# 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...

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Analyse and present data graphically using

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, 2019Torrens University Australia

## Answer To: Analyse and present data graphically using spreadsheet software (Excel). 2. Critically evaluate...

David answered on Dec 26 2019
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
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
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...
SOLUTION.PDF