Terms : Hide Images
| 14846204666 | Model | an equation or formula that simplifies and represents reality | 0 | |
| 14846206590 | Linear Model | An equation of a line. To interpret a linear model, we need to know the variables and their units. | 1 | |
| 14846209923 | predicted value | found by substituting the x-value in the regression equation; they're the values on the fitted line | 2 | |
| 14846213832 | residual | the differences between data values and the corresponding values predicted by the regression model | 3 | |
| 14846222160 | Least squares | This criterion specifies the unique line that minimizes the variance of the residuals, or equivalently, the sum of the squared residuals. | 4 | |
| 14846233657 | regression to the mean | Since the correlation is always less than 1 in magnitude, each predicted y-hat tends to be fewer standard deviations from its mean than its corresponding x was from its mean. | 5 | |
| 14846247967 | regression line (line of best fit) | the particular linear equation that satisfies the least squares criterion | 6 | |
| 14846251484 | Slope | This gives a value in y-units per x-unit. | 7 | |
| 14846261247 | Intercept | This is the starting value in y units. It is the y-hat value when x is 0. | 8 | |
| 14846273255 | R^2 | Coefficient of determination. Gives the fraction of the variability of y accounted for by the LSRL on x. | 9 | |
| 14846286913 | Does the Plot Thicken? Condition | the scatterplot or residuals plot should show consistent (vertical) spread in y-values | 10 | |
| 14846294533 | Standard deviation of the residuals | Gives the approximate size of a "typical" prediction error. | 11 |
