geoffreythelm said:Homework Statement
http://img687.imageshack.us/img687/7963/screenshot20120102at185.png
Homework Equations
The Attempt at a Solution
I understand how to compute a Chi-squared test, but I'm a bit confused about the wording of parts a) and c). Could someone possibly simplify it for me?
Thanks a lot.
geoffreythelm said:Homework Statement
http://img687.imageshack.us/img687/7963/screenshot20120102at185.png
Homework Equations
The Attempt at a Solution
I understand how to compute a Chi-squared test, but I'm a bit confused about the wording of parts a) and c). Could someone possibly simplify it for me?
Thanks a lot.
A null hypothesis is a statement that assumes there is no significant relationship between two variables or that there is no difference between two groups. It is typically denoted as H0 and is used as a starting point for statistical tests.
The purpose of a null hypothesis is to serve as a baseline or default assumption. It allows researchers to test whether there is enough evidence to reject the null hypothesis and accept an alternative hypothesis. This helps to determine whether the observed data is due to chance or if there is a significant relationship between variables.
A chi-squared test is a statistical test that compares the observed data to the expected data under the null hypothesis. It calculates the difference between the observed and expected values and determines whether this difference is significant. If the difference is significant, the null hypothesis is rejected in favor of an alternative hypothesis.
The assumptions of a chi-squared test include:
- The data must be in the form of frequencies or counts.
- The sample must be representative of the population.
- The expected frequency for each category must be at least 5.
- The observations must be independent.
- The variables being tested must be categorical or discrete.
If these assumptions are not met, the results of the chi-squared test may be unreliable.
The results of a chi-squared test are typically presented as a p-value. This p-value represents the probability of obtaining the observed data if the null hypothesis is true. If the p-value is less than the predetermined significance level (usually 0.05), the null hypothesis is rejected and the results are considered statistically significant. If the p-value is greater than the significance level, the null hypothesis cannot be rejected and the results are not considered statistically significant.