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One of the assumptions in regression analysis is that
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a graph of the sample points that will be used to develop a regression line is called
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Regression

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One of the assumptions in regression analysis is that the errors have a mean of 0
a graph of the sample points that will be used to develop a regression line is called a scatter diagram
when using regression, an error is also called a residual
in a regression model, Y is called the dependent variable
a quantity that provides a measure of how far each sample point is from the regression line is the SSE
the percentage of the variation in the dependent variable that is explained by a regression equation is measured by the coefficient of determination
In a regression model, if every sample point is on the regression line (all errors are 0), then correlation coefficient would be -1 or 1
when using dummy variables in a regression equation to model a qualitative or categorical variable, the number of dummy variables should equal 1 less than the number of categories
a multiple regression model differs from a simple linear regression model because the multiple regression model has more than one independent variable
the overall significance of a regression model is tested using an F test. The model is significant if the significance level of the F value is low
A new variable should not be added to a multiple regression model if that variable causes the adjusted R squared to decrease
a good regression model should have a low R squared and a low significance level for the F test
The model that allows us to compare several populations ANOVA tables
If the computed F is greater than the critical F there is significant difference
If the computed F is smaller than the critical F there is not significant difference
Residual is a synonym for error
Df1 Treatments/Regression
Df2 Residual/Error
Correlation Coefficient The strength of a relationship between two variables; R; always between 0-1; can have a positive or an inverse relationship
Coefficient of determination shows what percentage of correlation the independent variable has on the dependent variable; R squared; always positive and always between 0-1
Regression is a synonym for treatments
Variables tend to be WXYZ
Constants tend to be ABCD
regression is all about forecasting based on past data
B slope (^Y/^X)
line of best fit minimizes distance between points - regression equation describes this
y intercept is equal to the A value
if X=O Y=A
no matter what, there will always be associated error, because the line of best fit isn't exact
N # of observations
K total number of variables
innocently assumed as not correlated
null hypothesis innocently assumed as not correlated
The tested hypothesis differences exist
Coefficient of non-determination 1-Rsquared;
to lower error the only thing you can do is enlarge the sample size
computed t shows that the variable is significant
computed F shows that the model is significant
what is the meaning of least squares in a regression model that the regression line will minimize the sum of the squared errors. no other line will give a lower sse
What is the SSE Sum of Squares Error; the total sum of the squared differences between each observation and the predicted value
What is the SSR Sum of squares regression; the total sum of the squared differences between each predicted value and the mean
What is the SST Sum of Squares Total; the total sum of the squared differences between each observation and the mean
Created by: hopekn
 

 



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