Relationship Between Two Quantitative Variables

The correlation between two variables X and Y is –0.26. A new set of scores, X* and Y*, is constructed by letting X* = 2X and Y* = Y +12. The correlation between X* and Y* is

• -0.26

• .26

• -.52

• .52

• 0

What is an outlier

• attempts to explain the observed outcomes.

• measures an outcome of a study. independent variable

• value of Q3- Q1

• value that is very high or low from overall pattern.

Describe relationship of r=.99

• strong, negative linear relationship

• strong, positive linear relationship

• no linear relationship, scattered

• slightly linear negative relationship

What type of data is used in scatterplots?

• quantitative data

• qualitative data

What does the correlation "r" measure

• difference between highest and lowest values in the set

• spread of the graph of values

• direction and strength of relationship between two variables

• standard deviation

Given a set of ordered pairs (x, y) so that sx = 0.75, sy = 1.6, r = 0.55. What is the slope of the least‐square regression line for these data

• 1.82

• 1.17

• 2.18

• 0.26

• 0.78

Suppose the LSRL for predicting Weight (in pounds) from Height (in inches) is given by Weight = –115 + 3.6 (Height). Which of the following statements is correct?

I. A person who is 61 inches tall will weigh 104.6 pounds.

II. For each additional inch of Height, Weight will increase on average by 3.6 pounds.

III.There is a strong positive linear relationship between Height and Weight.

• I only

• II only

• III only

• II and III only

• I and II only

What is a response variable

• attempts to explain observed outcomes, independent variable, x

• measures strength and direction

• shows relationship between two quantitative variables

• measures outcome of a study, dependant variable, y

What is the coefficient of determination

• line that makes the sum of the squares of the vertical distances of data points from the line as small as possible.

• proportion of the variation in the values of y that is explained by the LSRL

• random uniform scatter of points above and below the LSRL residual

• observation that if it is removed it would drastically change the result of some calculation

What is the least square regression line

• The line that makes the sum of the squares of the vertical distances of the data points from the line as small as possible. minimizes total area in all of the squares.

• observed value(y) - predicted value (y"hat")

• random uniform scatter of points above and below the LSRL residual

• an observation that lies outside the overall pattern of the other observations.