1)A product line is sold in 15 different configurations of packaging. How does t

Question

1)A product line is sold in 15 different configurations of packaging. How does the large number of package types influence the value of the chi-squared statistic?

2)Why are chi-squared statistics not directly comparable between tables of different dimensions when the null hypothesis of independence holds?

3)Could Cramer’s V have been used rather than p-value to standardize the results? Give an advantage and a disadvantage of p-values compared to Cramer’s V statistics.

4)Of the 650 products, 69 come in five types of packaging. If packaging type and location are independent, what should be the average value of these 69 chi-squared statistics?

5)Suppose managers evaluate the association between package type and location for 50 products for which these are independent attributes. The data in each table are independent of the data in other tables.

    a)How many of these 50 p-values would be expected to be less than 0.05?

    b)What is the probability that at least one p-value would be less than 0.01?

    c)If the smallest p-value is less than 0.01, should we conclude that package type and location for this product are associated?

6)The data used in the chi-squared analysis have 200 cases for each location. It is necessary to have the same number of observations from each location for every product?

7)Explain how the analysis of packaging types could be used to manage the mix of color or sizes of apparel in clothing stores that operate in different parts of the United States.

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Don't have the data go with these questions.

Thank you so much!!!

Explanation / Answer

1) As the value of chi-squared statistic increases, p-value decreases. There is a direct relationship between sample size and chi-squared statistic. Since chi-squared statistic =Summation of (observed value-Expected Value)^2/ Expected value. hence larger sample size, influence the chi-statistic (It increases)

2) when the null hypothesis of independence holds, there is no need of direct comparison between tables of different dimensions as the decision is generalized over the whole population.

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