The assumptions of a regression model can be evaluated by plotting and analyzing the error terms.
Important assumptions in regression model analysis are
- There should be a linear and additive relationship between dependent (response) variable and independent (predictor) variable(s).
- There should be no correlation between the residual (error) terms. Absence of this phenomenon is known as auto correlation.
- The independent variables should not be correlated. Absence of this phenomenon is known as multi col-linearity.
- The error terms must have constant variance. This phenomenon is known as homoskedasticity. The presence of non-constant variance is referred to heteroskedasticity.
- The error terms must be normally distributed.
Hence we can conclude that the assumptions of a regression model can be evaluated by plotting and analyzing the error terms.
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Answer:
Erm. If you know pie use it with the 3.141 Since its a half circle i believe you need that since thats the equasion with it. I don't really know :P
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number 1 is 68 degrees Fahrenheit and number 2 is 98.6=37 degrees Celsius and 102 fahrenheit is higher than the normal
Answer:
Imagine
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