24982.68649

Median

2.74804E+11

Median

2.270139797

Median

5942169.676

Mode

#N/A

Mode

#N/A

Mode

#N/A

Standard Deviation

90893694696

Standard Deviation

0.683967958

Standard Deviation

124913.4324

Sample Variance

8.26166E+21

Sample Variance

0.467812167

Sample Variance

15603365603

Kurtosis

-1.410063247

Kurtosis

0.20907679

Kurtosis

-0.246940649

Skewness

0.236536248

Skewness

-0.559509217

Skewness

0.59020418

Range

2.65776E+11

Range

2.863067761

Range

478828.1537

Minimum

1.72028E+11

Minimum

0.570401746

Minimum

5764460.952

Maximum

4.37803E+11

Maximum

3.433469507

Maximum

6243289.106

Sum

7.24811E+12

Sum

53.62215793

Sum

149589000.7

Count

25

Count

25

Count

25

Largest(1)

4.37803E+11

Largest(1)

3.433469507

Largest(1)

6243289.106

Smallest(1)

1.72028E+11

Smallest(1)

0.570401746

Smallest(1)

5764460.952

Confidence Level(95.0%)

37519072889

Confidence Level(95.0%)

0.282328095

Confidence Level(95.0%)

51561.73035

Sales data is not present in case of Sweden. However, GDP and Price Index provides a clear approach.

Federal Islands:

Sales US$

 

GDP US$

 

Price index

 

Population 15-65

 

Survey score

 

Advertisement

 

Stores

 

Mean

713603.3679

Mean

2.23E+08

Mean

5.314

Mean

12513.95038

Mean

6.536

Mean

14.36

Mean

9.2

Standard Error

35483.93442

Standard Error

9250910

Standard Error

0.26507106

Standard Error

210.972911

Standard Error

0.236423349

Standard Error

0.660504857

Standard Error

0.476095229

Median

756207.5849

Median

2.23E+08

Median

5.67

Median

13005.56024

Median

7

Median

15

Median

10

Mode

#N/A

Mode

#N/A

Mode

#N/A

Mode

#N/A

Mode

7.1

Mode

15

Mode

10

Standard Deviation

177419.6721

Standard Deviation

46254549

Standard Deviation

1.325355298

Standard Deviation

1054.864555

Standard Deviation

1.182116746

Standard Deviation

3.302524287

Standard Deviation

2.380476143

Sample Variance

31477740040

Sample Variance

2.14E+15

Sample Variance

1.756566667

Sample Variance

1112739.23

Sample Variance

1.3974

Sample Variance

10.90666667

Sample Variance

5.666666667

Kurtosis

-1.39412175

Kurtosis

-0.89636

Kurtosis

-1.133216218

Kurtosis

-0.194709246

Kurtosis

0.790733513

Kurtosis

-1.481640157

Kurtosis

-1.394532051

Skewness

-0.33096273

Skewness

-0.02181

Skewness

-0.137949719

Skewness

-1.033930997

Skewness

-1.19237126

Skewness

-0.345609593

Skewness

-0.419009642

Range

524983.2423

Range

1.7E+08

Range

4.28

Range

3430.877116

Range

4.4

Range

8

Range

6

Minimum

432966.8482

Minimum

1.41E+08

Minimum

3.18

Minimum

10162.59237

Minimum

3.6

Minimum

10

Minimum

6

Maximum

957950.0905

Maximum

3.11E+08

Maximum

7.46

Maximum

13593.46949

Maximum

8

Maximum

18

Maximum

12

Sum

17840084.2

Sum

5.57E+09

Sum

132.85

Sum

312848.7594

Sum

163.4

Sum

359

Sum

230

Count

25

Count

25

Count

25

Count

25

Count

25

Count

25

Count

25

Largest(1)

957950.0905

Largest(1)

3.11E+08

Largest(1)

7.46

Largest(1)

13593.46949

Largest(1)

