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Goodness of fit test:
Compares an observed set of frequencies with an expected set of frequencies

Test of independance:
Checks to see if independance holds in the population

X^{2 }test Conditions:
 Random Sample
 Sample is large

Goodness of fit test
Conditions:
 Fixed number of trials
 Constant probability of success
 Independant trials

X^{2 }test of independance
Conditions:
 Random Sample
 Large sample

Regression Analysis is used primarily to model :
 Causality and provide prediction (forecasting).
 To predict the values of a dependant variable based on values of at least one independant variable.
 Explain the effect of the independant variables on the dependant variable.

Types of Relationships
 Positive Linear Relationship
 Negative Linear Relationship
 Relationship Not Linear
 No Relationship

Regression Conditions:
 Errors: (distance between a value of y and the population regression line) are:
 Independant of the independant variable (x)
 Do not systematically vary with the dependant variable (y)
 Are independant of time
 Normality:
 The errors are normally distributed atound the regression line
 Homoscedasticity (constant variance):
 For each x value, the variable of y around the regression line is the same

Correlation measures:
the degree of linear association between 2 numerical values

Covariance measures:
how one numerical variabl linearly covaries with another numerical variables.

Strength of Correlation:
 Very Strong : r > 0.90
 Strong : 0.75 < r < 0.90
 Moderate : 0.4 < r < 0.75
 Weak : r < 0.4
 Virtually no correlation : r is close to zero

Z confidence levels:
 90% = 1.645
 95% = 1.96
 99% = 2.576

