Polynomials

cjjstone
Flashcards by cjjstone, updated more than 1 year ago
cjjstone
Created by cjjstone almost 7 years ago
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Flashcards on Polynomials, created by cjjstone on 05/19/2014.

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Question Answer
Linear vs. everything else linear is a straight line, everything else is curvilinear
Why are curvilinear ignored in the literature? People generally don't think in terms of polynomial predictions, theories. They don't know how to compute them. They don't know how to create a figure for them. They struggle interpreting them.
The most famous curvilinear? Yerkes-Dodson Law. Performance x Levels of Arousal As levels of arousal increase, performance increases descreasingly until it hits an asymptote and then performance increases level off
Compute heirarchical regression Multiply the IV by itself, do nothing with the DV, Enter terms in ascending order ones step at a time, look at the R squared change values
R squared change values Any values above .015 should be taken seriously. If below .015 but still registering as significant then perhaps look at it more closely.
Slope on a linear? Constant
slope on a curvilinear is not constant, it changes across the x variable.
Quadratic has one distinct curve
cubic has two distinct curves
In the macro the x values are how many SDs? -2SD - M - 2SD Mean is the centre point. This can mean sometimes raw data is left out
Any interesting results should be instantly? replicated
If there is a significant linear relationship then perhaps there may be some going on in the data. So take a look at the quadratic and cubic etc.
polynomial relationship qualifies the linear relationship. It tells you more about how the relationship is working. That the Pearsons r is not constant across the whole range of the IV but changes
Shapes of quadratic slopes U-shaped, inverse U-shaped Upward, downward, level
Finding polynomials Begin with null hypothesis, ora weaker than expected significant correlation, or don't know
Non-sginificant linear is related to quadratic how? It is not. They are all independent. A non-significant linear relationship does not mean that there is not a sig. quadratic. They don't affect the presence of each other
A non-sig quadratic looks like what? A flat line
Does the direction of the analysis/graph, i.e. from x to y or y to x, make a difference? Yes it does. In some instances it has been shown that x can predict y but y does not predict x. x squared and y squared are not mathematically equivalent.
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