Business Finance Homework Help

• Download the file Sample Data. 
• Prepare a chart similar to the one in the downloaded file to indicate whether the correlation between variables A and Bwere found to be positive, negative, or minimal. 
• Provide explanation and justification for your decisions.

In your own words, explain what it means if the correlation of 2 variables is positive, negative, or minimal (close to 0), and give an example of each.

• What do you deduce from the correlations? Explain if you believe these to be short or long-term objectives and outcomes. 
• What are the implications for Big D Incorporated regarding its client in the outdoor sporting goods? 
• What are the implications for the penetration into the indoor sporting goods market? 
• Also, how can you use the correlation tools to identify the variables in the research toward the expansion into the indoor sporting goods market?

Chi Square and Hypothesis Testing

Student’s Name

Institutional Affiliation

Chi-Square and Hypothesis Testing

The Chi-square distribution X2 is a mathematical expression derived from the normal distribution, X to recall effectiveness in decision-making under a null and an alternative hypothesis. It is based on the square variables hence X2, and is often used if data is grouped into contingency tables. The degree of freedom in the Chi-square distribution is based upon the number of categories less 1. This implies that with the addition of several squared items that have been standardized (average subtracted and distributed by S), the outcome has variables that could be classified, analyzed, and compared within a probability distribution table (Bozeman Science, 2011). Tallied data from the contingency distribution table is relative at enhancing the use of the Chi-square formula to define whether one cell in the table has a higher or a lower expected count or if either of the groups has a higher or a lower expected score relative to the observed tally.

In this case, we will compare two distributions to establish whether from the decision on the production, it is effective for the outdoor sporting goods client to not expand to the next market, H0, or retain the current position, H2, depending on average monthly buyers. For the 200 buyers in January, 85 voted not to expand to the next market, 90 voted to retain the current market,and the rest were unwilling to vote. In February, out of 300 buyers, 165 voted not to expand to the next market, while 130 voted to retain to the current market. Within the two months, all the buyers voted in a similar distribution; thus, the data from the number of voters over the null and the alternative hypothesis were identical, and it call be represented In the contingency table below;

Observed Values

Expected

Not to expand to the next market

Retain the Current Market

Not Expected Rep.

Other

Total

January

85

90

200X 0.5

100

200X 0.46

92

200 X 0.04

8

February

165

130

300 X 0.5

150

300 X 0.46

138

300 X 0.04

12

Total

250

220

250 (50%)

230 (46%)

500 then other is 0.4 %

By choosing to decide from the Chi-Square test with the degree of freedom, then the calculation is based on the test statistic and the p-values as illustrated below;

X2 = (85 – 100)2/ 100 + (100 – 92)2 / 92 + (15 – 8)2 / 8 + (165 – 150)2 / 150 + (130 – 138)2 / 138 + (5 – 12)2 12 = 15.1

TheP-value equals P (X2 > 15.1) = 0.0005; thus, the data shows that it is effective for the outdoor sporting goods agent to not expand to the next market since the information from the buyers’ perspectives highlights uncertainties relenting to the alternative hypothesis.The board of directors must utilize the demographic demands for the products rather than retaining the current position, thus their ability to establish a strategic market position.

The Chi-square test has helped test and analyze the customers’ sample variance, thus evaluating the confidence interval for the data presented between the two months. The test and comparison from the two theoretical frameworkss forJanuary and February have facilitated the decision-making over not to expand to the next market, probably due to an unfavorable business environment postulated by either climate, society, culture, or legal government. Using the Chi-square test to present the board of directors is relevant since it includes its robustness relative to the distribution of data and ease in computation from the contingency tables (Bozeman Science, 2011). The parametric assumptions cannot be met, thus flexibility in handling data from different buyers over the decision not to expand to the next market or retain the current position.