 Analyse and present data graphically usingspreadsheet software (Excel).
 Critically evaluate summary statistics against suitable benchmarks.
 Apply judgement to select appropriate methods of data analysis drawing on knowledge of regression analysis, probability, probability distributions and sampling distributions.
 Select and apply a range of data analysis tools to inform problem solving and decision making.
 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
 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?
 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.

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?
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 ageinterval 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.
Sales US$ 

GDP US$ 

Price index 

Population 1565 

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 $4.34E+08. The distribution of Sales of Industria is slightly negatively skewed and almost close to normal as the skewness is (0.05202).
Sales US$ 

GDP US$ 

Price index 

Population 1565 

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 $196491965.6. The distribution of Sales of Nokaragua is slightly positively skewed and almost close to normal as the skewness is (0.01944256).
GDP US$ 

Price index 

Population 1565 

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.
Sales US$ 

GDP US$ 

Price index 

Population 1565 

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).
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 (1565):
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 1565 years have grown gradually from 1991 to 2009, then got stable after 2009. Federal Island has lowest population in that age limit. Average population (1565) 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.
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)

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).

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).

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).
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 Rsquare is also known as coefficient of determination (Harrell 2015).
The structure of hypothesis is given by
Null Hypothesis (H_{0}): There is no significant linear relationship between sales and price factors.
Alternative Hypothesis (H_{A}): There is a significant linear relationship between sales and price factors.
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 
2.22272E42 



Residual 
19 
0.071982227 
0.003788538 





Total 
25 
4521.065719 







Coefficients 
Standard Error 
t Stat 
Pvalue 
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 1565 
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 Rsquare = 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.
Fstatistic= 198889.0975– The value of Fstatistic is high.
Pvalue=2.22272E42– pvalue 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 Rsquare = 0.999992
Fstatistic= 198889.0975
Pvalue=2.22272E42.
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 
7.79951E51 



Residual 
19 
0.012690837 
0.000667939 





Total 
25 
6933.153003 







Coefficients 
Standard Error 
t Stat 
Pvalue 
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 
7.65E12 
0.304781892 
0.228905642 
0.304781892 
0.228905642 
Population 1565 
0.836051117 
0.039904454 
20.95132313 
1.37E14 
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 
Advertisement 
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 Rsquare=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.
Fstatistic=1729984 – The value of Fstatistic is very high.
Pvalue=7.8E51  pvalue 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 Rsquare=0.99999
Fstatistic=1729984
Pvalue=7.8E51
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 
1.01E56 



Residual 
19 
0.002549525 
0.000134 





Total 
25 
6281.303699 







Coefficients 
Standard Error 
t Stat 
Pvalue 
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 
1E05 
0.153255 
0.319613 
0.153255 
0.319613 
Price index 
0.032264777 
0.006566533 
4.91352 
9.65E05 
0.04601 
0.01852 
0.04601 
0.01852 
Population 1565 
0.483241266 
0.059844242 
8.074984 
1.46E07 
0.357986 
0.608497 
0.357986 
0.608497 
Survey score 
0.144253973 
0.027318231 
5.280502 
4.27E05 
0.087076 
0.201432 
0.087076 
0.201432 
Advertisement 
0.360209578 
0.086920562 
4.144124 
0.000551 
0.178283 
0.542136 
0.178283 
0.542136 
Stores 
0.315096665 
0.055014483 
5.727522 
1.61E05 
0.19995 
0.430243 
0.19995 
0.430243 
Multiple Rsquare= 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.
Fstatistic= 7801760 – the value of Fstatistic is very high.
Pvalue= 1.01E56  pvalue 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 Rsquare= 0.999999
Fstatistic= 7801760
Pvalue= 1.01E56
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 1565 into account.
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 
1.20091E44 



Residual 
23 
1.844611871 
0.080200516 





Total 
25 
17352.96695 







Coefficients 
Standard Error 
t Stat 
Pvalue 
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 1565 
1.697380091 
0.007241045 
234.4109285 
2.31595E40 
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 1565 
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 
The alternative forecasting techniques that can be applied are trend analysis and exponential smoothing.
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 is a kind of moving average used for time series forecasting. The forecasting is done using the following equation
Where
F_{t }is the forecasted sales of year t
A_{t1} is the actual sales of previous year
F_{t1} 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.
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.
 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.
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), 169186. https://dx.doi.org/10.1080/01930826.2014.915162
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Seber, G.A., & Lee, A.J. (2012). Linear regression analysis. New York: Wiley
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