1 CHECKING MODEL ASSUMPTIONS (CHAPTER 5) The
... Rationale: The order here is arranged so that you should stop if you find a major problem. (Possible remedies to problems will be discussed later.) 1. If you’ve got the wrong model, everything else will be messed up. 2. Outliers might indicate mistakes in recording data. 3. Procedures are least robu ...
... Rationale: The order here is arranged so that you should stop if you find a major problem. (Possible remedies to problems will be discussed later.) 1. If you’ve got the wrong model, everything else will be messed up. 2. Outliers might indicate mistakes in recording data. 3. Procedures are least robu ...
Model 1
... Sensitivity of least squares estimates and their standard errors to small changes in model. ...
... Sensitivity of least squares estimates and their standard errors to small changes in model. ...
Ch14-Notes
... indicate negative linear relationship and positive values of r indicate positive linear relationship. The magnitude of r indicates the strength of the linear relationship with a value of 1 or -1 indicating perfect relationship. Values close to zero for r indicate that X and Y are not linearly relate ...
... indicate negative linear relationship and positive values of r indicate positive linear relationship. The magnitude of r indicates the strength of the linear relationship with a value of 1 or -1 indicating perfect relationship. Values close to zero for r indicate that X and Y are not linearly relate ...
Week 5: Simple And Multiple Regression
... • Linearity and normality, outliers taken care of ~ X3 is kinda sketchy (skew is gone, but still problems with kurtosis) • Predictor variables continuous or categorical ~ X3 is kinda sketchy • Outcome variable continuous = YES! • Non-zero variances ~ X3 is kinda sketchy • No multicollinearity betwee ...
... • Linearity and normality, outliers taken care of ~ X3 is kinda sketchy (skew is gone, but still problems with kurtosis) • Predictor variables continuous or categorical ~ X3 is kinda sketchy • Outcome variable continuous = YES! • Non-zero variances ~ X3 is kinda sketchy • No multicollinearity betwee ...
notes #19
... • Coefficients indicates the standardized beta coefficients – this is the most important information about which variables contribute the most. • t value indicates with the Sig whether the contribution of this variable is significant – needs to be < 0.05. • Changing the variables may have an effect ...
... • Coefficients indicates the standardized beta coefficients – this is the most important information about which variables contribute the most. • t value indicates with the Sig whether the contribution of this variable is significant – needs to be < 0.05. • Changing the variables may have an effect ...
Topic #10: Correlation
... linear dependence between the variables. The closer the coefficient is to either -1 or 1, the stronger the correlation between the variables. If the variables are independent then the correlation is 0, but the converse is not true because the correlation coefficient detects only linear dependencies ...
... linear dependence between the variables. The closer the coefficient is to either -1 or 1, the stronger the correlation between the variables. If the variables are independent then the correlation is 0, but the converse is not true because the correlation coefficient detects only linear dependencies ...
Interaction (statistics)
In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the simultaneous influence of two variables on a third is not additive. Most commonly, interactions are considered in the context of regression analyses.The presence of interactions can have important implications for the interpretation of statistical models. If two variables of interest interact, the relationship between each of the interacting variables and a third ""dependent variable"" depends on the value of the other interacting variable. In practice, this makes it more difficult to predict the consequences of changing the value of a variable, particularly if the variables it interacts with are hard to measure or difficult to control.The notion of ""interaction"" is closely related to that of ""moderation"" that is common in social and health science research: the interaction between an explanatory variable and an environmental variable suggests that the effect of the explanatory variable has been moderated or modified by the environmental variable.