If a null hypothesis is rejected, what can we infer about the alternative hypothesis?
- It has no relation to the null hypothesis
- It is likely to be true
- It is rejected as well
- It needs to be tested separately
If a null hypothesis is rejected, it means that the alternative hypothesis is likely to be true. We can infer that there's enough evidence in our data to support the claim of the alternative hypothesis.
What is heteroscedasticity in the context of residual analysis?
- It is the assumption that residuals have constant variance
- It is the condition where residuals have varying variance
- It is the linear relationship between residuals and the dependent variable
- It refers to the independence of residuals
Heteroscedasticity refers to a situation where the variance of the errors or the residuals is not constant across all levels of the independent variables. This violates one of the assumptions of linear regression and can result in inefficient estimates of the regression coefficients.
How do Type I and Type II errors relate to the power of a statistical test?
- Both decrease the power of a test
- Both increase the power of a test
- Type I errors decrease the power, Type II errors increase it
- Type I errors increase the power, Type II errors decrease it
The power of a test is the probability that it correctly rejects a false null hypothesis (true positive). It's the complement of a Type II error. As Type I error probability increases, power also increases because we're more willing to reject the null hypothesis. However, a Type II error decreases power because it's a missed opportunity to reject a false null hypothesis.
What happens to the correlation coefficient when you have outliers in your data?
- It decreases
- It increases
- It may become misleading
- It remains the same
Outliers can greatly affect the correlation coefficient, making it misleading. If outliers are in the same direction, they can inflate the correlation. If they are in opposite directions, they can deflate or even reverse the sign of the correlation. Hence, it's important to handle outliers before conducting correlation analysis.
What are the degrees of freedom in a Chi-square test for a 2x3 contingency table?
- 2
- 3
- 4
- 6
In a Chi-square test, the degrees of freedom for a 2x3 contingency table is (2-1) * (3-1) = 2.
The process that aims to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables is called _______.
- correlation analysis
- covariance analysis
- factor analysis
- regression analysis
The process that aims to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables is called factor analysis.
If the Kruskal-Wallis H test is significant, it is often followed up with ________ to find which groups differ.
- ANOVA
- correlation analysis
- post hoc tests
- t-tests
If the Kruskal-Wallis H test is significant, it is often followed up with post hoc tests to find which groups differ. These tests are used to make pairwise comparisons between groups.
What are the implications of autocorrelation in the residuals of a regression model?
- It causes bias in the parameter estimates
- It indicates that the model is overfit
- It suggests that the model is underfit
- It violates the assumption of independent residuals
Autocorrelation in the residuals of a regression model violates the assumption of independent residuals. This can lead to inefficient estimates and incorrect standard errors, leading to unreliable hypothesis tests and confidence intervals.
How does increasing the sample size affect the power of a statistical test?
- Decreases the power
- Does not affect the power
- Increases the power
- May either increase or decrease the power
Increasing the sample size generally increases the power of a statistical test. This is because a larger sample provides more information, making it more likely that the test will detect a true effect if one exists.
How do post-hoc tests in ANOVA assist in interpreting the results?
- They help to adjust the level of significance
- They help to calculate the F statistic
- They help to check the assumptions of the ANOVA
- They help to determine which specific group means are significantly different from each other
Post-hoc tests in ANOVA help to determine which specific group means are significantly different from each other, after a significant overall ANOVA result. They control the overall Type I error rate across multiple comparisons.