The ________ is the average of a data set calculated by adding all values and then dividing by the number of values.
- Mean
- Median
- Mode
- nan
The mean, also referred to as average or arithmetic mean, is calculated by adding all values in the data set and then dividing by the number of values. The mean is often used as a summary statistic.
The probability of committing a Type I error is also known as the ______ level of the test.
- Confidence
- Power
- Significance
- Size
The probability of committing a Type I error (rejecting a true null hypothesis) is known as the significance level (often denoted by alpha) of the test. A common significance level is 0.05, indicating a 5% risk of committing a Type I error if the null hypothesis is true.
The process of testing the effect of varying one predictor at different levels of another predictor is known as ________ effect analysis.
- Additive
- Independent
- Interaction
- Subtractive
This is known as interaction effect analysis. Interaction effect analysis involves testing how the effect of one predictor on the response variable changes at different levels of another predictor. It helps in understanding how different variables interact with each other to affect the dependent variable.
How does the Spearman rank correlation deal with categorical variables?
- It assigns a numerical value to each category
- It can't handle categorical variables
- It groups categorical variables together
- It transforms categorical variables into ranks
The Spearman rank correlation transforms categorical variables into ranks, which allows it to handle both continuous and ordinal (a type of categorical variable) data.
How does independence between events affect the calculation of their joint probability?
- It makes the joint probability equal to the difference of the probabilities of each event
- It makes the joint probability equal to the product of the probabilities of each event
- It makes the joint probability equal to the ratio of the probabilities of each event
- It makes the joint probability equal to the sum of the probabilities of each event
If events are independent, their joint probability equals the product of their individual probabilities. That is, P(A ∩ B) = P(A) * P(B) for independent events A and B.
How does the Spearman’s Rank Correlation test handle ties in data ranks?
- Assigns the maximum rank to ties
- Assigns the minimum rank to ties
- Averages the tied ranks
- Ignores the tied ranks
The Spearman’s Rank Correlation test handles ties in data ranks by averaging the ranks. For example, if two values tie for a place in the ranking, they are assigned a rank equal to the average of those places.
__________ in multiple linear regression refers to the proportion of the variance in the dependent variable that is predictable from the independent variables.
- Beta coefficient
- F-statistic
- R-squared
- T-statistic
R-squared is a statistical measure in regression models that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables.
The degrees of freedom for a Chi-square test for a contingency table with r rows and c columns is (r-1)*(c-1), otherwise known as ________ degrees of freedom.
- dependent
- independent
- joint
- multicollinearity
The degrees of freedom for a Chi-square test for a contingency table with r rows and c columns is calculated as (r-1)*(c-1). These are also known as independent degrees of freedom as they depend on the number of independent ways that the data can vary.
Can Pearson's Correlation Coefficient be used with non-linear relationships?
- No, never
- Yes, always
- Yes, but it may not provide meaningful results
- Yes, but only if the relationship is monotonic
While you can technically compute a Pearson correlation coefficient for non-linear relationships, it may not provide meaningful results. The Pearson correlation measures the degree of a linear relationship between variables, and does not fully capture the dynamics of a non-linear relationship. In such cases, Spearman's rank correlation or other non-parametric correlations may be more appropriate.
What is the purpose of a Z-test?
- To assess the relationship between categorical variables
- To calculate the correlation between two variables
- To compare sample and population means when the population standard deviation is known
- nan
A Z-test is used to compare the mean of a sample to the mean of a population when the population standard deviation is known. It's not used to calculate correlations or assess relationships between categorical variables.