| 31. | This model assumes that each group has a different regression model-with its own intercept and slope.
|
| 32. | The standard assumptions of the linear regression model are also assumed to hold, as discussed below.
|
| 33. | An important consideration when carrying out statistical inference using regression models is how the data were sampled.
|
| 34. | Start with a regression model where the outcome Y _ i is impacted by X _ i
|
| 35. | Binomial regression models are essentially the same as binary choice models, one type of discrete choice model.
|
| 36. | The site predicts game outcomes and rates teams using a logistic regression model based on team efficiency statistics.
|
| 37. | In empirical studies, these implicit characteristic prices are coefficients that relate prices and attributes in a regression model.
|
| 38. | In particular, least squares estimates for regression models are highly sensitive to ( not robust against ) outliers.
|
| 39. | Mahalanobis distance and leverage are often used to detect outliers, especially in the development of linear regression models.
|
| 40. | An alternative approach to probability calibration is to fit an isotonic regression model to an ill-calibrated probability model.
|
