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overhead - 09 Seasonal and Moving Avg
... Regression Results for Monthly Dummy Variable Model May not have significant effect for each month Must include all months when using model to forecast Jan forecast = 45.93+4.147 * (1) +1.553*T -0.017 *T2 +0.000 * T3 note that Excel shows beta-hat on T3 is 0.000 but in reality it is not zero, expand ...
... Regression Results for Monthly Dummy Variable Model May not have significant effect for each month Must include all months when using model to forecast Jan forecast = 45.93+4.147 * (1) +1.553*T -0.017 *T2 +0.000 * T3 note that Excel shows beta-hat on T3 is 0.000 but in reality it is not zero, expand ...
The Concentration Index Introduction The concentration index
... The concentration index is defined with reference to the concentration curve (q.v.), which graphs on the xaxis the cumulative percentage of the sample, ranked by living standards, beginning with the poorest, and on the y-axis the cumulative percentage of the health variable corresponding to each cum ...
... The concentration index is defined with reference to the concentration curve (q.v.), which graphs on the xaxis the cumulative percentage of the sample, ranked by living standards, beginning with the poorest, and on the y-axis the cumulative percentage of the health variable corresponding to each cum ...
Package `conformal`
... The reference class ConformalClassification contains the following fields: • ClassificationModel: stores a classification Random Forest model. • confidence: stores the user-defined confidence level. • data.new: stores the descriptors corresponding to an external set. • NonconformityScoresMatrix: sto ...
... The reference class ConformalClassification contains the following fields: • ClassificationModel: stores a classification Random Forest model. • confidence: stores the user-defined confidence level. • data.new: stores the descriptors corresponding to an external set. • NonconformityScoresMatrix: sto ...
paper_Scholtus_v3
... The same data have been analysed previously by Scholtus et al. (2015) using a different type of measurement error model, a so-called structural equation model (SEM). Some additional information about these data sources can be found in that paper. Under an SEM, errors are not supposed to be intermitt ...
... The same data have been analysed previously by Scholtus et al. (2015) using a different type of measurement error model, a so-called structural equation model (SEM). Some additional information about these data sources can be found in that paper. Under an SEM, errors are not supposed to be intermitt ...
Predicting Individual Response with Aggregate Data
... parameter estimates. This is because the approach implicitly assumes that all the variation across zip codes is due to the observed average zip code demographics. The authors add zip code level random effects that take into account the unobserved variation across zip codes and increase the standard ...
... parameter estimates. This is because the approach implicitly assumes that all the variation across zip codes is due to the observed average zip code demographics. The authors add zip code level random effects that take into account the unobserved variation across zip codes and increase the standard ...
Document
... When there are just two groups, the F statistic will have 1 and n – 2 degrees of freedom. In this case, the one-way analysis of variance is equivalent to an independent t test. The square root of this F statistic equals the absolute value of the t statistic with n – 2 degrees of freedom. A test due ...
... When there are just two groups, the F statistic will have 1 and n – 2 degrees of freedom. In this case, the one-way analysis of variance is equivalent to an independent t test. The square root of this F statistic equals the absolute value of the t statistic with n – 2 degrees of freedom. A test due ...
Document
... The R2 and R 2 don’t make sense here (why?). So, two other specialized measures are used: The fraction correctly predicted = fraction of Y’s for which predicted probability is >50% (if Yi=1) or is <50% (if Yi=0). The pseudo-R2 measure the fit using the likelihood function: measures the improvement ...
... The R2 and R 2 don’t make sense here (why?). So, two other specialized measures are used: The fraction correctly predicted = fraction of Y’s for which predicted probability is >50% (if Yi=1) or is <50% (if Yi=0). The pseudo-R2 measure the fit using the likelihood function: measures the improvement ...
Coefficient of determination
In statistics, the coefficient of determination, denoted R2 or r2 and pronounced R squared, is a number that indicates how well data fit a statistical model – sometimes simply a line or a curve. An R2 of 1 indicates that the regression line perfectly fits the data, while an R2 of 0 indicates that the line does not fit the data at all. This latter can be because the data is utterly non-linear, or because it is random.It is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related information. It provides a measure of how well observed outcomes are replicated by the model, as the proportion of total variation of outcomes explained by the model (pp. 187, 287).There are several definitions of R2 that are only sometimes equivalent. One class of such cases includes that of simple linear regression where r2 is used instead of R2. In this case, if an intercept is included, then r2 is simply the square of the sample correlation coefficient (i.e., r) between the outcomes and their predicted values. If additional explanators are included, R2 is the square of the coefficient of multiple correlation. In both such cases, the coefficient of determination ranges from 0 to 1.Important cases where the computational definition of R2 can yield negative values, depending on the definition used, arise where the predictions that are being compared to the corresponding outcomes have not been derived from a model-fitting procedure using those data, and where linear regression is conducted without including an intercept. Additionally, negative values of R2 may occur when fitting non-linear functions to data. In cases where negative values arise, the mean of the data provides a better fit to the outcomes than do the fitted function values, according to this particular criterion.