What is the main difference between causation and correlation?

• Causation implies a cause, while correlation implies a relationship.

• Causation and correlation are the same.

• Causation implies no relationship, while correlation implies a cause.

• Causation implies a relationship, while correlation implies a cause.

Which of the following is an example of a causal relationship?

• Ice cream sales and soup sales

• Number of banks and city population

• Absences and lower grades

• Eating sugar and gaining weight

What does a residual represent in a data set?

• The correlation between two variables

• The sum of all data points

• The average of all data points

• The difference between actual and predicted values

In the context of residuals, what does a zero residual indicate?

• The actual value equals the predicted value.

• The actual value is above the predicted value.

• The residual plot is U-shaped.

• The actual value is below the predicted value.

How can a residual plot indicate a good fit for a linear model?

• By showing a curved pattern

• By having a V-shaped pattern

• By having a U-shaped pattern

• By being evenly dispersed with no pattern

What does a U-shaped pattern in a residual plot suggest?

• The data is not linear

• The data is linear

• The residuals are evenly dispersed

• The model fits the data well

What is the purpose of a residual plot?

• To calculate the average of residuals

• To find the sum of residuals

• To assess the fit of a linear model

• To determine the correlation between variables

Which of the following statements is true about correlation and causation?

• Causation always implies correlation.

• Causation never implies correlation.

• Correlation never implies causation.

• Correlation always implies causation.

Which scenario is likely to have a causal relationship?

• Ice cream sales and soup sales

• Number of banks and city population

• Time spent exercising and calories burned

• Education expenses and annual salary

What does a positive correlation indicate?

• The variables are inversely related.

• As one variable increases, the other decreases.

• There is no relationship between the variables.

• As one variable increases, the other also increases.