Analyse and present data graphically using

spreadsheet software (Excel).

2. Critically evaluate summary statistics against suitable

benchmarks.

3. Apply judgment 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.

Answered Same DayDec 05, 2019STAT6003Torrens University Australia

Regression analysis helps me to predict the sales of federated island, Industria, Nokaragua, and Sweden country. I am given ln(GDP), price index, population, advertisement and stores for countries federated island, Industria, Nokaragua, and Sweden country. I perform three regression analysis to predict sales of federated island, Industria and Nokaragua on basis of co

esponding ln(GDP), price index, population, advertisement and stores for each country. The values of future independent variables are predicted by using formula of growth rate. I plug in these calculated independent variables in the co

esponding regression equation for 3 countries federated island, Industria, Nokaragua in order to obtain the predicted value of sales. For the prediction of sales co

esponding to Sweden, I choose the best appropriate regression model among 3 models created for countries federated island, Industria and Nokaragua. The value of GDP/population and Price index for Sweden is compared with that of federated island, Industria and Nokaragua. The country among federated island, Industria and Nokaragua with closest GDP/population and Price to Sweden is selected to prediction of Sweden sales. Then I plug in the calculated values of independent variables (using growth rate) in the co

esponding regression equation for selected country.

There are 25 observations of sales for each country. The mean sale of federated island, Industria and Nokaragua is $713,603, $17,372,234 and $7,859,679. Industria highest mean and median of sales. And federated Island has the lowest mean and median of sales. The minimum and maximum of sales for Federated Islands, Industria and Nokaragua is ($432,967 ; $957,950) ($11,919,253 ; $23,103,581) and ($5,162,754 ; $10,554,044) respectively.

Advertisement and Stores have a strong linear relationship between them for three countries of my interest Federated Islands, Industria and Nokaragua. Hence there is problem of Multi-co-linearity. Survey store has weak linear relationship with Sales. Hence I Remove survey store as independent variable in further regression analysis. Fox, J. (1997).

For country Federated Islands, (Sales, GDP) (Sales, population 15-65) has positive linear relationship. Sales and price index have a weak negative linear relationship. The regression output is given below.

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.99731

R Square

0.994628

Adjusted R Square

0.993215

Standard E

o

14614.74

Observations

25

ANOVA

df

SS

MS

F

Significance F

Regression

5

751407540992

150281508198

703.5963

7.18053E-21

Residual

19

4058219974

213590525

Total

24

755465760966

Coefficients

Standard E

o

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-1295339

640295.1687

-2.02303359

0.057371

-2635491.824

44814.56

-2635492

44814.56

ln(gdp)

82937.48

36853.36199

2.250472565

0.036458

5802.506939

160072.5

5802.507

160072.5

Price index

-19470

2528.258944

-7.700948584

2.94E-07

-24761.69892

-14178.3

-24761.7

-14178.3

Population 15-65

-21.1937

8.052980923

-2.631787281

0.01643

-38.04881555

-4.33865

-38.0488

-4.33865

Advertisement

42453.3

4096.434857

10.36347511

2.95E-09

33879.36399

51027.24

33879.36

51027.24

Stores

19081.54

5497.566605

3.47090629

0.002559

7574.999367

30588.08

7574.999

30588.08

There is 99.46% variation in sales which is explained by ln(GDP), price index, population 15-65, advertisement and stores. Regression equation is given by sales = -1295338.63 + 82937.488ln(GDP) - 19470*Price_index - 21.197*population + 42453.3*advertisement + 19801.54*stores. Ho: coefficient of independent...

esponding ln(GDP), price index, population, advertisement and stores for each country. The values of future independent variables are predicted by using formula of growth rate. I plug in these calculated independent variables in the co

esponding regression equation for 3 countries federated island, Industria, Nokaragua in order to obtain the predicted value of sales. For the prediction of sales co

esponding to Sweden, I choose the best appropriate regression model among 3 models created for countries federated island, Industria and Nokaragua. The value of GDP/population and Price index for Sweden is compared with that of federated island, Industria and Nokaragua. The country among federated island, Industria and Nokaragua with closest GDP/population and Price to Sweden is selected to prediction of Sweden sales. Then I plug in the calculated values of independent variables (using growth rate) in the co

esponding regression equation for selected country.

There are 25 observations of sales for each country. The mean sale of federated island, Industria and Nokaragua is $713,603, $17,372,234 and $7,859,679. Industria highest mean and median of sales. And federated Island has the lowest mean and median of sales. The minimum and maximum of sales for Federated Islands, Industria and Nokaragua is ($432,967 ; $957,950) ($11,919,253 ; $23,103,581) and ($5,162,754 ; $10,554,044) respectively.

Advertisement and Stores have a strong linear relationship between them for three countries of my interest Federated Islands, Industria and Nokaragua. Hence there is problem of Multi-co-linearity. Survey store has weak linear relationship with Sales. Hence I Remove survey store as independent variable in further regression analysis. Fox, J. (1997).

For country Federated Islands, (Sales, GDP) (Sales, population 15-65) has positive linear relationship. Sales and price index have a weak negative linear relationship. The regression output is given below.

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.99731

R Square

0.994628

Adjusted R Square

0.993215

Standard E

o

14614.74

Observations

25

ANOVA

df

SS

MS

F

Significance F

Regression

5

751407540992

150281508198

703.5963

7.18053E-21

Residual

19

4058219974

213590525

Total

24

755465760966

Coefficients

Standard E

o

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-1295339

640295.1687

-2.02303359

0.057371

-2635491.824

44814.56

-2635492

44814.56

ln(gdp)

82937.48

36853.36199

2.250472565

0.036458

5802.506939

160072.5

5802.507

160072.5

Price index

-19470

2528.258944

-7.700948584

2.94E-07

-24761.69892

-14178.3

-24761.7

-14178.3

Population 15-65

-21.1937

8.052980923

-2.631787281

0.01643

-38.04881555

-4.33865

-38.0488

-4.33865

Advertisement

42453.3

4096.434857

10.36347511

2.95E-09

33879.36399

51027.24

33879.36

51027.24

Stores

19081.54

5497.566605

3.47090629

0.002559

7574.999367

30588.08

7574.999

30588.08

There is 99.46% variation in sales which is explained by ln(GDP), price index, population 15-65, advertisement and stores. Regression equation is given by sales = -1295338.63 + 82937.488ln(GDP) - 19470*Price_index - 21.197*population + 42453.3*advertisement + 19801.54*stores. Ho: coefficient of independent...

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