AP Stats Unit 2 MCQ Practice

Front

What does a strong positive correlation indicate in a scatterplot?

Back

A strong positive correlation indicates that as one variable increases, the other variable also tends to increase.

Front

What is the purpose of a residual plot?

Back

A residual plot is used to assess the fit of a regression model by showing the residuals on the vertical axis and the independent variable on the horizontal axis. A random pattern indicates a good fit.

Front

What does the r^2 value represent in regression analysis?

Back

The r^2 value represents the proportion of variance in the dependent variable that can be explained by the independent variable. A value close to 100% indicates a strong relationship.

Front

What is the least-squares regression line?

Back

The least-squares regression line is a straight line that best fits the data points in a scatterplot, minimizing the sum of the squares of the vertical distances of the points from the line.

Front

How do you calculate the residual for a data point?

Back

The residual is calculated as the observed value minus the predicted value from the regression line.

Front

What does a negative correlation indicate?

Back

A negative correlation indicates that as one variable increases, the other variable tends to decrease.

Front

What is the formula for the least-squares regression line?

Back

The formula for the least-squares regression line is ŷ = b0 + b1x, where b0 is the y-intercept and b1 is the slope.

Front

What is the significance of a correlation coefficient of 0.75?

Back

A correlation coefficient of 0.75 indicates a strong positive linear relationship between the two variables.

Front

What does it mean if a regression line explains 90% of the variation in the dependent variable?

Back

It means that 90% of the variability in the dependent variable can be explained by the independent variable in the regression model.

Front

What is the relationship between correlation and causation?

Back

Correlation does not imply causation; just because two variables are correlated does not mean that one causes the other.