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