Operational Risk Research on Social Pooling Fund Under Diseases
... input neuron Xi,and the hospitalization expenditure of the social pooling fund Y as output neuron to simulate the training sample trend. Through the simulation of neural network model, the trend of testing sample had been predicted. In addition, before the start of the model training, samples should ...
... input neuron Xi,and the hospitalization expenditure of the social pooling fund Y as output neuron to simulate the training sample trend. Through the simulation of neural network model, the trend of testing sample had been predicted. In addition, before the start of the model training, samples should ...
AP Stat 12.1
... If we calculate the least-squares regression line, the slope b is an unbiased estimator of the population slope β, and the y-intercept a is an unbiased estimator of the population y-intercept α. The remaining parameter is the standard deviation σ, which describes the variability of the response y ...
... If we calculate the least-squares regression line, the slope b is an unbiased estimator of the population slope β, and the y-intercept a is an unbiased estimator of the population y-intercept α. The remaining parameter is the standard deviation σ, which describes the variability of the response y ...
Selection of models for the analysis of risk-factor trees
... SMART-scan is based on a biological assumption that multiple, closely-related OTUs in a tree may function in a similar manner as a group because of shared evolutionary ancestry. Evidence supporting this assumption can be found in many studies, e.g. an entire bacterial family of Streptococcaceae has ...
... SMART-scan is based on a biological assumption that multiple, closely-related OTUs in a tree may function in a similar manner as a group because of shared evolutionary ancestry. Evidence supporting this assumption can be found in many studies, e.g. an entire bacterial family of Streptococcaceae has ...
Question 1 - BrainMass
... determining whether or not there is a significant difference in the gas consumption of the two models of automobiles. A random sample of 7 cars from each manufacturer is selected, and 7 drivers are selected to drive one of GM and one of Ford cars for a specified distance implying that driver 1 drove ...
... determining whether or not there is a significant difference in the gas consumption of the two models of automobiles. A random sample of 7 cars from each manufacturer is selected, and 7 drivers are selected to drive one of GM and one of Ford cars for a specified distance implying that driver 1 drove ...
BSGS: Bayesian Sparse Group Selection
... Variable selection is a fundamental problem in regression analysis, and one that has become even more relevant in current applications where the number of variables can be very large, but it is commonly assumed that only a small number of variables are important for explaining the response variable. ...
... Variable selection is a fundamental problem in regression analysis, and one that has become even more relevant in current applications where the number of variables can be very large, but it is commonly assumed that only a small number of variables are important for explaining the response variable. ...
x - statspages
... value of y is the best predictor for the actual value of y. This implies y = y is preferable. • If the value of y does change linearly with the value of x, then using the regression model gives a better prediction for the value of y than using the mean of y. This implies y = yˆ is preferable. © 2011 ...
... value of y is the best predictor for the actual value of y. This implies y = y is preferable. • If the value of y does change linearly with the value of x, then using the regression model gives a better prediction for the value of y than using the mean of y. This implies y = yˆ is preferable. © 2011 ...
Basic Business Statistics, 10/e
... Multiple Regression Model Contribution of a Single Independent Variable Xj SSR(Xj | all variables except Xj) = SSR (all variables) – SSR(all variables except Xj) Measures the contribution of Xj in explaining the total variation in Y (SST) ...
... Multiple Regression Model Contribution of a Single Independent Variable Xj SSR(Xj | all variables except Xj) = SSR (all variables) – SSR(all variables except Xj) Measures the contribution of Xj in explaining the total variation in Y (SST) ...
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.