DECK 4 UNIT 2 REGRESSION STUFF

Question 1

association or correlation?

Answer 1

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

Question 2

How to describe association? scatterplot

Answer 2

DIRECTION, FORM, STRENGTH (and strange stuff)

Question 3

direction?

Answer 3

positive or negative

Question 4

form?

Answer 4

straight, curved or zig zag

Question 5

strength?

Answer 5

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

Question 6

does correlation mean causation?

Answer 6

NO WAY DUUUUUDE

Question 7

does high r squared mean a good model?

Answer 7

not alone, you should check your plot and residuals to make sure model is appropriate and no outliers present? then it means something

Question 8

does high r value mean anything?

Answer 8

NOT IF IT ISN’T liNEARand there aren’t outliers.. LOOK AT THE DATA.. THEN IT MEANS AN AWFUL LOT»»>

Question 9

How is r calculated?

Answer 9

r= sum(ZxZy) / (n-1)—- kind of like the average sized rectangle on the standardized axes

Question 10

how can you check for “straight enough?”

Answer 10

residuals plot fool!

Question 11

how do you interpret slope?

Answer 11

ON AVERAGE, for an increas of 1 [unit of x] there is an (increase/decrease) of [SLOPE] [units of y]

Question 12

how do you interpret y intercept?

Answer 12

if there were no [x stuff] the model predicts you’d have this much [y stuff]. USE UNITS FROM CONTEXT.

Question 13

how to interpret slope EQUATION?

Answer 13

rsy/1sx means that for each increase of 1 st dev in x direction, you go r st dev in y direction.SO, think “ r Sy for each 1Sx”

Question 14

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

Answer 14

no. the strength remains the same. if you mult or div by negative the sign will change but it will still have same strength.(If you log or square it, it will change, but just adding or multiplying won’t change it)

Question 15

if you switch x and y does r change?

Answer 15

NO. The strength stays the same.

Question 16

if you switch x and y will slope change?

Answer 16

YES- slope is rsy/sx. to get new slope you do: (r sqared)/old slope

Question 17

interpret r squared

Answer 17

r squared % of variability in y can be explained by the model (with x stuff). The rest is in residuals.

Question 18

is r sensitive to outliers?

Answer 18

yes. A single outlier can make it seem like there is a relationship (out in x direction..)

Question 19

Look for lurking variables?

Answer 19

think hot chocolate sales at ski mountain and ski accidents? strong positive relationship, but why are they??? (lurking weather)

Question 20

outliers in regression?

Answer 20

doesn’t follow the “flow”

Question 21

what about your calculator for using curves to fit curved data?

Answer 21

Quadreg, cubicreg, lnreg, etc. just be careful when substituting while writing the model’s equation.

Question 22

what does “regression to the mean” mean?

Answer 22

predictions for y are closer to the mean y (y bar) than the actual x is to the mean x (in s.d)..

Question 23

what does influential mean?

Answer 23

It means that the point, when added or removed to data, will influence the SLOPE.. Generally these are outliers in the x direction?. Far left or right.

Question 24

what is a linear model?

Answer 24

it is an equation you can use, or a line of a graph, but it is just a model that says what kind of happens, and can be used to ESTIMATE WHAT MIGHT HAPPEN

Question 25

what is a residual?

Answer 25

ACTUAL-PREDICTED. A-P. like this class.. AP (get it?)

Question 26

what is b1 and bo ?

Answer 26

b1 is the SLOPE, and bo is the intercept. Remember that bo can be thought of as “b old” it is the old b. With y=mx+b, b was intercept…. b old.

Question 27

what is leverage?

Answer 27

leverage just means it is far away from x-bar. far right or left. Some leverage points are not influential if they go along with the flow of the scatter.

Question 28

what is the line that you plot?

Answer 28

IT IS A MODEL!

Question 29

Will the LSRL go through a lot of points?

Answer 29

Usually it hits no points.

Question 30

what is the LSRL

Answer 30

the “least squares regression line”

Question 31

what should we look for in resid plot?

Answer 31

We want random looking residuals so we look for curve or pattern.. Also, it should have equalish scatter from left to right. IF curved or unequal scatter, then linear model not good.

Question 32

what’s up with extrapolation?

Answer 32

not a good idea?? sometimes it’s all you can do, but still, NOT GOOD.

Question 33

what is extrapolation?

Answer 33

when you use a linear model to make predictions outside the range of gathered data. (predictions for x values smaller or larger than the x values you collected. way right or way left beyond scatterplot)

Question 34

which is explanatory variable?

Answer 34

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

Question 35

which is response?

Answer 35

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

Question 36

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

Answer 36

No? sometimes the outlier has so much leverage and is so influential that is pulls the LSRL right up to it, so it looks like a small residual.

Question 37

How to handle outliers?

Answer 37

Sometimes you can talk about it and run regression with and without the outlier present. Just have a valid reason why you are doing this? Sometimes an outlier is simply a typo.. Or so strange it sways what “typically” happens? Since your model is supposed to represent what sort of happens, tossing it is ok sometimes as long as you discuss it.

Question 38

“there is a correlation between SAT scores and gender” why is this wrong?

Answer 38

Correlation is a number calculated from 2 quantitative variables… Gender is categorical. It should say “association”

Question 39

If you add stuff to x or y, or switch the units from cm to inches, will that change r?

Answer 39

No, shifting and scaling won’t change r. It is a standardized measurement. If you measure height in inches and your European buddy used cm.. you’ll still have the same r value.

Question 40

If r = 0.8, The prediction for an x value 2 standard deviations above the x mean will be ___ standard deviations above the y mean.

Answer 40

1.6 Think… every 1 sd in x direction give prediction that is r sd in y direction. In this case, 1 sd in x means 0.8 in y direction.. so 2 sd above mean x will give a predicted y that is 0.8+0.8= 1.6 sd above mean y in y direction

Question 41

What is homoscedasticity?

Answer 41

Equal scatter about a regression line. A good thing.