Data Analysis and Forecasting

Data Analysis and Forecasting

Introduction

Expansion is an essential process that an organization has to adequately consider in maintaining a more influential platform for successful engagement. As a salesperson, it is vital to assess different measures which can help in maintaining a unique system for an improved level of development. Various factors need to be evaluated in determining the underlying independent factors that influence vehicle ownership. Thus, ensuring that the expansion criteria are adequately assessed, it would be possible to ensure that the company emphasizes better concepts that promote the needs of individuals. Therefore, this research will determine the data collected in Turkey. The data focused on the most recent data in 2018. Thus, the research will involve the assessment of different factors that will help in improving the company commitment for Automobile Inc. so that it would help in making a better decision regarding its focus on expansion.

Scatterplots

Scatterplots are essential in assessing the underlying relationship between two variables that are investigated. The relationship can be either linear or inverse based on the relationship of the variables that are assessed. Therefore, the scatterplot will be plotted, focusing on the relationship between vehicles and income, population, population density, and percentage of the population in urban areas.

Scatterplot between vehicles per thousand against income

The scatterplot shows that there is a positive linear relationship between the vehicles per 1000 population and per capita income. There is a closer distribution of the variables along the line. The correlation coefficient shows that there is a moderate positive relationship between the variables (r² = 0.524).

Scatterplot between vehicles per thousand against the population

The scatter plot shows that there is no linear relationship between vehicles per 1000 and the population. The correlation shows that there is a very weak relationship between the variables (r² = 0.026).

Scatterplot between vehicles per thousand against population density

The scatterplot analysis shows that there is a wider spread of vehicles per 1000 and population density. The wider distribution of data presents a different platform within which it is possible to understand that there is a weak correlation, which is defined with a correlation coefficient of r² = 0.00024.

Scatterplot between vehicles per thousand against percentage population in urban areas

The analysis from the scatterplot shows that there is comprehensive data, which indicates that there is no relationship between the variables included in the report. The findings reveal that there was a weak positive correlation between vehicles per 1000 population and the percentage of population in urban areas (r² = 0.154).

Variables that are closely correlated with  vehicles per 1000 population

Based on the scatterplot and correlation analysis of the variables. the findings showed that per capita income (r² = 0.524) and percentage population in urban area (r² =0.154). therefore it is possible to develop a regression model. The variables that are considered in this case include the dependent variable, which is the vehicles per 1000 population, while the independent variables that were investigated in the study include a percentage of the population in urban areas and per capita income.

The regression equation for the relationship between vehicles per 1000 population

Thus the regression equation, in this case, is

This means that an increase in one unit of per capita income the vehicles per 1000 population becomes 255.

Scatter graphs for Total vehicle ownership against the population, population density per km^2, and population in urban areas.

Scatter graph between total vehicle ownership and population

There is a robust linear relationship between the total vehicle ownership and population, as shown in the scatter graph. The correlation coefficient shows that there is a strong positive relationship with the variables (r² = 0.974).

Scatter graph between total vehicle ownership and population density per km²

The analysis shows that the variables are widely distributed, which indicates that there is no linear relationship between Total vehicles (millions) and Population density per km^2. The correlation coefficient shows that there is quite a weak relationship between Total vehicles (millions) and Population density per km^2 (r² = 0.079).

Scatter graph between total vehicle ownership and percentage population in urban areas

The scatter graph shows that there is no linear relationship between the variable, considering that the variables are widely distributed. The correlation coefficient analysis presents a different approach in helping understand the relationship between total vehicles and percentage population in urban areas (r² = 0.014).

Variables that are closely correlated with total vehicle ownership

Thus, from the analysis, the variable that is closely related to the vehicle ownership variable is the relationship between total vehicle ownership and the population (r² = 0.974). Therefore the regression equation will be

From the regression analysis, the equation is

The most crucial regression for the company

The regression equation between total car ownership and the population is the most crucial regression which helps understand the ability of individuals to own a car. Automobile Inc is a car selling firm that will want to utilize essential information from the field and help ensure that there is a greater understanding of different elements that define positive outcomes. The analysis has shown that population size explains 97% of car ownership. Thus, understanding the total population, present a better focus on the need for cars within the country. The intention to expand into Turkey will be based on the assessment between the country’s total population and car demand.