8

Largest(1)

18

Largest(1)

12

Smallest(1)

432966.8482

Smallest(1)

1.41E+08

Smallest(1)

3.18

Smallest(1)

10162.59237

Smallest(1)

3.6

Smallest(1)

10

Smallest(1)

6

Confidence Level(95.0%)

73235.24069

Confidence Level(95.0%)

19092939

Confidence Level(95.0%)

0.547079775

Confidence Level(95.0%)

435.4266846

Confidence Level(95.0%)

0.487953807

Confidence Level(95.0%)

1.363215016

Confidence Level(95.0%)

0.982612251

Initial descriptive statistics indicates that Federal Islands has average sales $713603.3679 with standard deviation $177419.6721. It indicates a minor variation of the observations from sample mean. The highest sales is $957950.0905 with a total amount of sale $17840084.2. The distribution of Sales of Nokaragua is positively skewed and close to normal as the skewness is (0.33096273).

Graphical interpretation:-
Sales Graph:

The trend of sales of Industria is rising with the presence of some fluctuations in the 25 years. Sales figure for Nokaragua indicates upward trend but sales is lower in amount than Industria. Federal Island indicates a stable and flat trend with the lowest sales figure. Industria has significantly higher average sales over the years from 1991 to 2015.

GDP:

Sweden has highest GDP followed by Industria. GDP is growing over the years and the differences in the GDP are reducing year by year. Federal Island has lagged behind Industria and Nokaragua. Average GDP is also low and insignificant in case of Federal Islands. Sweden has average GDP. Federal Islands has very less amount of average GDP than other countries.

Industrial Population (15-65):

Population trend is higher than other nations in case of Sweden. Nokaragua and Federal Island indicates more or less stable trend. Population of Industria of the range 15-65 years have grown gradually from 1991 to 2009, then got stable after 2009. Federal Island has lowest population in that age limit. Average population (15-65) is insignificant in case of Federal Islands (King'oriah, 2004).

Price Index:-

Federal Islands have significantly different and higher Price Index. Sweden and Nokaragua have similar Price Indexes over the years. Industria has lesser price index than federal Islands. All the Price Index curves of different countries have shown visible fluctuation over the years from 1991 to 2015.

Correlation Analysis:-

value of correlation coefficient ( r)

Interpretation

-1

Perfect negative linear correlation

(-1) to (-.07)

Strong negative linear correlation

(-0.7) to (-0.5)

Moderate negative linear correlation

(-0.5) to (-0.3)

Weak negative linear correlation

(-0.3) to 0

Insignificant negative correlation or no correlation

0

Absolutely no linear correlation

0 to (0.3)

Insignificant negative correlation or no correlation

(0.5) to (0.3)

Weak positive linear correlation

(0.5) to (0.7)

Moderate positive linear correlation

(0.7) to (1)

Strong positive linear correlation

1

Perfect positive linear correlation

(Rodgers and Nicewander 1988)

Federated Islands

 

Sales US$

Survey score

Advertisement

Stores

Sales US$

1

 

 

 

Survey score

0.588601019

1

 

 

Advertisement

0.986236917

0.54833082

1

 

Stores

0.971109284

0.52890297

0.970966786

1

  • Strong positive correlation is between advertisement and sales (0.986236917) (Miller 2014).
  • Strong positive correlation is between sales and number of stores (0.971109284).
  • Strong positive correlation is between advertisement and number of stores (0.970966786).
  • Survey score has a moderate positive correlation with sales (0.588601019).
  • Survey score has a moderate positive correlation with advertisement (0.54833082).
  • Survey score has a moderate positive correlation with stores (0.52890297).
Industria

 

Sales US$

Survey score

Advertisement

Stores

Sales US$

1

 

 

 

Survey score

-0.20067

1

 

 

Advertisement

0.912745

-0.20232024

1

 

