Regression
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| One of the assumptions in regression analysis is that | show 🗑
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| show | a scatter diagram
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| when using regression, an error is also called | show 🗑
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| in a regression model, Y is called | show 🗑
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| a quantity that provides a measure of how far each sample point is from the regression line is | show 🗑
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| the percentage of the variation in the dependent variable that is explained by a regression equation is measured by | show 🗑
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| show | correlation coefficient would be -1 or 1
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| show | 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 | show 🗑
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| the overall significance of a regression model is tested using an F test. The model is significant if | show 🗑
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| A new variable should not be added to a multiple regression model if that variable causes | show 🗑
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| show | 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 | show 🗑
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| If the computed F is greater than the critical F | show 🗑
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| show | there is not significant difference
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| Residual is a synonym for | show 🗑
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| Df1 | show 🗑
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| show | Residual/Error
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| Correlation Coefficient | show 🗑
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| Coefficient of determination | show 🗑
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| show | treatments
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| show | WXYZ
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| show | ABCD
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| show | is all about forecasting based on past data
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| show | slope (^Y/^X)
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| show | minimizes distance between points - regression equation describes this
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| y intercept | show 🗑
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| show | Y=A
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| no matter what, there will always be associated | show 🗑
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| N | show 🗑
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| show | total number of variables
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| innocently assumed as | show 🗑
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| null hypothesis | show 🗑
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| The tested hypothesis | show 🗑
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| show | 1-Rsquared;
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| to lower error | show 🗑
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| computed t shows that the | show 🗑
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| show | model is significant
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| what is the meaning of least squares in a regression model | show 🗑
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| show | Sum of Squares Error; the total sum of the squared differences between each observation and the predicted value
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| show | 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 | show 🗑
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Created by:
hopekn