chillywilly12a
2020-12-25
Answered

Explain what changes would be required so that you could analyze the hypothesis using a chi-square test. For instance, rather than looking at test scores as a range from 0 to 100, you could change the variable to low, medium, or high. What advantages and disadvantages do you see in using this approach? Which is the better option for this hypothesis, the parametric approach or nonparametric approach?

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StrycharzT

Answered 2020-12-26
Author has **102** answers

Step 1

The changes would be required so that you could analyze the hypothesis using a chi-square test are:

Step 2

In ANOVA, we have two or more group means that we have to compare.

In Chi square test, we have two categorical variables and want to determine whether one variable is related to the other variable.

Thus, for changing from ANOVA to chi square, rather than looking at test scores as a range from 0 to 100, we could change the variable to low, medium or high, so that it become categorical, thus amenable for chi square test.

Step 3

Advantages of chi-square test over ANOVA:

Chi-square test is robust with respect to the distribution of the data due to its non-parametric characteristic.

Disadvantages of chi-square test over ANOVA:

Chi-square test does not give much information about the strength of the relationship. It is sensitive to sample size. It is sensitive to to small expected frequencies in one or more cells in the$\chi}^{2$ table.

Step 4

Non-parametric test is better option for this hypothesis since data are not given as normally distributed and the non-parametric test which is distribution free is applicable

The changes would be required so that you could analyze the hypothesis using a chi-square test are:

Step 2

In ANOVA, we have two or more group means that we have to compare.

In Chi square test, we have two categorical variables and want to determine whether one variable is related to the other variable.

Thus, for changing from ANOVA to chi square, rather than looking at test scores as a range from 0 to 100, we could change the variable to low, medium or high, so that it become categorical, thus amenable for chi square test.

Step 3

Advantages of chi-square test over ANOVA:

Chi-square test is robust with respect to the distribution of the data due to its non-parametric characteristic.

Disadvantages of chi-square test over ANOVA:

Chi-square test does not give much information about the strength of the relationship. It is sensitive to sample size. It is sensitive to to small expected frequencies in one or more cells in the

Step 4

Non-parametric test is better option for this hypothesis since data are not given as normally distributed and the non-parametric test which is distribution free is applicable

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Which of the statements are correct?

1 .A condition for using the chi-square goodness-of-fit test is that all expected counts must be at least five.

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a. I only

b. II only

c. I and III only

d. II and III only

e. I, II, and III

1 .A condition for using the chi-square goodness-of-fit test is that all expected counts must be at least five.

2. The chi-square goodness-of-fit test uses 1 - n degrees of freedom.

3. A condition for using the chi-square goodness-of-fit test is the observations are based on a random sample.

a. I only

b. II only

c. I and III only

d. II and III only

e. I, II, and III

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Who knows?

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Who knows?

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