Report on the relationship between assets and sales
Executive summary
A company’s investment in foreign assets has a bearing on the amount of foreign sales it makes. The regression analysis indicates a positive association between the ratio of foreign sales to total sales and the ratio of foreign assets to total assets. This implies that transnational companies should invest more in foreign assets to improve their foreign sales. They should also consider other factors affecting foreign sales such as international marketing strategies including advertising expenditure, among others. Investment in foreign assets is not the only contributor to foreign sales as indicated by the model’s coefficient of determination. The analysis further showed that there is no significant difference between the ratio of foreign to total sales and that of foreign to total assets. This implies that it does not matter which variable one uses to assess international competitiveness. T-tests also provide that the average of the three ratios is not equal to 55% hence the claim by the economist is not valid.
Introduction 4
Summary statistical analysis 4
Test for the claim on the Index of Transnationality 6
Test of difference between two means –International competitiveness 6
Regression analysis 6
Regression coefficients 7
Goodness of fit 7
Linear relationship 8
Implications of the results 8
Assumptions of regression analysis 8
Conclusion 9
Bibliography 10
Appendices 11
Introduction
Transnational companies face challenges in strategic decision making. Managers of multinational corporations need to make strategic decisions to improve the performance of their foreign operations. To optimise the use of resources, managers should focus on the variables that have a significant impact on foreign sales. It is important to understand the relationship between foreign sales and foreign investment, employees, among other variables. This report provides a detailed analysis of the relationship between the ratios of foreign sales to total sales and that of foreign assets to total assets. The report is based on a comprehensive analysis of the data on foreign assets, total assets, foreign sales, foreign employees and total sales for 25 transnational corporations. The report contains summary statistical analysis, results of the regression analysis, and implications of the results as well as the assumptions.
Summary statistical analysis
A study of summary statistical analysis indicates that the mean ratio of foreign assets to total assets is 58.45% with a standard deviation of 21.73%. Seagram Corporation in the Beverages Industry has the maximum ratio of 98.1982% while General Motors of the Automotive Industry had the lowest ratio of 26.21232%.
Figure 1: Graph of the ratio of foreign assets to total assets
The mean ratio of foreign sales to sales for the 25 corporations is 66.637% with a standard deviation of 22.689%. Nestla, SA had the highest ratio of foreign sales to total sales (98.5507%) while General Electric had the lowest ratio of foreign sales to total assets (26.9824%).
Figure 2: Ratio of foreign sales to total sales
The analysis of this ratio for the 25 corporations shows that the average of the ratios of foreign employees to total employees is 64.187% with a standard deviation of 20.126%. Asea Brown Boveri has the highest ratio of foreign employees to total employees of the 25 companies. Its foreign employees constitute 97.6% of its total workforce. Daimler-Benz AG has the least percentage of foreign employees in its total workforce. The corporation’s foreign employees make just 24.9% of the total employees.
Test for the claim on the index of nationality
International competitiveness: Test of difference between means
A t-test was conducted to determine if there is a significant difference between the mean ratio of foreign sales to total sales and that of foreign assets to total assets. A pooled t-test since the population variances are equal (Anderson, Sweeney and Williams, 2011, 401).
The t-test results (from excel) in Appendix 1 shows that the t-Statistic is -1.3023055 with a p-value for the t-Statistic is 0.19902638. The p-value is more than 0.05 hence we cannot reject the null hypothesis. This implies that the data given does not provide sufficient evidence that the two average ratios are different at 5% confidence level. Therefore, there is no difference between the ratio of foreign assets to total assets and the ratio of foreign sales to total sales. The CEO’s claim that it does not matter which ratio a person uses to assess international competitiveness is valid.
Assumptions of the test
The t-tests assumed that the 25 corporations were randomly obtained from a defined population of corporations (Oakshott, 2006). T-test assumes random sampling was used in selecting the sample. Secondly, it assumes that the scores follow a normal distribution in the population. It also assumed that population variances are equal. This can be tested by comparing the sample variances. Besides, the test assumed that the samples used are independent and do not overlap. The results of the test will be misleading if the assumptions do not hold. Most of the above assumptions are true though they may not be perfectly true.
REGRESSION ANALYSIS
This analysis helps in expressing a dependent variable in terms of one or more independent variables (Black, 2011, p. 507). The analysis indicates whether there is a relationship between two or more variables. The result of a regression analysis is the regression model or equation which can be used to predict the values of the response variable given the values of the explanatory variables. It also provides measures for testing the reliability of the regression equation.
The ratio of foreign sales to total sales was the response variable while the ratio of foreign assets to total assets was the explanatory variable. The value of each of the two variables for the 25 corporations were regressed to obtain a model expressing the ratio of foreign sales to total assets as a function of the ratio of foreign assets to total assets.
Regression results
Y = 0.1664 + 0.8553x
Regression coefficients
The results of the regression analysis indicates that the coefficient is 0.553 indicating that an increase in the ratio of foreign assets to total assets by 1% causes a 0.8553% increase in the ratio of foreign sales to total sales. The coefficient was found to be statistically significant since the p-value was less than 0.05. Thus, the coefficient is good measure of change in the ratio of foreign sales to total sales caused by a 1% change in the ratio of foreign assets to total assets.
