Advantages:
The estimates of the unknown parameters obtained from linear least squares regression are the optimal.
Estimates from a broad class of possible parameter estimates under the usual assumptions are used for process modeling.
It uses data very efficiently. Good results can be obtained with relatively small data sets.
The theory associated with linear regression is well-understood and allows for construction of different types of easily-interpretable statistical intervals for predictions, calibrations, and optimizations.
Disadvantages:
Outputs of regression can lie outside of the range [0,1].
It has limitations in the shapes that linear models can assume over long ranges
The extrapolation properties will be possibly poor
It is very sensitive to outliers
It often gives optimal estimates of the unknown parameters.
It all depends on what data set you're working with. There a quite a number of different regression analysis models that range the gambit of all functions you can think of. Obviously some are more useful than others. Logistic regression is extremely useful for population modelling because population growth follows a logistic curve. The final goal for any regression analysis is to have a mathematical function that most closely fits your data, so advantages and disadvantages depend entirely upon that.
You lose the skills and thinking abilitys you once had.
In cases wherethe dependent variable can take any numerical value for a given set of independent variables multiple regression is used.But in cases when the dependent variable is qualitative(dichotomous,polytomous)then logistic regression is used.In Multiple regression the dependent variable is assumed to follow normal distribution but in case of logistic regression the dependent variablefollows bernoulli distribution(if dichotomous) which means it will be only0 or 1.
+ 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.
Yes they can.
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Simple regression is used when there is one independent variable. With more independent variables, multiple regression is required.
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Advantages: The estimates of the unknown parameters obtained from linear least squares regression are the optimal. Estimates from a broad class of possible parameter estimates under the usual assumptions are used for process modeling. It uses data very efficiently. Good results can be obtained with relatively small data sets. The theory associated with linear regression is well-understood and allows for construction of different types of easily-interpretable statistical intervals for predictions, calibrations, and optimizations. Disadvantages: Outputs of regression can lie outside of the range [0,1]. It has limitations in the shapes that linear models can assume over long ranges The extrapolation properties will be possibly poor It is very sensitive to outliers It often gives optimal estimates of the unknown parameters.
Advantages of different types of storage mediums are having multiple backups in multiple locations that can be accessed from a variety of systems. Disadvantages are that they can be easily lost or stolen and may be more easily corruptible.
It all depends on what data set you're working with. There a quite a number of different regression analysis models that range the gambit of all functions you can think of. Obviously some are more useful than others. Logistic regression is extremely useful for population modelling because population growth follows a logistic curve. The final goal for any regression analysis is to have a mathematical function that most closely fits your data, so advantages and disadvantages depend entirely upon that.
You lose the skills and thinking abilitys you once had.
Advantages and Disadvantages of equity
In cases wherethe dependent variable can take any numerical value for a given set of independent variables multiple regression is used.But in cases when the dependent variable is qualitative(dichotomous,polytomous)then logistic regression is used.In Multiple regression the dependent variable is assumed to follow normal distribution but in case of logistic regression the dependent variablefollows bernoulli distribution(if dichotomous) which means it will be only0 or 1.
advantages and disadvantages
there are no advantages or disadvantages
advantages and disadvantages of recession