Week 7 Flashcards

1
Q

What did francis galton do in 1886

A

Created a graph representing peoples height and their parents height

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2
Q

How to calculate correlation

A

r = Sxy/Sx.Sy

Sxy = how much x and y change together
Sx.Sy = how much x and y change separately

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3
Q

What is the r squared value and how is it calculated

A

The r-squared value tells you (just the r value squared):

How much of the variance is explained by your correlation

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4
Q

If your r-squared valye is close to 1…

A

your correlation explains a lot of variance

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5
Q

What is the r-squared value sometimes called

A

Coefficient of determination

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6
Q

What does the r value go from?

A

-1 to 1

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7
Q

What does the r squared value go from

A

0 to 1

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8
Q

1-r sqaured is

A

The amount of variance not explained.

e.g.if height explains half the data, then what explains the other half

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9
Q

Regression facts

A

When x=0, y=intercept

When x increases by 1, y increases by the slope

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10
Q

y = mx+c

A

y = what the y axis is
m = slope
x + what the x axis is
c = y intercept

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11
Q

Are X and Y interchangeable in regression

A

No, only in correlation

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12
Q

What is the formula for reporting correlations

A

r (df) = (pearsons r), p (p value)

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13
Q

Degree of freedom for correlations

A

Total number minus 2

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14
Q

Reporting regressions

A

Overall results = r squared value

Model fit results = F (df1), (df2) = F value, p value

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15
Q

How to calculate the t value

A

t = estimate / SE

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16
Q

Difference between correlation and regression

A

Correlation describes a single relationship
Describes the direction and strength of a relationship
X and Y are inter-changeable and does not allow predicition

regression can describe multiple relationships
It describes the directionS and strengthS of relationships, X and Y are not interchangeable and it allows prediciton

17
Q

Regression and correlation both

A

Involve linear relationships between one or more input
(predictor) variables and a single output (outcome) variable

18
Q

Multiple regression

A

A single outcome variable (y)
Multiple predictor variables (x1, x2)
Residuals are distances from the surface