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This work aims at analyzing the data from an Australian manufacturing company. These results from the analysis will help the manufacturer company to expand its market into Asia. The method by which the data were collected was sampling products from different markets. In part 1 of the analysis, we find that the shoe prices in males and females differ between genders and three countries i.e. Thailand Singapore, and China. In part 2, even though the price is different among the genders, there is no statistical evidence to support and validate the claim. The ANOVA test in part 2 of the work supports and validates the fact the prices of the three countries are different. It was also found that there is no connection between the price of the shoes and the cost of production. Since there is no statistical evidence to validate the price of shoes among the genders, the company should focus on selling both genders of shoes. It is also recommended that the shoes be sold in Thailand rather than the other two nations as the price is higher there. We can say if other factors are kept constant the company will have higher sales than other region.
The business problem that needs to be tackled is to establish the product that needs to be produced by the new production line. The products that are used for the study are on gender i.e. female and male products. We need to determine the product that can be used in the next production line. It is either one or both products. The business person needs to take a concrete decision on which product to focus on introducing in a new market. The interest or demand of people in the market is to be considered. Mostly the problematic decision depends on the interest of people in the new market and whether a product is better in a certain location than in other locations. The business person with some reasons makes critical mistakes in introducing the new product in the market as they lack the preference of the people living in those regions. In this work, we are analyzing Shoes data from three different countries to conduct the statistical analysis to help in making effective and efficient predictions. With these data and analysis, the company can make better critical decisions.
The statistical problem here is we compare the performance of the product. We are conducting descriptive statistics to analyze the performance of the products. We prepare some charts, Central Tendency, and some relationship analysis. We are involved in doing some t-tests and ANOVA tests to support the hypothesis formulated. We prepare a scatter-plot and interpret the plot to determine the relationship among those variables. Lastly, we also prepare a Linear regression model to confirm the results obtained from the scatter plot.
The mean price for the men's shoes is $128.53 while that for the women's is $118.48.This means that men’s shoes are more expensive compared to women's. The median for men’s shoes is $133 while that for women is $120 This means that the middle price for men’s shoes is $133 and that for women is $120 This also means that the price for men’s shoes is higher than that of the female. The Standard deviation for the men’s shoes is 66.51233652 while that for the female is 60.70893775. This means that the prices of the women’s shoes were spread from the mean compared to the price for the men’s shoes.
Similarly, the coefficient of variation of the women’s shoe price 51.23917847 is widely spread compared to that of the men's 51.74714978. The mode for the men’s shoes is 66 and that for the female is 43. This shows that most of the men’s shoes were sold at $66 while that for the female was $43. Both men's and women; 's shoe prices are distributed symmetrically and there are no outliners in both prices. (Bluman, 2013).
The average price of shoes in Thailand is $160.8611111 is the highest compared to Singapore ($107.5142857) and China $93.857. The same applies to the median and the mode. This means that the shoe price in Thailand is the highest amongst China and Singapore. Most of the shoes in Thailand are sold at $ 170 in China $67.5, and in Singapore, they are sold at $89. Using the standard deviation and the coefficient of variation, we can conclude the price of shoes in Singapore was the most spread than the price of shoes in Thailand and China.
It can be noticed that the distribution of the price of shoes in Thailand is skewed to the right. The distribution of the price of shoes in Singapore is skewed to the left while the distribution of the price of shoes in China is skewed to the left (Groebner et al. 2013).
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