calculate the expected number of survey respondents in each cell of the table above.  Assuming independence between the two variables, how many 18-34 year olds would we expect to prefer USPS?

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Part of performing a hypothesis test to determine the independence between shipping carrier preference and age is to calculate the expected number of survey respondents in each cell of the table above.  Assuming independence between the two variables, how many 18-34 year olds would we expect to prefer USPS?

### Survey on Preferred Shipping Carriers for Holiday Gifts

A local news survey was conducted with a sample size of 500 individuals to discover which shipping carrier people prefer for shipping holiday gifts. The survey results were categorized by age groups, and the distribution of responses is depicted in the table below.

#### Distribution of Responses by Age Group

| **Carrier**       | **18-34** | **35-54** | **55+** |
|-------------------|-----------|-----------|---------|
| **USPS**          | 72        | 97        | 76      |
| **UPS**           | 52        | 76        | 34      |
| **FedEx**         | 31        | 24        | 9       |
| **Something Else**| 7         | 6         | 3       |
| **Not Sure**      | 3         | 6         | 4       |

### Instructions

Using the above data, you are required to perform a chi-square hypothesis test for independence. This will help to determine whether the preference for shipping carriers is independent of age group among the survey respondents.

#### Steps to Perform Chi-square Test for Independence:

1. **State the Hypotheses:**
   - Null Hypothesis (\(H_0\)): The preference for shipping carriers is independent of age.
   - Alternative Hypothesis (\(H_a\)): The preference for shipping carriers is not independent of age.

2. **Construct the Contingency Table:** 
   - Use the provided data to construct a contingency table for the chi-square calculation.

3. **Calculate the Expected Frequencies:**
   - Use the formula \( E = \frac{(Row \ Total \times Column \ Total)}{Grand \ Total} \).

4. **Compute the Chi-square Statistic:**
   - Apply the formula \( \chi^2 = \sum \frac{(O - E)^2}{E} \), where \(O\) represents the observed frequency and \(E\) represents the expected frequency.

5. **Determine the Degrees of Freedom:**
   - Degrees of Freedom (df) = (Number of rows - 1) * (Number of columns - 1).

6. **Compare with the Critical Value:**
   - Compare the calculated chi-square statistic with the critical value from the chi-square distribution table for the determined degrees of freedom and significance level (e.g., \( \alpha
Transcribed Image Text:### Survey on Preferred Shipping Carriers for Holiday Gifts A local news survey was conducted with a sample size of 500 individuals to discover which shipping carrier people prefer for shipping holiday gifts. The survey results were categorized by age groups, and the distribution of responses is depicted in the table below. #### Distribution of Responses by Age Group | **Carrier** | **18-34** | **35-54** | **55+** | |-------------------|-----------|-----------|---------| | **USPS** | 72 | 97 | 76 | | **UPS** | 52 | 76 | 34 | | **FedEx** | 31 | 24 | 9 | | **Something Else**| 7 | 6 | 3 | | **Not Sure** | 3 | 6 | 4 | ### Instructions Using the above data, you are required to perform a chi-square hypothesis test for independence. This will help to determine whether the preference for shipping carriers is independent of age group among the survey respondents. #### Steps to Perform Chi-square Test for Independence: 1. **State the Hypotheses:** - Null Hypothesis (\(H_0\)): The preference for shipping carriers is independent of age. - Alternative Hypothesis (\(H_a\)): The preference for shipping carriers is not independent of age. 2. **Construct the Contingency Table:** - Use the provided data to construct a contingency table for the chi-square calculation. 3. **Calculate the Expected Frequencies:** - Use the formula \( E = \frac{(Row \ Total \times Column \ Total)}{Grand \ Total} \). 4. **Compute the Chi-square Statistic:** - Apply the formula \( \chi^2 = \sum \frac{(O - E)^2}{E} \), where \(O\) represents the observed frequency and \(E\) represents the expected frequency. 5. **Determine the Degrees of Freedom:** - Degrees of Freedom (df) = (Number of rows - 1) * (Number of columns - 1). 6. **Compare with the Critical Value:** - Compare the calculated chi-square statistic with the critical value from the chi-square distribution table for the determined degrees of freedom and significance level (e.g., \( \alpha
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