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59 Cards in this Set
- Front
- Back
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H0
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Null Hypothesis ( No relationship )
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H1
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Alternative Hypothesis ( There is a relationship )
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Type 1 Error
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Odds of saying there is a relationship, when there is not
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Type 2 Error
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Odds of saying there isn’t a relationship, when there is
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Nominal X Nominal
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Yule’s Q / Lambda /
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Nominal X Ordinal
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Lambda
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Ordinal X Ordinal
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Gamma
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GAMMA
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The magnitude, strength and direction of the relationship (strength = low or high, direction = positive or negative)
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LAMBDA
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The strength and direction
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Magnitude
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The closer the value is to 1 the stronger the association
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•R squared (Correlation Squared)
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How much of the variance in the dependent can be explained by the variance in the independent variable (USE PEARSONS P!)
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•Adjusted R squared
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How much percentage of the variance can be explained by ALL of the independent variable
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R^2 of 1
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Explaining everything
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R^2 of 0
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Explaining nothing
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F Score
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How sufficient your model is
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F Score – 4 or above
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Good model
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F score
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R^2/1-R^2 = (n-k-1)/k
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N
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Total number of things
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K
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Number of independent variables
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Nominal X Ordinal
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Chi Squared ( X^2)
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Critical/Rejection Region
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Ability to reject the null hypothesis (You always want to)
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Test of Significance
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T-tests
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68%, 95%, 99.7% RULE
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STANDARD DEVIATION RULE
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68%
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1 st.dev = 68% of observations
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95%
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2 st.dev = 95% of observations
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99.7%
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3 st.dev = 99.7% of observations
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P <= 0.05 (alpha level)
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95% (confidence level)
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P <= 0.01 (alpha level)
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99% (confidence level)
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P <= 0.001 (alpha level )
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99.9% (confidence level
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Standardized Beta Coefficients
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Relative strength of the independent variables (only the significant ones) What is the best independent variable to explain the dependent variable!
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H0
|
Null Hypothesis ( No relationship )
|
|
|
H1
|
Alternative Hypothesis ( There is a relationship )
|
|
|
Type 1 Error
|
Odds of saying there is a relationship, when there is not
|
|
|
Type 2 Error
|
Odds of saying there isn’t a relationship, when there is
|
|
|
Nominal X Nominal
|
Yule’s Q / Lambda /
|
|
|
Nominal X Ordinal
|
Lambda
|
|
|
Ordinal X Ordinal
|
Gamma
|
|
|
GAMMA
|
The magnitude, strength and direction of the relationship (strength = low or high, direction = positive or negative)
|
|
|
LAMBDA
|
The strength and direction
|
|
|
Magnitude
|
The closer the value is to 1 the stronger the association
|
|
|
•R squared (Correlation Squared)
|
How much of the variance in the dependent can be explained by the variance in the independent variable (USE PEARSONS P!)
|
|
|
•Adjusted R squared
|
How much percentage of the variance can be explained by ALL of the independent variable
|
|
|
R^2 of 1
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Explaining everything
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|
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R^2 of 0
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Explaining nothing
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F Score
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How sufficient your model is
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F Score – 4 or above
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Good model
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F score
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R^2/1-R^2 = (n-k-1)/k
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N
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Total number of things
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K
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Number of independent variables
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Nominal X Ordinal
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Chi Squared ( X^2)
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Critical/Rejection Region
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Ability to reject the null hypothesis (You always want to)
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Test of Significance
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T-tests
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68%
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1 st.dev = 68% of observations
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95%
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2 st.dev = 95% of observations
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99.7%
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3 st.dev = 99.7% of observations
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P <= 0.05 (alpha level)
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95% (confidence level)
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P <= 0.01 (alpha level)
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99% (confidence level)
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P <= 0.001 (alpha level )
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99.9% (confidence level
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Standardized Beta Coefficients
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Relative strength of the independent variables (only the significant ones) What is the best independent variable to explain the dependent variable!
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