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Unformatted text preview: of independence chi squares o Understand why the distribution of chi square is completely positive; that is, know why it is impossible to get a negative value for chi square. o Be able to perform a chi square test from raw data o Know the degrees of freedom for both the goodness of fit and test of independence chi squares o Be able to identify whether a particular research question needs a test of independence or goodness of fit chi square. • Regression and Linear Equations o Be able to fit a line to raw data o Be able to fit a line using summary statistics, such as the sample means, SP, and SS o Be able to identify in a scatter plot which of several linear equations best describes the line shown o Be able to plot raw data on a scatter plot o Know the criterion used to determine “best fitting” line; i.e. the least squares solution o Know how to calculate a predicted Y score using a particular X o Know how to calculate a predicted X score using a particular Y o Know the difference between interpolation and extrapolation, and why extrapolation poses problems • Correlation o Know that correlation does not imply causation o Be able to calculate a correlation from raw data o Be able to calculate a correlation from summary statistics o Know the range of values the correlation can be o Be able to identify among several correlations which is the “strongest” o Be able to identify from scatter plots which data appear to have the strongest correlation o Know how to calculate r2 and be able to identify from a list of options which definition is most accurate Know the range of values that r2 can be o Know how to perform a hypothesis test using r o Know what the null and alternative hypotheses are for a hypothesis test with the correlation coefficient Some Important Formulas from Before Midterm 1 !Xi
µ=
N Mean !Xi
X=
n Standard deviation for population From SS SS
N != SS
n ! 1 Sum of squares for population, Definition form !( X i " µ) 2 Sum of squares for population Computational Form !X 2
i ( !X i )
"
N !( X i " X ) 2 Sum of squares for sample Computational Form !X Standard deviation for sample From SS s= Sum of squares for sample 2 2
i ( !X i )
" 2 n Raw score to z score Xi ! µ
zi =
"...
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 Fall '08
 Ard
 Psychology

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