Estimating Average Treatment Effects
... the associated standard errors. Supporting analyses should be presented to convey to the reader that the estimates are credible (Athey and Imbens [2016]). By credible we do not mean whether the unconfoundedness property holds, but whether the estimates effectively adjust for differences in the covar ...
... the associated standard errors. Supporting analyses should be presented to convey to the reader that the estimates are credible (Athey and Imbens [2016]). By credible we do not mean whether the unconfoundedness property holds, but whether the estimates effectively adjust for differences in the covar ...
Measures of Center and Spread
... Focus on Critical Thinking MP.3 Ask students what must be true about a data set in order for the median to be one of the values in the data set. Students should understand that when the median is in the data set, either the set has an odd number of values, or the set has an even number of values and ...
... Focus on Critical Thinking MP.3 Ask students what must be true about a data set in order for the median to be one of the values in the data set. Students should understand that when the median is in the data set, either the set has an odd number of values, or the set has an even number of values and ...
Multivariate Data Analysis
... The contribution of individual predictors: The logistic regression coefficient for the i-th independent variable shows the change in the predicted log odds of having an outcome for one unit change in the i-th independent variable, all other variables being equal. That is, if the i-th independent var ...
... The contribution of individual predictors: The logistic regression coefficient for the i-th independent variable shows the change in the predicted log odds of having an outcome for one unit change in the i-th independent variable, all other variables being equal. That is, if the i-th independent var ...
Poisson Regression - Department of Statistics
... approximately the same as the Poisson probability of k with µ = np. Here is an example with p = 0.01 and n = 10. > dbinom(0:4, 10, 0.01) [1] 9.043821e-01 9.135172e-02 4.152351e-03 1.118478e-04 1.977108e-06 > dpois(0:4, 10 * 0.01) [1] 9.048374e-01 9.048374e-02 4.524187e-03 1.508062e-04 3.770156e-06 ...
... approximately the same as the Poisson probability of k with µ = np. Here is an example with p = 0.01 and n = 10. > dbinom(0:4, 10, 0.01) [1] 9.043821e-01 9.135172e-02 4.152351e-03 1.118478e-04 1.977108e-06 > dpois(0:4, 10 * 0.01) [1] 9.048374e-01 9.048374e-02 4.524187e-03 1.508062e-04 3.770156e-06 ...
Business Statistics: A Decision
... Since σ is known, the cutoff value is a z value: Reject H0 if z < z = -1.645 ; otherwise do not reject H0 ...
... Since σ is known, the cutoff value is a z value: Reject H0 if z < z = -1.645 ; otherwise do not reject H0 ...