What is the difference between correlation and causation?
- Causation implies correlation
- Correlation and causation are independent of each other
- Correlation implies causation
- Correlation means there is no causation
While correlation simply implies a relationship between two variables, causation goes a step further to explain that one variable actually causes the other to change. It's important to remember that correlation does not imply causation. However, if there is causation, there's likely to be correlation.
The correlation coefficient is denoted by the letter __.
- C
- P
- R
- S
The correlation coefficient is often denoted by the letter 'R'. In the case of Pearson's correlation, it's specifically denoted as 'r'. It measures the degree of relationship between two variables.
________ data is data that can be organized or ranked in a specific order.
- Continuous
- Discrete
- Nominal
- Ordinal
Ordinal data is a type of categorical data that can be organized or ranked in a specific order. For example, customer satisfaction ratings (satisfied, neutral, dissatisfied) can be organized from most to least satisfied.
How do you interpret the coefficients of interaction terms in a regression model?
- The interaction coefficient indicates the effect of one variable at a specific level of the other variable
- The interaction coefficient indicates the joint effect of the variables, independent of their individual effects
- The interaction coefficient is a measure of the correlation between the variables
- The interaction coefficient represents the average effect of two variables
The interaction coefficient in a regression model indicates the effect of one independent variable on the dependent variable for a specific level of another independent variable. It signifies that the effect of one variable depends on the value of another variable, thus capturing the interaction effect between the two variables.
What is the measure of central tendency that divides a data set into two equal halves?
- Mean
- Median
- Mode
- Range
The median is the measure of central tendency that divides a data set into two equal halves. When the observations are ordered from smallest to largest, the median is the middle value, ensuring that 50% of the data falls below and 50% above the median value.
The larger the number of observations, the closer the sample mean will be to the population mean, according to the _________.
- Central Limit Theorem
- Law of Large Numbers
- Probability Rule
- Sampling Distribution
According to the Law of Large Numbers, the larger the number of observations, the closer the sample mean will be to the population mean. This law is a fundamental principle of probability and statistics that states that as the size of a sample is increased, the estimate of certain parameters obtained from the sample will tend to approach the true value for the population.
What happens if the Kruskal-Wallis Test results in a statistically significant H value?
- It means nothing
- It means the groups are different
- It means the groups are the same
- It means the test failed
A statistically significant H value in the Kruskal-Wallis Test suggests that at least one of the sample distributions is different from the others.
How is the Chi-square distribution related to the normal distribution?
- The Chi-square distribution is a special case of the normal distribution
- The Chi-square distribution is the distribution of the square of a standard normal random variable
- The Chi-square distribution is the distribution of the sum of two standard normal random variables
- The normal distribution is a special case of the Chi-square distribution
The Chi-square distribution is related to the normal distribution in that it is the distribution of the square of a standard normal random variable.
Quantitative data can be broken down into two types: ________ and ________.
- Continuous, Categorical
- Discrete, Continuous
- Nominal, Ordinal
- Ratio, Interval
Quantitative data can be broken down into two types: Discrete and Continuous. Discrete data can only take specific values (like whole numbers) while Continuous data can take any value (within a range).
What is the purpose of a Chi-square test for independence?
- To compare the means of two groups
- To compare the variance of two groups
- To test for a relationship between two categorical variables
- To test the difference between an observed distribution and a theoretical distribution
The Chi-square test for independence is used to test for a relationship or association between two categorical variables.