Instructions Using The Research Question And Two Variables Y
Instructions Using the research question and two variables your learning team developed for the week 2 business research project Part 1 assignment, 1
Using the research question and two variables your learning team developed for the Week 2 Business Research Project Part 1 assignment, create a no more than 350-word inferential statistics (hypothesis test). Include: (a) The research question, (b) Mock data for the independent and dependent variables, determine the appropriate statistical tool to test the hypothesis based on the research question, conduct a hypothesis test with a 95% confidence level using the statistical tool, interpret the results, and provide your findings. Format your paper consistent with APA guidelines. Submit both the spreadsheet and the paper.
Paper For Above instruction
The research question posed by my learning team is: “What is the impact of price on the supply of titanium?” This question aims to examine how changes in market price influence the availability of titanium, a critical component used in aerospace, medical devices, and industrial applications. The two key variables involved are the independent variable—price of titanium—and the dependent variable—the availability or supply of titanium. To explore this relationship, we developed mock data simulating market price fluctuations and corresponding supply levels over time.
Our mock data includes three price points: $10, $15, and $20 per kilogram, with associated supply quantities of 300, 250, and 200 metric tons respectively. The data suggest an inverse relationship; as the price increases, the supply decreases. Given this context, the appropriate statistical test is a simple linear regression to analyze the correlation between price and supply, or alternatively, a correlation coefficient test. Considering the small sample size, a Pearson correlation coefficient test with significance at the 0.05 level is suitable.
Applying the Pearson correlation coefficient to the mock data yields an r-value of -0.99, indicating a very strong negative correlation between the price and supply of titanium. Conducting a hypothesis test for the correlation coefficient, the t-statistic is calculated as t = r√(n-2)/(1-r^2), which results in a t-value exceeding the critical value for 1 degree of freedom at the 95% confidence level. Therefore, we reject the null hypothesis that there is no correlation, concluding that a significant inverse relationship exists between titanium price and supply. These findings support the economic theory that higher prices tend to decrease supply availability in this context, possibly due to supply chain constraints or strategic stockpiling by suppliers.
In summary, our mock analysis demonstrates that understanding the price-supply relationship through hypothesis testing provides valuable insights for stakeholders in the titanium market. This information can guide pricing strategies and supply chain management, emphasizing the importance of empirical statistical analysis in business decision-making.
References
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