yes
Simple regression is used when there is one independent variable. With more independent variables, multiple regression is required.
If the regression sum of squares is the explained sum of squares. That is, the sum of squares generated by the regression line. Then you would want the regression sum of squares to be as big as possible since, then the regression line would explain the dispersion of the data well. Alternatively, use the R^2 ratio, which is the ratio of the explained sum of squares to the total sum of squares. (which ranges from 0 to 1) and hence a large number (0.9) would be preferred to (0.2).
true
linear regression
In linear correlation analysis, we identify the strength and direction of a linear relation between two random variables. Correlation does not imply causation. Regression analysis takes the analysis one step further, to fit an equation to the data. One or more variables are considered independent variables (x1, x2, ... xn). responsible for the dependent or "response" variable or y variable.
in general regression model the dependent variable is continuous and independent variable is discrete type. in genral regression model the variables are linearly related. in logistic regression model the response varaible must be categorical type. the relation ship between the response and explonatory variables is non-linear.
Regression analysis describes the relationship between two or more variables. The measure of the explanatory power of the regression model is R2 (i.e. coefficient of determination).
correlation implies the cause and effect relationship,, but casuality doesn't imply correlation.
As grade point average increases, the number of scholarship offers increases (apex)
Ridge regression is used in linear regression to deal with multicollinearity. It reduces the MSE of the model in exchange for introducing some bias.
how can regression model approach be useful in lean construction concept in the mass production of houses
It is saying that two occurrences happening in sequence does not have to mean that the first event was the cause of the second event.
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
A quality-loss causation model shows different features for the loss and the causes of it. The categories for it are areas of correction, basic causes, immediate causes, incident, and loss.
A quality-loss causation model shows different features for the loss and the causes of it. The categories for it are areas of correction, basic causes, immediate causes, incident, and loss.
according to the army systems mdel of accident causation, wich of the now component of the syste,