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No it doesn't. Cause and effect is not demonstrated with regression, it only shows that the variables differ together. One variable could be affecting another or the affects could be coming from the way the data is defined.
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Can you obtain R-square from standardized regression model?
yes
Simple regression and multiple regression?
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 large relative to the error sum of squares is the regression equation useful for making predictions?
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).
In theory causation in fact is limitless true or false?
true
What is another name for line of best fit?
linear regression
What is the difference between correlation analysis and regression analysis?
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.
What is the difference between the logistic regression and regular regression?
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.
What is the relationship between correlation and causation?
correlation implies the cause and effect relationship,, but casuality doesn't imply correlation.
What is a measure of the explanatory power of the regression model?
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).
Which of these does correlation most likely imply causation?
As grade point average increases, the number of scholarship offers increases (apex)
Where ridge regression is used?
Ridge regression is used in linear regression to deal with multicollinearity. It reduces the MSE of the model in exchange for introducing some bias.
What does the modern accident causation model provide?
The modern accident causation model provides a more comprehensive understanding of accidents by considering multiple contributing factors beyond just human error, such as organizational, environmental, and systemic factors. It helps identify underlying root causes and systemic issues that can be addressed to prevent future accidents from happening.
What is the difference between classical regression analysis and spatial regression analysis?
how can regression model approach be useful in lean construction concept in the mass production of houses
What does correlation does not imply causation mean?
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.
What is quality loss causation model?
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.
What is quality-loss causation model?
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.
What does f statistic mean?
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.
