# Unit 2 AP Stats Review

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## Description and Tags

### 24 Terms

1

bivariate

two variables

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2

what are we interested in when exploring bivariate data

whether not/how changes in one variable can allow us to predict changes in the other variable

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3

scatterplot

2-dimensional graph of ordered pairs

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4

what do we say about positively associated variables

higher than average values of one variable TEND TO BE PAIRED WITH higher than average values of the other variable

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5

correlation coefficient (r) is a measure of what?

the strength of the LINEAR relationship between 2 variables, as well as the direction of this relationship (+ or -)

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6

if r is equal to the absolute value of 1â€¦

all points lie on a line

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7

if abs(r) is greater than .8â€¦

the LINEAR correlation is generally regarded to be strong

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8

if abs(r) is between .5 & .8â€¦

the LINEAR correlation is generally regarded to be moderateif

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9

if abs(r) is less than .5â€¦

the LINEAR correlation is generally regarded to be weak

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10

if r is = 0, there isâ€¦

no LINEAR correlation (may be nonlinear correlation)

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11

if you change the order of the explanatory/dependent variables, what effect will this change have on r?

none!

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12

if you change the units of measurement of one of your variables (e.g. ft to yrds), what effect will this change have on r?

none!

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13

is r resistant to extreme values? why/why not?

no; r is based on the MEAN & is effected by extreme values

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14

least squares regression line does what?

minimizes the sum of squared errors/distances of points from our line

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15

residual

difference between the observed values of the response variable (y) and the predicted values (Ĺ·) from the model

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16

a negative residual means that Ĺ· was tooâ€¦

large

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17

a positive residual means that Ĺ· was tooâ€¦

small

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18

a pattern of residuals that doesnâ€™t appear to be randomly distributed about 0 indicatesâ€¦

a regression line that isnâ€™t a good model of our data

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19

interpolation

trying to predict a value of y from a value of x which is WITHIN the range of x-values we have (your traditional prediction, think internal)

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20

extrapolation

trying to predict a value of y from a value of x which ISNâ€™T within the range of x-values we have (unadvised, rarely have confidence, think external)

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21

coefficient of determination

r^2; the proportion of the total variability in y which is explained by the regression of y on x

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22

outlier when dealing with bivariate dataâ€¦

lies outside the general pattern of data (for regression, datapoint has a LARGE RESIDUAL)

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23

influential observation

an observation that has a strong influence on the regression model; most influential points tend to be extreme in the x direction (high LEVERAGE points)

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24
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