y - RachelSawyer
... Thus, if the relationship between two variables follows an exponential model, and we plot the logarithm (base 10 or base e) of y against x, we should observe a straight-line pattern in the transformed data. If we fit a least-squares regression line to the transformed data, we can find the predicted ...
... Thus, if the relationship between two variables follows an exponential model, and we plot the logarithm (base 10 or base e) of y against x, we should observe a straight-line pattern in the transformed data. If we fit a least-squares regression line to the transformed data, we can find the predicted ...
CH 3 Student Notes - Princeton High School
... o Only statistically meaningful when the response can take a value close to zero. Coefficient of Determination - r 2 • The fraction of variation in the values of “y” that is explained by the least – squares regression of “y” on “x” • The proportion€of the total sample variability that is explained b ...
... o Only statistically meaningful when the response can take a value close to zero. Coefficient of Determination - r 2 • The fraction of variation in the values of “y” that is explained by the least – squares regression of “y” on “x” • The proportion€of the total sample variability that is explained b ...
Fixed Effects Models (very important stuff)
... so if you estimate a plain vanilla OLS model, you will have to worry about unobservable factors that are correlated with the variables that you included in the regression. Omitted variable bias would result. If you believe that these unobservable factors are time-invariant, then fixed effects regres ...
... so if you estimate a plain vanilla OLS model, you will have to worry about unobservable factors that are correlated with the variables that you included in the regression. Omitted variable bias would result. If you believe that these unobservable factors are time-invariant, then fixed effects regres ...
Document
... In Chapter 15, we looked at associations between two categorical variables. We will now focus on relationships between two continuous variables. Regression is used to describe a relationship between a quantitative response variable and one or more quantitative predictor variables. In this class we d ...
... In Chapter 15, we looked at associations between two categorical variables. We will now focus on relationships between two continuous variables. Regression is used to describe a relationship between a quantitative response variable and one or more quantitative predictor variables. In this class we d ...
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.