Correlation and Predictions of Linear Functions

A method used to find the line of best fit.

• Association

• Causation

• Line of Best Fit

• Linear Regression

The measure of the strength and direction of a linear relationship

• Interpolation

• Extrapolation

• Correlations Coefficient

• Residual

Kirsten planted a number of flowers in her garden. The data in the scatter plot displays the heights of random flowers over twelve weeks of growth. Which function approximates the height of the flowers after x weeks?

• There is a positive linear relationship between x and y.

• There is a negative linear relationship between x and y.

• There is no linear relationship between x and y.

• There is an exponential relationship between x and y.

When graphing Natalie’s distance on the x-axis and time on the y-axis, what is the best fit for her recorded data? (You may have to round).

• 8.003x-0.36

• 27.5x + 5

• 27.5x + 10

• 7.96x-0.36

The graph below models the estimated value of a car, x years after 2000. Which value is the closest to the initial value of the car?

• $10,000

• $16,000

• $12,000

• $20,000

• The candle’s height increases by 0.75 cm each minute.

• The candle’s height decreases by 0.75 cm each minute.

• The candle’s height increases by 3.0 cm each minute.

• The candle’s height decreases by 30.0 cm each minute.

• 47 people

• 88 people

• 95 people

• 84 people

A linear model is used to represent the number of goals scored against a soccer team using the height of the goalkeeper. The correlation coefficient for this model is -0.21. Which statement about this linear model is true?

• The correlation coefficient represents a strong positive relationship.

• The correlation coefficient represents a weak negative relationship.

• The correlation coefficient represents a weak positive relationship.

• The correlation coefficient represents a strong negative relationship.

Consider the data table pictured. Based on the correlation coefficient for this data, which statement is true? (Make a table, y1~, look at the r-value. Is it positive or negative? Is it close to 1 (strong) or 0 (weak)?)

• The data displays a strong negative linear association.

• The data displays a weak negative linear association.

• The data displays a weak positive linear association.

• The data displays a strong positive linear association.