Stores

0.916224

-0.230785592

0.969905466

1

  • “Sales” has a strong positive correlation with advertisement (0.912745) and stores (0.916224).
  • Strong positive correlation is between advertisement and stores (0.969905466).
  • Weak negative correlation of survey score exist with sales (-0.20067), advertisement (-0.20232024) and stores (-0.230785592).
Nokaragua

 

Sales US$

Survey score

Advertisement

Stores

Sales US$

1

 

 

 

Survey score

0.019299902

1

 

 

Advertisement

0.990967665

0.002906977

1

 

Stores

0.990458823

0.011010721

0.992123908

1

  • Strong positive correlation is between advertisement and sales (0.990967665).
  • Strong positive correlation is between sales and number of stores (0.990458823).
  • Strong positive correlation is between advertisement and number of stores (0.992123908).
  • Weak negative correlation of survey score exist with all other variables such as with sales (0.019299902), advertisement (0.002906977) and scores (0.011010721).
 Regression Analysis:-

We are interested to determine whether there is any linear relationship between prices and sales in the three countries except Sweden or not. For determining this, the researcher performed simple linear regression to establish whether there exist any causal relationship between price and sales or not. Prudentially we have to determine the causal relationship assuming the Price factors as independent variable and amount of sales as dependent variables. The relationship was significant to make inference of the total sales (Seber and lee 2012). The level of significance was used as 5% (0.05). The value of multiple R-square is also known as coefficient of determination (Harrell 2015).

The structure of hypothesis is given by-

Null Hypothesis (H0): There is no significant linear relationship between sales and price factors.

Alternative Hypothesis (HA): There is a significant linear relationship between sales and price factors.

The regression result to be estimated is-

Regression model of sales

 

 

 

 

 

 

 

SUMMARY OUTPUT

 

 

 

 

 

 

 

 

Regression Statistics

 

 

 

 

 

 

 

Multiple R

0.999992039

 

 

 

 

 

 

 

R Square

0.999984078

 

 

 

 

 

 

 

Adjusted R Square

0.94734831

 

 

 

 

 

 

 

Standard Error

0.061551103

 

 

 

 

 

 

 

Observations

25

 

 

 

 

 

 

 

ANOVA

 

 

 

 

 

 

 

 

 

df

SS

MS

F

 

Significance F

 

 

 

Regression

6

4520.993737

753.4989561

198889.0975

2.22272E-42

 

 

 

Residual

19

0.071982227

0.003788538

 

 

 

 

 

Total

25

4521.065719

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

12.76

#N/A

#N/A

#N/A

#N/A

#N/A

#N/A

#N/A

GDP US$

0.205247077

0.154118182

1.331751219

0.198698856

-0.117325985

0.5278201

-0.11732598

0.52782014

Price index

-0.15767235

0.052124715

-3.024905768

0.006966417

-0.266770631

-0.048574

-0.26677063

-0.04857407

Population 15-65

0.833930629

0.30016399

2.77825008

0.011977372

0.205680179

1.4621811

0.205680179

1.46218108

Survey score

0.123452577

0.075146765

1.642819572

0.116867278

-0.03383141

0.2807366

-0.03383141

0.28073656

Advertisement

0.627153351

0.279416177

2.244513394

0.036902494

0.042328573

1.2119781

0.042328573

1.21197813

Stores

0.006708251

0.232259441

0.028882577

0.977259465

-0.479416345

0.4928328

-0.47941634

0.49283285

Multiple R-square = 0.99999 – It indicates a strong linear association of Sales as response and rest other factors as predictors. 99.99% variability  of sales is explained by rest other price factors in Federal Islands.

F-statistic= 198889.0975– The value of F-statistic is high.

P-value=2.22272E-42– p-value less than 0.05 indicates that we can reject the null hypothesis of insignificant association among the factors to predict sales in Federal Islands at 95% confidence interval.

