Regression Module Flashcards
Explain simple linear regression
used to examine if the DV can be predicted by the IV
e.g., can the results (DV) be predicted by the arrival time (IV)?
What are the three differences between simple linear regression and correlation
> asymmetry vs symmetry relationship
2 variables with relationship vs 2 variables without relationships
look for predictions vs look for correlations
What is the formula of simple regression model equation, full simple regression equation, and the residual
Y(hat) (predicted DV, which is the Y axis) = a + bX(i) (observed IV, which is the X axis)
Y(i) (observed DV, y axis) = a + bX(i) + e (residual)
residual = observed - predicted of the DV (y axis)
what does the best fitting line mean, what is the method that is used to obtain the line, and what are the residuals called
the least residual, the line that is drawn closes to all points on the model
the method of least square
sum of all squared residuals
residuals above the line = positive residuals
residuals below the line = negative residuals
What are the two things that are assessed after completing the regression model?
the strength of the model (Rˆ2) and whether the model can be used for the population (rhoˆ2)
explain Rˆ2
> measures the variation predicted of the DV
ranges between 0-1
0 = no prediction of the DV; 1 = perfect prediction of the DV
Rˆ2 x 100% = the percentage of variation that the model can predict
explain rhoˆ2
run a f-test and NHST
H0 = rhoˆ2 = 0 (i.e., IV does not significantly predict the DV)
H1 vice versa
run f-test
df(1) = number of IV = k
df(2) = n - k - 1
if the F-score > critical + p < critical
can reject null hypothesis and accept H1
What is the regression coefficient, and what does it determine
b
the sign of b determines the direction of the model
the size of b determines the degree of the model
What will happen when:
1. b = +
- b = -
> the model is positive
if IV increases by one unit, DV also increases by b unit
if IV decreases by one unit, DV also decreases by b unit
> the model is negative
if IV increases by one unit, DV decreases by b unit
if IV decreases by one unit, DV increases by b unit
c x b: c = number of units increases
explain the difference between unstandardised and standardised regression model
unstandardised
> used to predict by how many DV units are changed due to the unit change of IV
> usually used when the variables are interpretable
standardised
> used to predict by how many DV SD is changed when the SD of the IV is changed
> useful when the variables are uninterpretable (attitude)