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Psych 10 > Regression > Flashcards

Regression Flashcards

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1
Q

Regression

A

Using correlation (r), you can roughly predict one variable value based on another

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2
Q

Regression uses

A

Prediction (College admissions, car insurance rates, dating sites, health insurance) estimation, hypothesis testing, modeling causal relationships

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3
Q

Predictor Variable (X)

A

information you have

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4
Q

Criterion Variable (Y)

A

to-be-predicted variable can be estimated within a certain degree of certainty given known values

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5
Q

X & Y Synonyms

A

X: Predictor Variable, Independent Variable, Explanatory Variable
Y: Criterion Variable, Dependent variable, response variable, outcome variable

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6
Q

Best-fit line

A

A line that is an equal distance from all points on a scatterplot. A line that best fits the data

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7
Q

Regression Questions:

A
  1. Is a pattern evident in a set of data points?
  2. Does the equation of a straight line describe this pattern?
  3. Are the predictions made from this equation significant?
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8
Q

Sum of Squares (SS) for regression

A

The sum of the squared distances of data points from a straight line (gives more information than basic deviation scores, that do not capture the degree of variability from the data points to the line)

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9
Q

Sum of deviation

A

The sum of deviation scores from best fit line always equals zero, does not measure spread

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10
Q

Sum of Squares

A

Squaring each deviation value captures spread from data points to regression line

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11
Q

Equation of a line (slope)

A

Y=bX+a (Y=Mx+b)

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12
Q

Slope

A

B(measures the change in y relative to the change in x)

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13
Q

Intercept

A

a (y when x equals 0)

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14
Q

Method of least squares

A

Method to compute slope and y-intercept of the best fitting straight line to a set of data

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15
Q

Formula for Least Squares

A
  1. Calclulate SSxy, SSx, SSy
  2. b=SSxy/SSx
  3. a=My-(b)(Mx)
  4. Yhat=bx+a
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16
Q

Regression Line

A

The straight line that minimizes total distance of all data points in the correlation

17
Q

regression analysis

A

A methodology for testing regression hypotheses concerning whether predictor variables x can predict outcome variables y

18
Q

Regression variation

A

A value representing the variance in Y associated with changes in X; measured by distance of data points from the regression line

19
Q

resitual variation

A

The variation in Y unrelated to X; the remaining variation

20
Q

F obtained for regression analysis

A

The variation of Y related to changes in x/variance of y not related to changes in x

21
Q

Fobt Formula for Regression analysis

A

MSregression/MSresidual

22
Q

Sum of Squares regression

A

The coefficient of determination times the sum of squares for Y

23
Q

r formula (regression)

A

SPxy/sqrtSSx*SSy (SP is sum of products, (X-Mx)(Y-My))

24
Q

SS residual formula

A

SSresid=(1-r^2)SSy

25
Q

SS residual

A

The Sum of Squares of y times the remaining variance not predicted by r^2, that is, 1-r^2

26
Q

Alternate formula for Sum of Squares Y

A

SSy=SSregression + SSresidual)

27
Q

Mean Square Regression (MSregression)

A

SSregression/dfRegression

28
Q

Mean Square Residual (MSresid)

A

SSresidual/DFresidual

29
Q

DF numerator (predictor) for regression

A

Equal to the number of predictor variables

30
Q

DF denominator (residual) for regression

A

Sample size-2

31
Q

Hypotheses structure for regressions

A

H0: variance for Y is unrelated to variance for X
H1: variance for Y is related to variance for X

32
Q

Multiple Regression

A

A method for predicting Y when two predictors (Xs) are present

33
Q

Multiple Regression formula

A

Yhat=b1X1+b2X2+a
Yhat=b1X1+b2X2+b3X3+a
And so on

Psych 10 (12 decks)

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