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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.
Linear regression model?
y(i) = a + b1.x1(i) + b2.x2(i) + b3.x3(i) + ... + bk.xk(i) + e(i)where i = 1, 2, ... n are n observations ofthe independent variables x1, x2, ... xk,y is the dependent variablea and the b are regression parameters.The e are independent, identically distributed random variables (representing the error).
What are some of the advantages and disadvantages of making forecasts using regression methods?
+ Linear regression is a simple statistical process and so is easy to carry out. + Some non-linear relationships can be converted to linear relationships using simple transformations. - The error structure may not be suitable for regression (independent, identically distributed). - The regression model used may not be appropriate or an important variable may have been omitted. - The residual error may be too large.
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 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 are the sources of error in regression model?
Random error, measurement error, mis-specification of model (overspecification or underspecification), non-normality, plus many more.
In a good regression model the residual plot shows?
a random pattern
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.
What does a large 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.
What is the normal probability plot of residuals?
When you use linear regression to model the data, there will typically be some amount of error between the predicted value as calculated from your model, and each data point. These differences are called "residuals". If those residuals appear to be essentially random noise (i.e. they resemble a normal (a.k.a. "Gaussian") distribution), then that offers support that your linear model is a good one for the data. However, if your errors are not normally distributed, then they are likely correlated in some way which indicates that your model is not adequately taking into consideration some factor in your data. It could mean that your data is non-linear and that linear regression is not the appropriate modeling technique.
Linear regression model?
y(i) = a + b1.x1(i) + b2.x2(i) + b3.x3(i) + ... + bk.xk(i) + e(i)where i = 1, 2, ... n are n observations ofthe independent variables x1, x2, ... xk,y is the dependent variablea and the b are regression parameters.The e are independent, identically distributed random variables (representing the error).
What does it mean to say that a data point has a residual of zero?
If a data point has a residual of zero, it means that the observed value of the data point matches the value predicted by the regression model. In other words, there is no difference between the actual value and the predicted value for that data point.
Why are your predictions inaccurate using a linear regression model?
There are many possible reasons. Here are some of the more common ones: The underlying relationship is not be linear. The regression has very poor predictive power (coefficient of regression close to zero). The errors are not independent, identical, normally distributed. Outliers distorting regression. Calculation error.
What are some of the advantages and disadvantages of making forecasts using regression methods?
+ Linear regression is a simple statistical process and so is easy to carry out. + Some non-linear relationships can be converted to linear relationships using simple transformations. - The error structure may not be suitable for regression (independent, identically distributed). - The regression model used may not be appropriate or an important variable may have been omitted. - The residual error may be too large.
What is a stochastic error?
In a statistical model, variations in the dependent variable can be attributed to independent variables. However, there is a random element that is not accounted for and this is the stochastic error.
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 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).
