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R-squared is regarded as the coefficient of determination and is used to give the proportion of the fluctuation of the variance of one variable to another variable. R-squared also establishes the percentage of data that is near to goodness of fit.
S = 0.0903972 R-Sq = 26.3% R-Sq(adj) = 26.1%
In this case R-squared is 26.3%; this indicates that there is a variation in Y (Wfood) in relation to the linear relationship between the Y and X variables. The remaining percentage (73.7%) is the variation which is unknown.
The adjusted R-squared figure of 26.1% is a more accurate measure of the goodness of fit and as it is lower than r-squared and it indicates that certain explanatory variables are missing therefore the fluctuation of the dependent variable is not fully measured.
VIF is the abbreviation of variance inflation factor which is a measure of the amount of multicollinearity that exists in a set of multiple regression variables. *The VIF value
The number of employees absent from work at a large electronics manufacturing plant over aperiod of 106 days is given in the table below. 146 141 139 140 145 141 142 131 142 140
Multicentre study : The clinical trial conducted simultaneously in the number of participating hospitals, with all centres following an agreed-upon study of the protocol and with
re-reference all these indexes
The term used for the estimation of the misclassification rate in the discriminant analysis. Number of techniques has been proposed for two-group situation, but the multiple-group
Generalized poisson distribution: The probability distribution can be defined as follows: The distribution corresponds to the situation in which the values of the rand
I have a problem I am trying to solve. An oil company thinks that there is a 60% chance that there is oil in the land they own. Before drilling they run a soil test. When there is
The Null Hypothesis - H0: There is no heteroscedasticity i.e. β 1 = 0 The Alternative Hypothesis - H1: There is heteroscedasticity i.e. β 1 0 Reject H0 if Q = ESS/2 >
The Null Hypothesis - H0: Model does not fit the data i.e. all slopes are equal to zero β 1 =β 2 =...=β k = 0 The Alternative Hypothesis - H1: Model does fit the data i.e. at
Censored observations : An observation xi on some variable of interest is consired to be censored if it is known that xi Li (left-censored)or xi Ui (right-censored) where Li and Ui
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