What implications does an insignificant F-test have in the context of multiple linear regression?
- The model does not explain a significant amount of the variance in the response
- The model explains a significant amount of the variance in the response
- The model has a high R-squared value
- The model has violated the assumption of homoscedasticity
The F-test in multiple linear regression tests the null hypothesis that all regression coefficients are equal to zero. An insignificant F-test suggests that the predictors do not explain a significant amount of the variance in the response variable.
Multicollinearity refers to a situation where two or more _______ are highly linearly related.
- constants
- predictors
- residuals
- responses
Multicollinearity occurs when two or more predictor variables in a multiple regression are highly correlated with each other.
How does the confidence level of an interval influence the width of that interval?
- Higher confidence level leads to a narrower interval
- Higher confidence level leads to a wider interval
- Higher confidence level makes the interval skewed
- It does not influence the width
The higher the confidence level, the wider the interval. This is because to be more confident that we've captured the true population parameter, we need to provide a wider range of possible values.
In the context of Bayes' theorem, the probability of the data given a specific event is called the ________.
- joint
- likelihood
- marginal
- prior
The likelihood is the probability of the data given a specific event. It is part of Bayes' theorem, which is used to update the probability of a hypothesis based on new data.
What does a positive Spearman's rank correlation coefficient mean?
- One variable increases as the other decreases
- One variable is twice as large as the other
- Variables decrease together
- Variables increase together
A positive Spearman's rank correlation coefficient indicates that as one variable increases, the other also increases. This suggests a positive association between the variables.