The intercept shows the ratio of foreign sales to total sales when the ratio of foreign assets to total assets is zero. This indicates that a corporation can still make foreign sales even if there is no investment in foreign assets. Significance tests also indicated that the intercept is statistically significant. Therefore, the intercept is a reliable measure of the ratio of foreign sales to total sales when the ratio of foreign assets to total assets is zero.
Goodness of fit of the model/Test of reliability of the model
For the model to be used in predicting the ratio of foreign sales to total sales, its reliability must be assessed. The first test is the coefficient of determination. As shown in Appendix 3, the R-Squared of the model is 0.671052905 indicating that 67% of the total changes in the ratio of foreign sales to total sales are explained by the changes in the ratio of foreign assets to total assets. The coefficient of determination is more than 50% hence the model is reliable since more than 50% of changes in the response variable are caused by changes in the explanatory variable.
The reliability of the model was also tested using the F-test. F-test is used in testing if the relationship between the response variable and the explanatory variable is statistically significant. Significance tests showed that the model is statistically significant and that the relationship between the two ratios was not just accidental (Sprinthall, 2012). The Significance F was found to be less than 0.05 as shown in appendix 3.
Linear relationship
The scatter plot indicates that the two ratios have a linear relationship. The pattern of the data points on the scatter plot suggests that the two variables move in the same direction. Besides, the regression line indicates a positive linear association between the two ratios. The equation of the regression line is expressed in the form of a linear function as shown in the scatter plot above.
Implications of the results
The tests carried provide sufficient evidence that there is a significant relationship between the two ratios. The coefficient of the slope, as well as the intercept, are statistically significant. Besides the model has passed the goodness of fit tests. This implies that foreign the volume of sales a multinational company makes is dependent on the amount of the company’s investment in foreign assets. A multinational company must invest in foreign assets to increase foreign n sales. The coefficient of determination for the model is only 67%. It implies that investment in foreign assets is not the only factor contributing to foreign sales. Other variables contribute to over 30% of the changes in the ratio of foreign sales to total sales. Therefore, multinational corporations should not only focus on foreign assets in their strategies to boost foreign sales. They must consider other variables such as foreign employees, marketing strategies, among other factors that may affect foreign sales. These findings are important to multinational corporations in their international business strategy formulation.
Assumptions of the analysis
The regression analysis above assumed that the ratio of foreign sales to total sales and the ratio of foreign assets to total assets are linearly related. As shown in the scatterplot above, there is enough evidence to conclude that the relationship is linear. Besides, the errors should be statistically independent and should follow a normal distribution. The independent variables should also additive (Sprinthall, 2012). The impact of different explanatory variables on the response variable should be additive. Where there are more than one explanatory variables, there should be no auto-correlation between the variables. Changes in one explanatory variable should not be causing any changes in another explanatory variable.
Estimation of the ratio of foreign sales to total assets
Equation: Y = 0.1664 + 0.8553x
When the ratio of foreign assets to total assets is 20%
Y = 0.1664 + (0.8553 × 0.2)
= 0.1164 + 0.1706
= 0.3375
The ratio of foreign sales to total assets will be 33.75% if that of foreign assets to total assets is 20%.
When the ratio of foreign assets to total assets is 50%
Y = 0.1664 + (0.8553 × 0.5)
= 0.1164 + 0.4277
= 0.5941
The ratio of foreign sales to total assets will be 59.41% if that of foreign assets to total assets is 50%.
Conclusion
The descriptive statistics of the three ratios and the t-tests indicate that the index of transnationality is not equal to 55% a claimed by the economist. A further test of the difference between the average of the ratio of foreign sales to total sales and that of foreign assets to total assets revealed that there is no significant difference. Any of the two ratios can be used to assess international competitiveness as argued by the CEO of one of the corporations. The regression analysis indicates a significant relationship between the ratio of foreign sales to total assets and that of foreign assets to total assets. Thus, multinational companies can improve foreign n sales by increasing their investment in foreign assets. However, their international strategies should not ignore other variables such as advertising expenditure, human resource practices, among other variables.
Bibliography
Anderson, D., Sweeney, D. and Williams, T. (2011). Fundamentals of business statistics. 6th
ed. New York: Cengage Learning.
Black, K. (2011). Business Statistics: For Contemporary Decision Making. Hoboken, NJ:
Wiley.
Brandimarte, P. (2011). Quantitative methods. Hoboken, N.J.: Wiley.
Oakshott, L. (2006). Essential quantitative methods for business, management, and finance.
Basingstoke [England]: Palgrave Macmillan.
Sprinthall, R. (2012). Basic statistical analysis. Boston: Pearson Allyn & Bacon.
APPENDICES
t- Test results
Null hypothesis: H0: µ1 = µ0
Alternative hypothesis, HA: µ1 ≠ µ0
Testing the claim that the average ratio is 55%
H0: µ = 55%
HA: µ ≠ 55%
The t-statistic is obtained by the following formula:
t =(X- µ0) (s/n)
X is the sample mean, µ0 is the hypothesised mean, s is the sample standard deviation while n is the sample size.
t =(63.09- 55) (18.67/25)
t = 2.1666
P (T < t = 2.1666) = 0.0202.
Two-tailed = 0.0404
Regression results