Regression equation to predict sales for the country Federal Islands is

Ln(Sales) = 12.76 + (0.02 * ln(GDP)) - (0.15 * ln(Price Index)) – (0.84 * ln(Population)) + (0.12 * ln(Survey Score)) + (0.63 * ln(Advertisement))  + (0.006 * ln(number of stores)).

Multiple R-square = 0.999992

F-statistic= 198889.0975

P-value=2.22272E-42.

Regression model of sales

SUMMARY OUTPUT

 

 

 

 

 

 

 

 

Regression Statistics

 

 

 

 

 

 

 

Multiple R

0.999999085

 

 

 

 

 

 

 

R Square

0.99999817

 

 

 

 

 

 

 

Adjusted R Square

0.947366109

 

 

 

 

 

 

 

Standard Error

0.025844511

 

 

 

 

 

 

 

Observations

25

 

 

 

 

 

 

 

ANOVA

 

 

 

 

 

 

 

 

 

df

SS

MS

F

Significance F

 

 

 

Regression

6

6933.140312

1155.523385

1729984

7.79951E-51

 

 

 

Residual

19

0.012690837

0.000667939

 

 

 

 

 

Total

25

6933.153003

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

8.40

#N/A

#N/A

#N/A

#N/A

#N/A

#N/A

#N/A

GDP US$

0.024568308

0.036976687

0.664426949

0.514401

-0.052824789

0.101961404

-0.052824789

0.101961404

Price index

-0.266843767

0.018125986

-14.72161385

7.65E-12

-0.304781892

-0.228905642

-0.304781892

-0.228905642

Population 15-65

0.836051117

0.039904454

20.95132313

1.37E-14

0.752530135

0.9195721

0.752530135

0.9195721

Survey score

0.178619201

0.059815627

2.986162831

0.007591

0.053423656

0.303814747

0.053423656

0.303814747

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0.512402307

0.150781794

3.398303554

0.003016

0.196812386

0.827992229

0.196812386

0.827992229

Stores

0.450532969

0.253939828

1.774172143

0.092061

-0.080969198

0.982035137

-0.080969198

0.982035137

Multiple R-square=0.99999 - It indicates a strong linear association of Sales as response and rest other factors as predictors. 99.99% variability  of sales is explained by rest other price factors  in Industria.

F-statistic=1729984 – The value of F-statistic is very high.

P-value=7.8E-51 -  p-value less than 0.05 indicates that we can reject the null hypothesis of insignificant association among the factors to predict sales in Industria at 95% confidence interval.

Regression equation to predict sales for the country Industria is

Ln(Sales) = 8.40 + (0.02 * ln(GDP)) - (0.26 * ln(Price Index)) + (0.83 * ln(Population)) + (0.17 * ln(Survey Score)) + (0.51 * ln(Advertisement))  + (0.45 * ln(number of stores)).

Multiple R-square=0.99999

F-statistic=1729984

P-value=7.8E-51

Regression model of sales

SUMMARY OUTPUT

 

 

 

 

 

 

 

 

Regression Statistics

 

 

 

 

 

 

 

Multiple R

0.999999797

 

 

 

 

 

 

 

R Square

0.999999594

 

 

 

 

 

 

 

Adjusted R Square

0.947367908

 

 

 

 

 

 

 

Standard Error

0.011583849

 

 

 

 

 

 

 

Observations

25

 

 

 

 

 

 

 

ANOVA

 

 

 

 

 

 

 

 

 

df

SS

MS

F

Significance F

 

 

 

Regression

6

6281.30115

1046.884

7801760

1.01E-56

 

 

 

Residual

19

0.002549525

0.000134

 

 

 

 

 

