##### The Simple Regression Model

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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 well-fitting 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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