Chapter 11 assignment PDF

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Alyssa Adler Dr. Budeva MTKG 330-02 25 October 2020

Part I: •

Type I error- definition • The chance of rejecting the null hypothesis when it is correct. (McLeod 2019) included this diagram from his article, What are Type I and Typle II Errors?:

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Type II error- definition • The chance of failing to reject the null hypothesis when it is wrong. Similarities between Chi-square, t-test, and ANOVA (compare in pairs): • Chi-square and t-test: • Chi-square and t-test are both used to compare relationships. • Chi-square and ANOVA: • Both chi-square and ANOVA are used to compare three or more froups. • T-test and ANOVA: • T-test and ANOVA both use an interval/ratio scale to compare groups. They can calculate and compare averages. Differences between Chi-square, t-test, and ANOVA (compare in pairs): • Chi-square and t-test: • A t-test tests a null hypothesis regarding two group’s means. It tests if they are equal. A chi-square test tests the relationship between any amount of variables. Chi-square uses a nominal scale to calculate and compare frequencies, while t-test uses an interval/ratio scale to calculate and compare averages. When you reject the null hypothesis with a t-test, the means are statistically different and when you reject the null hypothesis with a chi-square, it means there is no relationship between two variables. • Chi-square and ANOVA:

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Chi square uses a nominal scale, while ANOVA uses a interval/ratio scale. ANOVA is used to compare three or more variables and chi-square compares any number of groups. T-test and ANOVA: • The t-test is used to compare only two groups, while ANOVA is used to compare three or more groups. The t-test tried to determine if the difference between the two sample means occurred by chance, while ANOVA determines if two means are statistically different from each other.

Part II:

1.1. We want to compare males and females (gender) based on the difference in time spent on social media (measured as hours per day) 1.1.1. In this case we would use the t-test because we are comparing only two groups using an interval/ratio scale.

1.2. We want to compare our customers and the customers of our closest competitors based on their brand loyalty (measured as a Likert scale from 1 to 5) 1.2.1. In this case we would use the ANOVA test because we are comparing more than three groups on an interval/ratio scale.

1.3. We want to compare resident and commuter students based on differences in their gender (males and females) 1.3.1. In this case we would use the chi-square test because we are comparing groups based on a nominal scale.

1.4. We want to compare freshmen, sophomores, juniors, and seniors based on differences in time spent studying (hours per day) 1.4.1. In this case we would use the ANOVA test because we are comparing four groups on an interval/ratio scale.

1.5. We want to compare freshmen, sophomores, juniors, and seniors based on their willingness to recommend their school (simply measured as Yes/No).

1.5.1. In this case we would use the chi-square test because we are comparing groups based on a nominal scale.

1.6. We want to compare males and females based on their consumption of sugary drinks (ounces per day).

1.6.1. In this case we would use the t-test because we are comparing only two groups using an interval/ratio scale.

Part III:

Use the information summarized in Table 1 to calculate all possible descriptive statistics and explain their meaning. Show your work.

Table 1: Results from a survey measuring two variables: 1) Attitude Toward Nike; 2) Have you bought any Nike product in the last year

Respondent

Attitudes (Measured from 1 to 9, with 1 being very unfavorable and 9 being very favorable)

Have you bought any Nike product in the last year (with 1 being yes and 2 being no)

1

5

1

2

8

1

3

5

1

4

4

2

5

1

2

6

9

1

7

2

2

According to the above descriptive statistics, those that have bought a Nike product within the last year had an average attitude of 6.75, while those who have not had an average attitude of 2.33. It can be

concluded that whether or not someone has purchased a Nike product in the last year correlates to their attitude rating.

Part IV •

If you were interested in finding out whether or not young adults (21 - 34 years old) are more likely to buy products online than older adults (35 or more years old), how would you phrase your null hypothesis? What is the implicit alternative hypothesis accompanying your null hypothesis? • Ho: Young adults do not buy products online any more or less than older adults. • Ha: Young adults buy more products online than older adults.

References:

Mcleod, S. (2019, July 04). What are Type I and Type II Errors? Retrieved October 26, 2020, from https://www.simplypsychology.org/type_I_and_type_II_errors.html...