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QMB Test 1
Regression
| Question | Answer |
|---|---|
| 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