(adapted from Johnson, 1980) Each semester, there are seven sections of a particular biology course, taught at various times by various instructors. The table below shows the number of students who selected each of the seven sections in Fall 2016. Do the data suggest that students have a preference for certain sections of the course? Perform a test of hypothesis using a = 0.05. In answering, be sure to address each of the five steps in a test of hypothesis. Number of students 1 18 2 12 3 25 Section 4 5 23 8 6 19 7 14

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**Title: Student Enrollment Preferences in Biology Course Sections**

**Introduction:**
This dataset, adapted from Johnson (1980), explores the preferences of students for different sections of a biology course offered in Fall 2016. Each semester, the course is divided into seven sections, each taught at varying times and by different instructors. The aim is to determine if there is a statistically significant preference for certain sections of the course.

**Dataset Overview:**
The table below summarizes the number of students enrolled in each of the seven sections:

| Section | 1  | 2  | 3  | 4  | 5  | 6  | 7  |
|---------|----|----|----|----|----|----|----|
| Number of Students | 18 | 12 | 25 | 23 | 8  | 19 | 14 |

**Hypothesis Testing:**
To determine if students have a preference for certain sections, perform a hypothesis test with a significance level of α = 0.05. Follow these five steps for hypothesis testing:

1. **State the Hypotheses:**
   - Null Hypothesis (H0): There is no preference among the different sections; the distribution of students across sections is uniform.
   - Alternative Hypothesis (H1): There is a preference for certain sections; the distribution is not uniform.

2. **Choose the Significance Level:**
   - Significance level (α) = 0.05

3. **Calculate the Test Statistic:**
   - Use a chi-square test for goodness of fit since we are comparing observed frequencies with expected frequencies.

4. **Determine the Critical Value/Decision Rule:**
   - Critical value from chi-square distribution table based on degrees of freedom (df = n - 1, where n = number of sections).

5. **Make the Decision:**
   - Compare the calculated chi-square statistic to the critical value. If the chi-square statistic exceeds the critical value, reject the null hypothesis.

Through this method, educators and researchers can assess whether specific sections of the course are more popular, which could inform scheduling and instructor assignment decisions in future semesters.
Transcribed Image Text:**Title: Student Enrollment Preferences in Biology Course Sections** **Introduction:** This dataset, adapted from Johnson (1980), explores the preferences of students for different sections of a biology course offered in Fall 2016. Each semester, the course is divided into seven sections, each taught at varying times and by different instructors. The aim is to determine if there is a statistically significant preference for certain sections of the course. **Dataset Overview:** The table below summarizes the number of students enrolled in each of the seven sections: | Section | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |---------|----|----|----|----|----|----|----| | Number of Students | 18 | 12 | 25 | 23 | 8 | 19 | 14 | **Hypothesis Testing:** To determine if students have a preference for certain sections, perform a hypothesis test with a significance level of α = 0.05. Follow these five steps for hypothesis testing: 1. **State the Hypotheses:** - Null Hypothesis (H0): There is no preference among the different sections; the distribution of students across sections is uniform. - Alternative Hypothesis (H1): There is a preference for certain sections; the distribution is not uniform. 2. **Choose the Significance Level:** - Significance level (α) = 0.05 3. **Calculate the Test Statistic:** - Use a chi-square test for goodness of fit since we are comparing observed frequencies with expected frequencies. 4. **Determine the Critical Value/Decision Rule:** - Critical value from chi-square distribution table based on degrees of freedom (df = n - 1, where n = number of sections). 5. **Make the Decision:** - Compare the calculated chi-square statistic to the critical value. If the chi-square statistic exceeds the critical value, reject the null hypothesis. Through this method, educators and researchers can assess whether specific sections of the course are more popular, which could inform scheduling and instructor assignment decisions in future semesters.
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