Total

25

6281.303699

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-5.98

#N/A

#N/A

#N/A

#N/A

#N/A

#N/A

#N/A

GDP US$

0.236433914

0.039740882

5.949388

1E-05

0.153255

0.319613

0.153255

0.319613

Price index

-0.032264777

0.006566533

-4.91352

9.65E-05

-0.04601

-0.01852

-0.04601

-0.01852

Population 15-65

0.483241266

0.059844242

8.074984

1.46E-07

0.357986

0.608497

0.357986

0.608497

Survey score

0.144253973

0.027318231

5.280502

4.27E-05

0.087076

0.201432

0.087076

0.201432

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0.360209578

0.086920562

4.144124

0.000551

0.178283

0.542136

0.178283

0.542136

Stores

0.315096665

0.055014483

5.727522

1.61E-05

0.19995

0.430243

0.19995

0.430243

Multiple R-square= 0.999999 - It indicates a strong linear association of Sales as response and rest other factors as predictors. 99.99% variability  of sales is explained by rest other price factors in Nokaragua country.

F-statistic= 7801760 – the value of F-statistic is very high.

P-value= 1.01E-56 -  p-value less than 0.05 indicates that we can reject the null hypothesis of insignificant association among the factors to predict sales in Nokaragua at 95% confidence interval.

Regression equation to predict sales for the country Nokaragus is

Ln(Sales) = -5.98 + (0.23 * ln(GDP)) - (0.03 * ln(Price Index)) + (0.48 * ln(Population)) + (0.14 * ln(Survey Score)) + (0.36 * ln(Advertisement))  + (0.31 * ln(number of stores)).

Multiple R-square= 0.999999

F-statistic= 7801760

P-value= 1.01E-56

Note that, the predicted sales values for different years are estimated by taking antilog (Exponential function) of the values from the above equations. As in case of Sweden the values of predictor to predict Sales are not present, we are going to take only Price index and Population 15-65 into account.

Sweden Sales Prediction:-

Regression model of sales

SUMMARY OUTPUT

 

 

 

 

 

 

 

 

Regression Statistics

 

 

 

 

 

 

 

Multiple R

0.999946849

 

 

 

 

 

 

 

R Square

0.9998937

 

 

 

 

 

 

 

Adjusted R Square

0.956410818

 

 

 

 

 

 

 

Standard Error

0.283196956

 

 

 

 

 

 

 

Observations

25

 

 

 

 

 

 

 

ANOVA

 

 

 

 

 

 

 

 

 

df

SS

MS

F

Significance F

 

 

 

Regression

2

17351.12234

8675.561169

108173.3833

1.20091E-44

 

 

 

Residual

23

1.844611871

0.080200516

 

 

 

 

 

Total

25

17352.96695

 

 

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

0

#N/A

#N/A

#N/A

#N/A

#N/A

#N/A

#N/A

Price index

-0.203868936

0.140489115

-1.451136881

0.160245349

-0.494492811

0.086754939

-0.494492811

0.08675

Population 15-65

1.697380091

0.007241045

234.4109285

2.31595E-40

1.682400848

1.712359334

1.682400848

1.71236

Regression equation to predict sales for the country Sweden is

Ln(Sales) = - (0.20 * ln(Price Index)) + (1.70 * ln(Population)).

The average value of the Price Index of consumers is 5.31 for Federal Islands, 2.46 for Industria, 2.04 for Nokaragua and 2.14 for Sweden. We can observe that the price index for Nokaragua is closest to Sweden. The averages GDP for 25 years are 1.3403E+11 for Industria, 222890200.4 for Nikaragua, 1.7188E+11 for Federal Island and 2.89924E+11 for Sweden. Therefore, from this angle also Nokaragua is closest to Sweden. The average population of 25 years in Industria is 2666184, 3565573 in Nokaragua, 5983560 in Sweden and 12514 in Federal Island. Therefore, from this angle too Nokaragua is closest to Sweden. We can interpret that Nokaragua could be the best replacement of Sweden. Forecasted values of parameters of Sweden are given below-

Year

GDP US$

Price index

Population 15-65

2016

3.27711E+11

2.014040639

6619033.95

2017

3.3205E+11

2.014740223

6670800.296

2018

3.36446E+11

2.01544005

6722971.497

2019

3.409E+11

2.01614012

6775550.72

2020

3.45413E+11

2.016840433

6828541.157

Alternative method of Financial Forecasting

The alternative forecasting techniques that can be applied are trend analysis and exponential smoothing.

