Using the table provided in class, indicate the critical value of "f" for a 95% confidence interval and 27 df (round to the nearest hundredth)
Using the table provided in class, indicate the critical value of "f" for a 95% confidence interval and 27 df (round to the nearest hundredth)
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Transcribed Image Text:This image contains a statistical table likely used for determining critical values of the t-distribution. It helps in hypothesis testing, providing the critical t-values based on different probabilities and degrees of freedom.
### Description of Table Elements:
- **Probability Levels (Columns):**
The columns indicate the probability levels: 0.6, 0.7, 0.8, 0.9, 0.95, 0.975, 0.99, and 0.995. These probabilities represent the likelihood that a larger value of t would be observed in a two-tailed t-test.
- **Degrees of Freedom (df - Rows):**
The rows display the degrees of freedom. The degrees of freedom range from 1 to 40.
### Explanation:
- **Degrees of Freedom (df):**
This is a parameter that often depends on the sample size. In the context of a t-test, the degrees of freedom are usually calculated as the total number of observations minus the number of parameters being estimated (such as the mean).
- **Critical t-values:**
Each cell in the table provides a critical t-value that corresponds to a specific degree of freedom and probability level. For example, at a 0.95 probability level with 10 degrees of freedom, the critical t-value is 2.228.
- **Use of Table:**
This table is primarily used by statisticians to determine whether to reject a null hypothesis during testing. If the calculated t-statistic from sample data is greater than the critical t-value found in this table, the null hypothesis can typically be rejected at the chosen significance level.
This type of table is an essential tool in statistical analysis, particularly for t-tests, where it allows for determination of significance in hypothesis testing.
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