A dataset from 1987 contains information about credit card applications and approvals for 690 applicants. We are interested in looking at the amount of debt across industries, specifically between information technology, healthcare, real estate, and education. The following values are scaled down for ease of analysis. From the sample, 45 are in information technology with an average of $4.03 dollars in debt and a standard deviation of $3.88. 52 are in the healthcare industry with an average of $5.84 dollars in debt and a standard deviation of $6.58. 30 are in real estate with an average of $4.79 dollars in debt and a standard deviation of $5.43. 30 are in education with an average of $8.21 dollars in debt and a standard deviation of $6.25. Fill in the hypotheses below: Ho: All the means are equal. Ha: At least one mean is different from the others. Part 2 of 3 Complete the following ANOVA summary table (a = 0.01). Round your answers to three decimal places, if necessary, and round any interim calculations to four decimal places. What is the overall mean? Use this as a check before you begin your calculations. Source Between Within Total SS df MS F P-value

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**Credit Card Application Data Analysis - 1987**

A dataset from 1987 provides insights into credit card applications and approvals for 690 applicants. The focus is on analyzing debt amounts across various industries, specifically: information technology, healthcare, real estate, and education. Values are scaled for ease of analysis.

**Sample Data:**
- **Information Technology:** 45 applicants, average debt of $4.03, standard deviation $3.88.
- **Healthcare:** 52 applicants, average debt of $5.84, standard deviation $6.58.
- **Real Estate:** 30 applicants, average debt of $4.79, standard deviation $5.43.
- **Education:** 30 applicants, average debt of $8.21, standard deviation $6.25.

**Hypotheses:**
- \( H_0 \): All the means are equal.
- \( H_a \): At least one mean is different from the others.

**ANOVA Summary Table (α = 0.01):**
- Complete the table, rounding answers to three decimal places, and interim calculations to four decimal places.

**Overall Mean:**
- Calculate and use as a check before starting calculations.

**ANOVA Table Layout:**

| Source  | SS  | df  | MS  | F  | P-value |
|---------|-----|-----|-----|----|---------|
| Between |     |     |     |    |         |
| Within  |     |     |     |    |         |
| Total   |     |     |     |    |         |

Fill in calculations to compare the industry means and determine if there are significant differences at the 0.01 significance level.
Transcribed Image Text:**Credit Card Application Data Analysis - 1987** A dataset from 1987 provides insights into credit card applications and approvals for 690 applicants. The focus is on analyzing debt amounts across various industries, specifically: information technology, healthcare, real estate, and education. Values are scaled for ease of analysis. **Sample Data:** - **Information Technology:** 45 applicants, average debt of $4.03, standard deviation $3.88. - **Healthcare:** 52 applicants, average debt of $5.84, standard deviation $6.58. - **Real Estate:** 30 applicants, average debt of $4.79, standard deviation $5.43. - **Education:** 30 applicants, average debt of $8.21, standard deviation $6.25. **Hypotheses:** - \( H_0 \): All the means are equal. - \( H_a \): At least one mean is different from the others. **ANOVA Summary Table (α = 0.01):** - Complete the table, rounding answers to three decimal places, and interim calculations to four decimal places. **Overall Mean:** - Calculate and use as a check before starting calculations. **ANOVA Table Layout:** | Source | SS | df | MS | F | P-value | |---------|-----|-----|-----|----|---------| | Between | | | | | | | Within | | | | | | | Total | | | | | | Fill in calculations to compare the industry means and determine if there are significant differences at the 0.01 significance level.
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