9. The accuracy of a census report on a city is southern California was questioned by some government officials. A random sample of 1215 people living in the city was used to check the report and the results are shown here: Ethnic Origin Census Percent Sample Result African-American 10% 127 Asian 3% 40 480 Anglo Spanish American 38% 41% 502 Native American 6% 56 All other 2% 10 Using a 1% level of significance, test the claim that census distribution and sample distribution agree. (Goodness-of-fit)
9. The accuracy of a census report on a city is southern California was questioned by some government officials. A random sample of 1215 people living in the city was used to check the report and the results are shown here: Ethnic Origin Census Percent Sample Result African-American 10% 127 Asian 3% 40 480 Anglo Spanish American 38% 41% 502 Native American 6% 56 All other 2% 10 Using a 1% level of significance, test the claim that census distribution and sample distribution agree. (Goodness-of-fit)
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![### Analysis of Census Accuracy via Goodness-of-Fit Test
This educational discussion examines the accuracy of a census report for a city in Southern California. The accuracy was questioned by some government officials. A random sample of 1,215 people living in the city was used to check the report, and the results are presented in the following table:
#### Census Data Comparison Table
| Ethnic Origin | Census Percent | Sample Result |
|------------------------|----------------|---------------|
| African-American | 10% | 127 |
| Asian | 4% | 40 |
| Anglo | 38% | 480 |
| Spanish American | 41% | 502 |
| Native American | 6% | 56 |
| All other | 2% | 10 |
### Statistical Test: Goodness-of-Fit
We will use a Goodness-of-Fit test at a 1% level of significance to test the claim that the census distribution and the sample distribution agree.
**Hypothesis:**
- Null hypothesis (H0): The observed sample distribution matches the census distribution.
- Alternative hypothesis (H1): The observed sample distribution does not match the census distribution.
The Goodness-of-Fit test applies the Chi-square statistics to compare the observed frequencies (sample results) with the expected frequencies (based on the census percentages).
To proceed with this test, follow these steps:
1. Calculate the expected frequencies for each category based on the census percentages and the total sample size.
2. Use these expected frequencies along with the observed frequencies to calculate the Chi-square value.
3. Compare the calculated Chi-square value with the critical value from the Chi-square distribution table at the 1% significance level for the appropriate degrees of freedom.
Interpreting this will tell us whether to reject or fail to reject the null hypothesis, thus determining if there is a significant difference between the census report and the sample distribution.
### Conclusion
By analyzing the data and performing the Chi-square Goodness-of-Fit test at the specified significance level, we can draw conclusions about the accuracy of the census report for the city's population distribution. This will help in validating the census data or identifying potential discrepancies that may need further investigation.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F429495fa-514f-4d6e-b279-1e2d20094f9e%2Fd0b67743-2048-462f-9c85-baa5a0030620%2F9uabobt_processed.jpeg&w=3840&q=75)
Transcribed Image Text:### Analysis of Census Accuracy via Goodness-of-Fit Test
This educational discussion examines the accuracy of a census report for a city in Southern California. The accuracy was questioned by some government officials. A random sample of 1,215 people living in the city was used to check the report, and the results are presented in the following table:
#### Census Data Comparison Table
| Ethnic Origin | Census Percent | Sample Result |
|------------------------|----------------|---------------|
| African-American | 10% | 127 |
| Asian | 4% | 40 |
| Anglo | 38% | 480 |
| Spanish American | 41% | 502 |
| Native American | 6% | 56 |
| All other | 2% | 10 |
### Statistical Test: Goodness-of-Fit
We will use a Goodness-of-Fit test at a 1% level of significance to test the claim that the census distribution and the sample distribution agree.
**Hypothesis:**
- Null hypothesis (H0): The observed sample distribution matches the census distribution.
- Alternative hypothesis (H1): The observed sample distribution does not match the census distribution.
The Goodness-of-Fit test applies the Chi-square statistics to compare the observed frequencies (sample results) with the expected frequencies (based on the census percentages).
To proceed with this test, follow these steps:
1. Calculate the expected frequencies for each category based on the census percentages and the total sample size.
2. Use these expected frequencies along with the observed frequencies to calculate the Chi-square value.
3. Compare the calculated Chi-square value with the critical value from the Chi-square distribution table at the 1% significance level for the appropriate degrees of freedom.
Interpreting this will tell us whether to reject or fail to reject the null hypothesis, thus determining if there is a significant difference between the census report and the sample distribution.
### Conclusion
By analyzing the data and performing the Chi-square Goodness-of-Fit test at the specified significance level, we can draw conclusions about the accuracy of the census report for the city's population distribution. This will help in validating the census data or identifying potential discrepancies that may need further investigation.
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