Understanding Correlation and Regression

What is the definition of correlation?

• Correlation indicates the cause of one variable affecting another.

• Correlation is a measure of the relationship between two variables.

• Correlation is the average of two variables.

• Correlation is a statistical method for predicting future outcomes.

What does Pearson’s correlation coefficient measure?

• The difference between two variables.

• The strength and direction of the linear relationship between two variables.

• The total number of observations in a dataset.

• The average value of two variables.

How is rank correlation different from Pearson’s correlation?

• Rank correlation is used for linear relationships, while Pearson's correlation is for non-linear relationships.

• Rank correlation is non-parametric and uses ranks, while Pearson's correlation is parametric and uses raw data.

• Pearson's correlation is non-parametric and uses ranks, while rank correlation uses raw data.

• Rank correlation measures the strength of a relationship, while Pearson's correlation measures the direction of a relationship.

Explain the difference between correlation and causation.

• Correlation indicates a direct cause-and-effect relationship.

• Correlation means two variables are identical.

• Causation implies that variables are unrelated.

• Correlation is a relationship between variables, while causation indicates one variable causes a change in another.

What is a scatter diagram used for?

• To calculate the average of a dataset.

• To show the relationship between two variables.

• To create a pie chart representation.

• To display data in a tabular format.

What does a positive correlation indicate?

• A positive correlation indicates no relationship between the variables.

• A positive correlation means one variable increases while the other decreases.

• A positive correlation shows that the variables are independent of each other.

• A positive correlation indicates that two variables move in the same direction.

What is the range of values for Pearson’s correlation coefficient?

• From 0 to 1

• From -2 to 2

• From -0.5 to 0.5

• The range of values for Pearson’s correlation coefficient is from -1 to 1.

What does a correlation coefficient of 0 imply?

• A strong nonlinear relationship between the variables.

• A perfect negative relationship between the variables.

• No linear relationship between the variables.

• A perfect positive relationship between the variables.

How do you interpret a negative correlation?

• A negative correlation means that two variables move in opposite directions.

• A negative correlation suggests that one variable causes the other to decrease.

• A negative correlation indicates that both variables increase together.

• A negative correlation means there is no relationship between the variables.

What is the principle of least squares in regression analysis?

• A process to eliminate outliers from the dataset.

• A technique to calculate the average of the data points.

• A method to maximize the sum of the squares of the residuals.

• The principle of least squares is a method to minimize the sum of the squares of the residuals in regression analysis.