8.2 FA HW correlation and causation

What does a Pearson correlation coefficient of -1 indicate?

• No linear correlation

• Moderate negative linear correlation

• Perfectly negative linear correlation

• Perfectly positive linear correlation

Which value of Pearson correlation coefficient indicates a perfectly positive linear correlation?

• 0

• 1

• -1

• 0.5

What is considered a strong relationship between two variables in terms of Pearson correlation coefficient?

• r > 0.75

• 0.25 < r < 0.5

• r < 0.25

• r > 0.5

In which field might a correlation of r = 0.3 between taking a certain drug and the reduction in heart attacks be considered significant?

• Medical

• Technology

• Marketing

• Human Resources

What does a Pearson correlation coefficient of 0 indicate?

• Perfectly negative linear correlation

• No linear correlation

• Perfectly positive linear correlation

• Weak positive correlation

What type of correlation is indicated when the value of one variable increases as the value of another variable decreases?

• Strong positive correlation

• No correlation

• Weak positive correlation

• Strong negative correlation

Why is creating a scatterplot useful when studying the relationship between two variables?

• To measure the variables' variances

• To create a linear regression model

• To calculate the Pearson correlation coefficient

• To identify outliers impacting the correlation

What can cause the Pearson correlation coefficient to appear as if there is a strong relationship when there isn't?

• A perfectly linear relationship

• A high variance in one variable

• A large sample size

• One extreme outlier

What does a correlation coefficient of r = 0.00 between two variables X and Y indicate?

• A strong positive linear relationship

• No linear relationship

• A moderate relationship

• A strong negative linear relationship

How can a scatterplot help in understanding the relationship between two variables?

• By proving that no relationship exists

• By calculating the exact value of r

• By identifying nonlinear relationships

• By showing a linear relationship only