Management Summary

Chi Square 1

Chi Square 5

Big D- Chi Square

Tenika Tassin

MGMT600-2202A-03

Dr. W. Cousar

Colorado Technical University

3/20/2022

As presented in the last report, Chicago is an optimal region to make one’s operations and hence the company must prepare a strategy to enter the market of Chicago. Therefore, our argument alternative argument will be to retain the current position as our main argument claims that Bigg D should expand their market.

Chi-square test

The “chi-square distribution, also referred as the chi-square or χ2-distribution with the Kth degree of freedom is the distribution of the sum of squares of k independent standard normal random variables. The distribution of chi-square is a special case of distribution of gamma and is among the most utilized distribution in probability in statistics” (Rolke, & Gongora, 2021). It is especially important while justifying the hypothesis and in development of confidence intervals.

The chi-square test can be utilized for several situations, namely:

· The constructs must be measured on a nominal scale or an ordinary scale

· The test of “ is suitable for groups with equal and unequal sample sizes, however certain non-parametric tests only handle groups with same sizes of the sample”.

· The information or statistics which needs to tested must violate the normality assumption.

The assumptions for testing the test of are as follows:

· The analyzes statistics in terms of “frequencies and counts, rather than percentages or other transformations”.

· The groups of the constructs which are being analyzed should be exclusive.

· Lastly, each substance might provide data to a single cell in the χ2

The hypothesis testing of

:

N0- “There is no significant difference between the outdoor sporting production and indoor sporting production frequencies”.

H1- “There is a significant difference between the outdoor sporting production and indoor sporting production frequencies”.

Table 1

test of

Indoor sporting of Goods	In-house Production
Outdoor sporting of Goods	completed
Low High
Per Capital Income

Though all the facts on the corporation's core capabilities cannot be located, the Analysis employs the crucial and necessary features for the improvement and growth of . In order to undertake research on Big D's business expansion. There is a requirement for data collection on the observed and predicted frequencies of components that are participating in the expansion. It considers the salary counts, which is the salary by type of earning, for this scenario. The observed counts are for the United States, whereas the predicted figures are for Chicago in table 2.

Table 2

Earnings in the Two Countries

The test may be obtained in Excel by using the function Chi-square test. Through the Chi-square test, one can obtain actual range along with the anticipated range. If the p, which is lesser than 0.05, then there will be strong reason for accepting the original hypothesis i.e., H1. Thus, it can be stated that “there is a significant difference in the observed and expected counts of income in the Chicago(expected) and the US (observed)”.

Through chi-square one can get clear understanding on the variable as it denotes the possible factors which can influence the product purchase decision of consumers along with other outcomes. Based on above analysis, board of directors can get an idea how earnings impact the purchasing decision in consumers. Since the null hypothesis is rejected, it can be stated that “there is significant difference between the outdoor sporting production and indoor sporting production frequencies and thus it will be profitable for Big D to expand their market in Chicago”. The chi-square also aids in decision making process as it determines if there is association between the two categorical constructs. Henceforth, the process of decision making becomes easy once the relationship is known about the variables. For instance, after running chi-square test, it is easier for Big D company to make decision of expanding market in Chicago.

References

Bozeman, S. (2011). Chi-squared test[Video file]. Retrieved from https://www.youtube.com/watch?v=WXPBoFDqNVk

Rolke, W., & Gongora, C. G. (2021). A chi-square goodness-of-fit test for continuous distributions against a known alternative. Computational Statistics, 36(3), 1885-1900.