Predicting data for Turkey

The regression equations that have been calculated in this case include vehicles per 1000 population and an increase in income per capita

Thus, the regression equation, in this case, is y=265.71-10.71x

The second regression equation identifies  vehicle ownership and population

y= -0.91+0.58x

Therefore,

The available data from Turkey shows that

Predicting vehicles per 1000 population from income

is y=265.71-10.71x

x = 6.1

thus,

is y=265.71-10.71(6.1)

y = 265.71- 65.33

y = 200

Predicting vehicle ownership from the population

y= -0.91+0.58x

x = 67

Thus

y= -0.91+0.58(67)

y = -0.91 + 38.86

y = 38

The predicted figures based on the two equations show a higher finding considering that there are 200 vehicles per 1000 population, while there are 38 million total vehicles in a population of 67 million individuals. The prediction is higher mainly because there is a higher rate of vehicle ownership in the rest of Europe compared to the situation in Turkey. This means that there exists a gap in total vehicle ownership in Turkey, which the company can exploit in its expansion efforts. The differences can also be explained by other factors such as government regulations on vehicle imports. This is based on the fact that countries in Europe implement varying regulations aimed at protecting their local firms. However, the deficit is too big and cannot be effectively bridged by the local market.

Conclusion

The decision that Automobile Inc. should make considering the findings from the analysis should inform its efforts to expand into Turkey. It is essential to ensure that there is a better understanding of specific concepts that help in building a robust change platform that can be effectively undertaken to improve the level of company development. The findings from the analysis have shown that there is a considerable vehicle ownership gap in Turkey, which the company can consider in its expansion strategy. The decision that is taken in this case must provide a clear context within which it is easier to attain improved organizational outcomes. The decision that is taken must focus on maximizing the company’s profitability through a more significant commitment to different changes that help in understanding the changes within the industry. Therefore, expansion to the Turkish market seems a viable option considering that the company can exploit the current gap, which is extremely large. Although it is essential to conduct market research and help maintain a proper focus on different factors that define improved outcomes and minimize risks.

The regression equations that have been calculated in this case include vehicles per 1000 population and an increase in income per capita

Thus the regression equation, in this case, is

The second regression equation identifies  vehicle ownership and population

Therefore,

The available data from Turkey shows that

Predicting vehicles per 1000 population from income

is

x = 6.1

thus,

is

y = 265.71- 65.33

y = 200

Predicting vehicle ownership from the population

x = 67

Thus

y = -0.91 + 38.86

y = 38

The predicted figures based on the two equations show a higher finding considering that there are 200 vehicles per 1000 population, while there are 38 million total vehicles in a population of 67 million individuals. The prediction is higher mainly because there is a higher rate of vehicle ownership in the rest of Europe compared to the situation in Turkey. This means that there exists a gap in total vehicle ownership in Turkey, which the company can exploit in its expansion efforts. The differences can also be explained by other factors such as government regulations on vehicle imports. This is based on the fact that countries in Europe implement varying regulations aimed at protecting their local firms. However, the deficit is too big and cannot be effectively bridged by the local market

Conclusion

The decision that Automobile Inc. should make considering its efforts to expand into Turkey should be informed by the findings from the analysis. It is essential to ensure that there is a better understanding of specific concepts that help in building a strong change platform that can be effectively undertaken to improve the level of company development. The findings from the analysis have shown that there is a huge vehicle ownership gap in Turkey, which the company can consider in its expansion strategy. The decision that is taken in this case must provide a clear context within which it is easier to attain improved organizational outcomes. The decision that is taken must focus on maximizing the company’s profitability through a greater commitment to different changes, which help in understanding the changes within the industry. Therefore expansion to the Turkish market seems a viable option considering that the company can exploit the current gap, which is extremely large. Although it is essential to conduct market research and help maintain a proper focus on different factors that define improved outcomes and minimize risks.

References

Fan, Z.P., Che, Y.J. and Chen, Z.Y., 2017. Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research, 74, pp.90-100.

Ghinea, C., Drăgoi, E.N., Comăniţă, E.D., Gavrilescu, M., Câmpean, T., Curteanu, S.I.L.V.I.A. and Gavrilescu, M., 2016. Forecasting municipal solid waste generation using prognostic tools and regression analysis. Journal of environmental management, 182, pp.80-93.

Tang, L. and Sun, J., 2019. Predict the sales of New-energy Vehicle using linear regression analysis. In E3S Web of Conferences (Vol. 118, p. 02076). EDP Sciences.

• Plot scatter graphs of vehicles per thousand population against income, population, population density and percentage of population in urban areas in the Excel file with which you believe there might be correlation. What do your results suggest? [10%]