Trend analysis

Trend analysis is a common forecasting technique used by business or other organization to predict the future outcome based on previous data. In statistics, trend analysis captures the pattern of time series behaviour. Regression analysis provides a cause and effect relation based on least square measures (Cameron &Trivedi 2013). Trend analysis can predict the future values without the estimated equation. It analyse the behaviour of variables overtime and then predict the future value. In this study trend of sales and the dependent variables from 1991 to 2015 and the forecasted value of these indicators are used to predict sales of 2016. Accordingly, the predicted sale of 2016 is calculated as 1050012.9. The predicted value of sales by trend analysis is very close to that obtained from the regression analysis.

Exponential smoothing

Exponential smoothing is a kind of moving average used for time series forecasting. The forecasting is done using the following equation

Where

Ft is the forecasted sales of year t

At-1 is the actual sales of previous year

Ft-1 is the forecasted sales of the previous year

α is the smoothening constant , 0<α<1

The forecasting is incorporated for a given value of α. As no value of α is given, it is taken as 0.5. This forecasting technique compares the prior forecasting estimate with actual value and use the difference or error to make fresh forecast (Montgomery, Jennings &Kulahci 2015). Here values of baseline variable are used as a medium of forecasting. In the exponential smoothing previous years’ sales value are utilised to forecast sales in 2016. The forecasted value of sales in 2016 is 898035.5.

Conclusion

  Expansion of business is profitable in Nokaragua as the country shows highest trend. Sweden also indicates good results in various aspects. Advertisement and number of stores should be increased and should be decreased to the decrement of sales. A company in federal Islands should work on all considered factors to grow sales as it indicates comparatively bad results. In Federal Islands, companies need to keep prices low to enhance the amount of sales. In addition, entering a new market in Sweden is Profitable as it shows development almost in all parameters of finance. All the chosen explanatory variables are likely to have large influence on sales.

Recommendations
  • The improvement of Federal Islands is required almost in every field.
  • Industria need to emphasize on GDP US$ and price factors such as survey score and number of stores.
  • Variation in sale amount of all the countries are needed except Sweden.
  • Number of stores is to be increased in comparatively backward countries like Federal Islands or Industria.
References

Cameron, A. C., &Trivedi, P. K. (2013). Regression analysis of count data (Vol. 53). Cambridge university press.

Harrell,F.E. (2015). Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Cham: Springer.

King'oriah, G. K. (2004). Fundamentals of applied statistics. Nairobi: The Jomo Kenyatta Foundation.

Miller, A. (2014). Application of Excel® Pivot Tables and Pivot Charts for Efficient Library Data Analysis and Illustration. Journal Of Library Administration, 54(3), 169-186. https://dx.doi.org/10.1080/01930826.2014.915162

Rodgers, J. L., & Nicewander, W. A. (1988). Thirteen Ways to Look at the Correlation Coefficient. The American Statistician, 42(1), 59–66. Retrieved from https://www.stat.berkeley.edu/users/rabbee/correlation.pdf

Seber, G.A., & Lee, A.J. (2012). Linear regression analysis. New York: Wiley

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My Assignment Help (2021) Analyzing Sales Data And Forecasting Sales Figures [Online]. Available from: https://myassignmenthelp.com/free-samples/stat6003-research-and-enquiry-for-managers/trend-analysis-and-exponential-smoothing.html
[Accessed 29 May 2024].

My Assignment Help. 'Analyzing Sales Data And Forecasting Sales Figures' (My Assignment Help, 2021) <https://myassignmenthelp.com/free-samples/stat6003-research-and-enquiry-for-managers/trend-analysis-and-exponential-smoothing.html> accessed 29 May 2024.

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