Terms : Hide Images
| 7137369968 | The coefficient of determination, r² | A statistic: It is the proportion of the variation in the y-variable that is accounted for by the linear regression line. | 0 | |
| 7137388872 | Correlation, r | A statistic: It measures the direction and strength of a linear relationship between two quantitative variables. | 1 | |
| 7140517363 | Description of a scatterplot | It addresses the direction, form and strength of an association between two quantitative variables. | 2 | |
| 7140530392 | Explanatory variable | It is the variable that is used to predict or explain changes in the response variable. | 3 | |
| 7140532124 | Extrapolation | It is a prediction for the response variable, based on the regression line and an explanatory variable value that is outside the observed range of explanatory variable values. | 4 | |
| 7140543940 | Influential observation | It is an observation that has a strong effect on the regression line, especially on the slope of the regression line. | 5 | |
| 7140549825 | Least-squares regression line | It is the line that makes the sum of the squared residuals (vertical distances from the data to the line) as small as possible. | 6 | |
| 7140554408 | Negative association | It occurs when above-average values of one variable tend to accompany below-average values of the other, and vice versa. | 7 | |
| 7140559764 | Outlier in regression | It is an observation that lies outside the overall pattern of the other observations in a scatterplot. They may or may not have large regression residuals, and they may or may not be influential. | 8 | |
| 7140567113 | Positive association | It occurs when above-average values of one variable tend to accompany or occur together with above-average values of the other. | 9 | |
| 7140572135 | Predicted value | It is the response value (y-value) that is computed by substituting an explanatory value (x-value) into the regression equation. Referred to as yhat. | 10 | |
| 7140582631 | Residual | It is the difference between an actual y-value and the y-value predicted by the regression line. It can be positive or negative. | 11 | |
| 7140592316 | Residual plot | It is a scatterplot of the regression residuals against the explanatory variable values. It helps analyze the form of a relationship, and whether a linear model is appropriate. | 12 | |
| 7140597777 | Scatterplot | It is a plot of two variables, each one measured on an x- or y-axis. Each data point represents an (x,y) pair of numbers. | 13 | |
| 7140608598 | Slope | It is the amount by which the response variable changes for a one unit increase in the explanatory variable along a regression line. | 14 | |
| 7140617170 | Standard deviation of the residuals | It is the approximate size of a typical prediction error. It is the estimate of the typical vertical distance between the regression line and the actual data points. | 15 | |
| 7140623159 | y-intercept | It is the predicted y-value when x=0 is substituted into a linear regression model. | 16 |
