Unit 3 Flashcards

1
Q

What does r tell us

A

r= tells us strength and direction
r= how far away the points are from line of best fit

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2
Q

smaller r=

A

less correlation

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3
Q

how exact can the r be

A

1

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4
Q

high leverage points

A

tilt the line

far from the cluster in the x direction

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5
Q

influential points

A

have substantially large impact on slope, y-int, correlation

far from cluster in both x and y direction, doesnt fit pattern

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6
Q

formulas for find y-int and find slope

A

find y-int: a=ymean-bxmean
find slope: b=r
(sy)/(sx)

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7
Q

choosing the best regression

A

-scatterplot: linear pattern
-r and r^2: close to r=1 and r^2=100%
-residual plot: no residual pattern

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8
Q

what does r^2 mean

A

the percent of variation

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9
Q

interpret r^2
ex- r^2= .974

A

___97.4%___ of the variation in __y__ is explained by the linear relationship with ____x_____

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10
Q

correlation does not equal causation

A

the r is .99 does not mean that x causes y its just showing an association

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11
Q

the x is called
and the y is called

A

explanatory
response

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12
Q

how to describe relationships

A

DUFS
Directions (pos/neg/none(flat))
Unusual features (outliers/ clusters)
Form (linear/nonlinear)
Strength (weak, moderate, strong)
Contex (relationship between __X__ and __y__)

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13
Q

scattered points on a scatterplot. linear or non linear?

A

linear bc nonlinear would have a curve

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14
Q

interpret the residual

A

the actual __y__ was _residual value was __above/below__ the predicted value for __x__

Dont forget units!

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15
Q

Explain the relationship displayed in the scatterplot

A

DUFS
D(direction)
U(unusual features)
F(linear, nonlinear)
S(stength)

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16
Q

explanatory and response on graph

A

x axis is explanatory and y axis is response

17
Q

what is r^2

A

coefficient of determination

18
Q

interpret coefficient of determination

A

___% of the variation in __y__ is explained by the linear relationship with __x__

19
Q

how do you find the residual

A

actual-predicted (A-P)

20
Q

point above the line

A

positive residual

21
Q

point below the line

A

negative residual

22
Q

find the correlation

A

ur looking for r

23
Q

interpret slope

A

for each additional __x__, the predicted __y__ increases/decreases by __slope__

24
Q

interpret y-int

A

when __x__=0 the predicted __y__ __contex__ is __y-int__

25
Q

horizontal outlier

A

tilt the line
outlier that fit the y direction but not the x

26
Q

vertical outlier

A

small impact
move line up or down
outlier that fit the x direction but not the y