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MGMT601W3IP 1 MGMT601-Unit 3 Individual Project Misty D’Spain Colorado Technical University

MGMT601W3IP 2 Use Common Statistical Tests to Draw Conclusions from Data This project will explore the application of the chi-square distribution tool to support our decision to evaluate whether to broaden our horizon on market expansion or maintain our current situation with our sporting goods client. We detect that we do not have adequate data for a chi-square analysis, we will validate the initial steps of a nonparametric test, centering on qualitative aspects. Understanding Chi-Square and Hypothesis Testing The chi-square distribution is a statistical tool used for hypothesis testing in nonparametric settings, which means it does not rely on specific assumptions about the data’s distribution. Instead, it examines the relationship between two categorical variables to determine if they are independent or if they are independent or if there is a significant association between them. The chi-square test helps make informed decisions by assessing the relevance and significance of observed data. The data used in calculating a chi-square statistic must be random, raw, mutually exclusive, drawn from independent variables, and drawn from a large enough sample (Hayes, 2021). The Big D Scenario In this study, working with the “Big D” scenario, considering two proposed product lines in the outdoor sporting goods market. Assuming the same demographics that are utilized for each product, meaning that we have data on the same group of potential customers. Initial Steps of a Nonparametric Test 1. Null and Alternative Hypothesis: The first step in applying the chi-square test is formulating null (H0) and alternative (H1) hypotheses. In the scenario, the null hypothesis could be that there is no significant difference in customer preferences between the two proposed product lines, meaning they are equally likely to succeed in the market. The alternative hypothesis would be that there is a significant difference, suggesting that one product line is more likely to be successful than the other.

MGMT601W3IP 3 Null Hypothesis (H0): There is no significant difference in customer preferences between the two proposed product lines. Alternative Hypothesis (H1): There are significant differences in customer preferences between the two proposed product lines. 1. Data Collection and Analysis: To initiate the chi-square process, we would need to collect data related to customer preferences for the two product lines. This data could include surveys, focus groups, or market research. We would then organize this data into a contingency table, which would allow us to compare the observed and expected frequencies of the preferences for each product line. (2) Data Collection and Analysis: To initiate the chi-square process, we would need to collect data related to customer preferences for the two product lines. This data could include surveys, focus groups, or market research. We would then organize this data into a contingency table, allowing us to compare the observed and expected frequencies of the preferences for each product line. (3) Chi-Square Statistic and P-value: After organizing the data, we calculate the chi-square statistic and the associated p-value. The chi-square statistic quantifies the difference between the observed and expected frequencies, the p- value indicates the probability of observing such a difference by chance. The chi-square statistic compares the size of any discrepancies between the expected results and the actual results, given the size of the sample and the number of variables in the relationship (Hayes, 2021), Implications for the Board of Directors and Decision-Making: (1) Hypothesis Testing Outcome: The chi-square test will provide valuable insights into whether there is a statistically significant difference in customer preferences between the two proposed product lines. If

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MGMT601W3IP 4 the p-value is less than a chosen significance level (e.g., 0.05), we would reject the null hypothesis and conclude that there is a significant difference. (2) Decision-Making Process: The outcome of the chi-square test would significantly contribute to the decision-making process. If we find evidence of a significant difference, it suggests that one product line has a higher probability of success. This information will guide the board of directors in deciding which product line to prioritize for market expansion. On the other hand, if the test does not show a significant difference, it may rely on other factors such as cost, competition, or market demand. (3) Risk Mitigation: The chi-square test allows us to make data-driven decisions, reducing the risk of market expansion. It provides a quantitative basis for selecting the product line that aligns better with customer preferences, enhancing the chances of success in the new market. Conclusion In conclusion, while we may not have all the data necessary for a full chi-square analysis, the initial steps of this nonparametric text can provide substantial support for our decision-making process. It helps us assess the significance of differences in customer preferences and make an informed choice on whether to expand into a new market or retain the current position, benefiting our outdoor sporting goods client.

MGMT601W3IP 5 References Hayes, A. (2021, Mayla). Chi-Square (X2) Statistic Definition. Investopedia. https://investopedia.com/term/c/chi-square-statistic.asp . Bozeman Science. (2011, November 13). Chi-Squared-test [Video file]. Retrieved from https://www.youtube.com/watch?v=WXPBoFDqNVk Analytics Vidhya This is the official account of the Analytics Vidhya team. (2020, July 26). Nonparametric tests: Nonparametric statistical analysis. Retrieved June 04,2021, from https:// www.analyticsvidhya.com/blog/2017/11/a-guide-tp-conduct-analysis-using-non- parametric-tests/ American Marketing Association (n.d.). Summary reports. Retrieved from http://www.marketpower.com/content753.php

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