Unit 3

Question 1

What does r tell us?

Answer 1

How STRAIGHT a positive or negative relationship is between TWO QUANTITATIVE variables

Question 2

What values can r be?

Answer 2

from -1 to 1

Question 3

Give some strong r values and some weak r values.

Answer 3

Strong r values are close to 1 or -1, like -0.83 or 0.94. Weak r values are close to zero like 0.10 or -0.06

Question 4

If something is correlated is it associated?

Answer 4

Yes (if it is straight)

Question 5

association or correlation?

Answer 5

association is talking about a relationship? correlation is an actual calculated number

Question 6

How to describe association on scatterplot?

Answer 6

DIRECTION… FORM.. STRENGTH (and outliers)

Question 7

direction?

Answer 7

positive or negative

Question 8

form?

Answer 8

Linear or nonlinear (straight, curved)

Question 9

strength?

Answer 9

give the r value (if straight), or say? “tightly packed” or “ loosely packed”

Question 10

does correlation mean causation?

Answer 10

NO WAY!

Question 11

Give example of incorrectly using the word “correlation”

Answer 11

“There is a correlation between gender and video game playing.” This person should say “association.” You can’t say correlation because gender is categorical.

Question 12

Does high r value mean anything?

Answer 12

An r value alone tells little. CHECK THE SCATTERPLOT… IS IT LINEAR?? make sure it’s linear first.

Question 13

How can you check for “straight enough?”

Answer 13

by looking at the residuals plot

Question 14

how do you interpret slope?

Answer 14

“For an increase of 1 [unit of x] there is an (increase/decrease) of [SLOPE] [units of y]” Plug in your slope and your units!!!

Question 15

how do you interpret y intercept?

Answer 15

“The model predicts that if there were no [x stuff] this is how much [y stuff] you’d have” Plug in the appropriate contexts.

Question 16

how to interpret slope EQUATION? rSy/Sx

Answer 16

For each increase of 1 standard deviation in x direction, you go r *(standard deviation) in y direction.

Question 17

if you multiply or divide the x’s or y’s (shift/scale) does r change?

Answer 17

No change. r does not have any units so the strength of the line will not change.

Question 18

If you switch x and y does r change?

Answer 18

No change, the strength will stay the same.

Question 19

if you switch x and y will slope change?

Answer 19

YES- slope is rsy/sx

Question 20

Can you predict an X by using a Y?

Answer 20

NOT WITH THE SAME EQUATION! BE CAREFUL!! You have to change the entire equation and start from scratch.

Question 21

What point does every regression line pass through?

Answer 21

the mean-mean point. (x bar, y bar)

Question 22

Why is it called “least squares regression line”?

Answer 22

After you find the mean-mean point, you fix the line so that it minimizes the squared vertical distance to that line (minimizes the squared residuals)

Question 23

interpret r squared

Answer 23

r squared is the % of variability in y that can be explained by the linear model.

Question 24

does high r squared mean a good model?

Answer 24

CHECK STRAIGHTNESS FIRST. you should check your plot and residuals to make sure model is appropriate and no outliers present? then it means something

Question 25

is r sensitive to outliers?

Answer 25

yes. A single outlier can make it seem like there is a relationship (out in x direction..), or that there is none.

Question 26

What is a residual?

Answer 26

ACTUAL minus PREDICTED. A-P

Question 27

What are b1 and bo ?

Answer 27

b1 is the SLOPE, and bo is the y- intercept.

Question 28

what is the LSRL

Answer 28

least squares regression line

Question 29

What should we look for in residual plot?

Answer 29

curve or pattern. Also, it should have equalish scatter from left to right

Question 30

Which is explanatory variable?

Answer 30

x. horizontal axis. it “explains” what happens to y

Question 31

which is response?

Answer 31

y.. Vertical axis.. It “responds” to the x

Question 32

will residual plots always show outliers? (will outliers always have large residuals?)

Answer 32

Not necessarily.. Some points have so much leverage, they pull the line up to it?

Question 33

How do you get equation from computer output?

Answer 33

y= b0 + b1 x or y=a+bx
y is the dependent variable
b0 is the coefficient of constant (or it says intercept)
b1 is the coefficient of the variable given (independent)
x is the independent variable

Question 34

What does “y-hat” mean?

Answer 34

The prediction equation for y.