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| 14971239352 | model | an equation or formula that simplifies and represents reality | 0 | |
| 14971244239 | linear model | is an equation of a line. To interpret a linear model, we need to know the variables (along with their W's) and their units. | 1 | |
| 14971266279 | predicted variable | the value of y found for a given x-value in the data. A predicted value is found by substituting the x value in the regression equation. The predicted values are the values on the fitted line; the points (x,y) all lie exactly on the fitted line. | 2 | |
| 14971346286 | Residuals | are the differences between data values and the corresponding values predicted by the regression model ----or more generally values predicted by any model. | 3 | |
| 14971389014 | Leasts squares | The leasts squares criterion specifies the unique line that minimizes the variance of the residuals or equivalently, the sum of the squared residuals. | 4 | |
| 14971420936 | regression to the mean | Because the correlation is always less than 1.0 in magnitude, each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean. | 5 | |
| 14971443263 | regression line | The particular linear equation that satisfies the least squares criterion, often called the line of best fit. | 6 | |
| 14971448414 | line of best fit |  | 7 | |
| 14971474381 | slope | the slope b1 gives a value in "y-units per x-units." Changes of one unit in x are associated with changed of b1, units in predicted values of y. The slope can be found by |  | 8 |
| 14971519393 | intercept | b0 gives a starting value in y-units. Its the y value when x is 0 |  | 9 |
| 14971543837 | Se | standard deviation of residuals |  | 10 |
| 14971560940 | R2 | The square of the correlation between x and y gives the fraction of the variability of y accounted for by the least squares linear regressions on x. | 11 | |
| 14971596756 | Does the Plot thicken? Condition | The scatterplot or residuals plot should consistent (vertical) spread in y-values. | 12 |
