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Hint

1

explanatory variable

independent variable; x-axis

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2

response variable

dependent variable; y-axis

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3

correlation coefficient (r)

measures the strength of a linear relationship

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4

r interpretation

There is a (strength), (direction) linear relationship between (variable) and (variable).

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5

lurking variable

â€śbehind the scenesâ€ť variable that affects association

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6

LSRL

line that minimizes distance of data points to the line

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7

LSRL equation

y^= a + bx

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8

slope interpretation

For every increase in one (x-context), the model predicts an/a (increase/decrease) of about (slope) in (y-context).

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9

y-int interpretation

When (x=0 context) the predicted value of (y-context) is about (y-int).

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10

residual formula

residual = actual - predicted

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11

residual interpretation

The actual (y-context) was (residual) (above/below) the predicted value for (x-context).

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12

interpolation

predictions made within the range of data

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13

extrapolation

predictions made outside of the data range (unreliable)

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14

s

standard deviation of residuals (typical error)

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15

s interpretation

The actual (y-context) is typically about (S) away from the number predicted by the LSRL.

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16

RÂ˛

% improvement in error given an explanatory variable

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17

RÂ˛ interpretation

About (RÂ˛)% of the variability in (y-context) can be explained by the variability in (x-context).

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18

outliers

do not follow linear pattern and have a large residual

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19

high leverage points

substantially larger/smaller x-value than the rest of the data

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20

influential points

any point that will change the LSRL substantially if removed

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