The Simple Regression Model
Statistics

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What terms describe the fit of a regression equation to the data? When is this fit good enough?
Thank you for the opportunity to help you with your question!
When its good enough it implies that
 The model describes the data adequately .Its a prerequisite that you will trust the the conclusion. its evaluated by using a graph or scatters and plots
 When its tested that the the relationships you modeled depends on the model parameters
will continue. Be patient please
Please let me know if you need any clarification. I'm always happy to answer your questions.Regression Fitness describes how well it fits a set of observation . Measures the fitness by summarizing
Te the b discrepancy of the observed values i n the data provided .
Terms used to refer are
1. Test for residual
2. Normality test
3. Fit of distribution
Regression Fitness describes how well it fits a set of observation . Measures the fitness by summarizing
Te the b discrepancy of the observed values i n the data provided .
Terms used to refer are
1. Test for residual
2. Normality test
3. Fit of distribution
Regression Fitness describes how well it fits a set of observation . Measures the fitness by summarizing
Te the b discrepancy of the observed values i n the data provided .
Terms used to refer are
1. Test for residual
2. Normality test
3. Fit of distribution
Yes I need clarification please. Not understanding the answer or the spelling.
I am sorry for that , just a minute and i will clarify
.The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis model , e.g. test of normality .
Regression Fitness describes how well it fits a set of observation . Measures the fitness by summarizing the following test which are very important in determining the fitness of the data.
1. Test for residual
2. Normality test
3. Fit of distribution
The following are conditions/terms .
A wellfitting regression model results in predicted values close to the observed data values. When P>a
Adequately describe the functional relationship between the experimental factors and the response variable.
Hope your understanding now. Thanks
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