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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
X2 test Conditions:
- Random Sample
- Sample is large
Goodness of fit test
- Fixed number of trials
- Constant probability of success
- Independant trials
X2 test of independance
- 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
- 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
- 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
the degree of linear association between 2 numerical values
how one numerical variabl linearly co-varies 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
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