Bivariate Data Cleaning
... For the Z=2 group • the sample size drops by 1 • the mean increases (since all the outliers were "too small" outliers) • the std decreases (because extreme values were trimmed) The combined results is a significant mean difference - the previous results with the "full data set" were misleading becau ...
... For the Z=2 group • the sample size drops by 1 • the mean increases (since all the outliers were "too small" outliers) • the std decreases (because extreme values were trimmed) The combined results is a significant mean difference - the previous results with the "full data set" were misleading becau ...
Generalized Linear Models - Statistics
... In some cases where these conditions are not met, we can transform Y so that the linear model assumptions are approximately satisfied. However it is often difficult to find a transformation that simultaneously linearizes the mean and gives constant variance. If Y lies in a restricted domain (e.g. Y ...
... In some cases where these conditions are not met, we can transform Y so that the linear model assumptions are approximately satisfied. However it is often difficult to find a transformation that simultaneously linearizes the mean and gives constant variance. If Y lies in a restricted domain (e.g. Y ...
10 Correlation and regression
... Since d is approximately equal to 2(1-r), where r is the sample autocorrelation of the residuals, d = 2 indicates that appears to be no autocorrelation, its value always lies between 0 and 4. If the Durbin–Watson statistic is substantially less than 2, there is evidence of positive serial correlatio ...
... Since d is approximately equal to 2(1-r), where r is the sample autocorrelation of the residuals, d = 2 indicates that appears to be no autocorrelation, its value always lies between 0 and 4. If the Durbin–Watson statistic is substantially less than 2, there is evidence of positive serial correlatio ...
time series econometrics: some basic concepts
... of the preceding three specifications of the DF test, which can be seen clearly from Appendix D, Table D.7. • Moreover, if, say, specification (4.4) is correct, but we estimate (4.2), we will be committing a specification error, whose consequences we already know from Chapter 13. • The same is true ...
... of the preceding three specifications of the DF test, which can be seen clearly from Appendix D, Table D.7. • Moreover, if, say, specification (4.4) is correct, but we estimate (4.2), we will be committing a specification error, whose consequences we already know from Chapter 13. • The same is true ...
Logistic Regression
... where ŷ is the probability of a 1, e is the base of the natural logarithm (about 2.718) and b are the parameters of the model. The value of a yields ŷ when X is zero, and b adjusts how quickly the probability changes with changing X a single unit (we can have standardized and unstandardized b in l ...
... where ŷ is the probability of a 1, e is the base of the natural logarithm (about 2.718) and b are the parameters of the model. The value of a yields ŷ when X is zero, and b adjusts how quickly the probability changes with changing X a single unit (we can have standardized and unstandardized b in l ...
Political Science 30: Political Inquiry
... discussion we know that the total squared prediction errors equal 26,840. If take [1 – (26,840/81,776 = 1 - .328 = 67.1) we find that variation in senator conservatism, party affiliation and state median household income explained 67.1% of the variation in senatorial voting on tax legislation. ...
... discussion we know that the total squared prediction errors equal 26,840. If take [1 – (26,840/81,776 = 1 - .328 = 67.1) we find that variation in senator conservatism, party affiliation and state median household income explained 67.1% of the variation in senatorial voting on tax legislation. ...
Burnham et al. (2011)
... K-L “best” model. This uncertainty is quantified by the model probabilities (e.g., the best model has only probability 0.47). Often, a particular model is estimated to be the best of those in the model set; however, there may be substantial uncertainty over this selection. In addition, there is usua ...
... K-L “best” model. This uncertainty is quantified by the model probabilities (e.g., the best model has only probability 0.47). Often, a particular model is estimated to be the best of those in the model set; however, there may be substantial uncertainty over this selection. In addition, there is usua ...
