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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
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

Solution

David answered on Dec 26 2019
102 Votes
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...
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