Ap Stats Chapter 3

Front 1

Response Variable (dependent variable )

Back 1

A response variable meassures an outcome of a study.

Front 2

Explanatory Variable (independent variables)

Back 2

An explaneatory variable may help explain or influence changes in a response variable.

Front 3

Scatter Plot

Back 3

A scatter plot shows the relationship between two quanatative variables messured on the same indivisuals. The values of one variable appear on the horizontal axis, and the values of the other variable appear on the vertical axis. Each individual in the data appears as a point in the graph.

Front 4

How to make a Scatterplot by hand (three steps)

Back 4

1. Decide which variable should go on each axis.

2. Label and scale your axes.

3. Plot individual data values.

Front 5

Dirrection

Back 5

The overall pattern moves in the same general way.

Front 6

How to examin a Scatterplot (three steps)

Back 6

1. You can describe the overall pattern of a scatterplot by the direction, form and strength of the relationship.

2. AN important kind of departure is an outliar, an individual value that falls outside the overall pattern of the relationship.

Front 7

Positive Association

Back 7

Two variables have a positive associaion when above- adverage values of one tend to accompany above- average values of the other and when below- adverage values also tend to occur together

Front 8

Negetive Assosiation

Back 8

Two variables have a negetive assosiation when above- adverage values of one tend to accompany below-average values of the other.

Front 9

Correlation r

Back 9

meassures the dirrection and strength of the linear relationship between two quantitative variables.

Front 10

what is the sign for a positive assosiation

Back 10

r>0

Front 11

The sign for a negetive assosiation

Back 11

r<0

Front 12

Values of R near zero inidcate

Back 12

a very weak linear relationship

Front 13

The strength of the linear relationship increases as...

Back 13

R moves away from 0 towards either 1 or -1

Front 14

The exstreme values of r=1 and r=-1 occur only on the case of...

Back 14

a perfect linear relationship

Front 15

Standardized values have...

Back 15

no units

Front 16

Correlation makes no distinction between

Back 16

expanatory and response variables

Front 17

R does not change when we change

Back 17

units of meassurement

Front 18

Correlation requires both variables to be...

Back 18

Quanitative

Front 19

Use r with caution when...

Back 19

Outliars appear in the scatterplot

Front 20

Regression line

Back 20

rehression line is a line that describes how a response variable y changes as an explanatory variable x changes. We often use a regression line to predict the value of y for a given value of x

Front 21

A regression line summarizes the reationship between two variables but only in a specific setting:

Back 21

when one of the variables helps explain or predict the other

Front 22

Regression, unlike correlation requie that we have an...

Back 22

explanatory variable and a response variable

Front 23

A regression line relating to y and x has an equation of the form

Back 23

Y(y hat)= a+bx

Front 24

Y (read y hat) is the...

Back 24

predicted value of the response variable y for a given value of the expanatory variable x

Front 25

What is b

Back 25

B is the slope the amount by which y is predicted to change when x increases by one unit

Front 26

What is A

Back 26

A os the y intercept the predicted calue of y when x=0

Front 27

You cant't say how important a relationship is by :

Back 27

looking at the size of the regression line

Front 28

The accuracy of predictions from a regression line depends on how much...

Back 28

the daa scaters about the line

Front 29

Extapolation

Back 29

the use of a regression line for predictions far outside the interval of values if the explanatory variable x used to obtain the line. Such predictions are often not accurate.

Front 30

We can give the equatio of the least squares regression line in terms of the BLANK and BlANK of the two variables and their correlation.

Back 30

Means and Standard Deviations

Front 31

For an increases of one standard deviation in the value of the explanatory variabe x, the least squares regression line predicts...

Back 31

an increase of r standar deviations in the response variable Y.

Front 32

As the correlation grows BLANK strong, the prediction Y hat moves less on response to x.

Back 32

less

Front 33

Standardrizing a vraibale converts its mean to BLANK and its standard deviation to BLANK

Back 33

0, 1

Front 34

the mean of the least squares residual is always BLANK

Back 34

zero

Front 35

residual plot

Back 35

A scatterplot of the residual against the eplanatory variable.

Front 36

What do residual plots do ?

Back 36

Help us assess how well a regression line fits the data.

Front 37

The residual plot should show no BLANK

Back 37

obvious pattern

Front 38

The residuals should be BLANK

Back 38

relativly small in size

Front 39

What does the standard deviation of residuals do ?

Back 39

Gives the appropriate size of a typica or adverage prediction error (residual)

Front 40

SST

Back 40

measures the total variation in the y values

Front 41

the coefficient od determination r squared in regression

Back 41

The fraction of the variation in the values of Y that is accounted for by the least squares regresssion line of Y on X

Front 42

A and B equal the BLANK collum in calcilations. A is the Blank

Back 42

Coefficent, constant

Front 43

The difference between BLANK and BLANK variables is important in regression.

Back 43

expanatory and response

Front 44

Correlation and regression lines describe only BLANK relationship.

Back 44

Linear

Front 45

Correlation and least square regression lines are not BLANK

Back 45

resistent

Front 46

Points that are outliars in the y-dirrection of a scatter plot have BLANK

Back 46

large residuals

Front 47

Influntial points often have BLANK residuals because they pull the regression line towards themselves

Back 47

small