Regression.
The point lies 1 unit below the regression line.
False
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.
Possible maybe
In a regression of a time series that states data as a function of calendar year, what requirement of regression is violated?
The logistic regression "Supervised machine learning" algorithm can be used to forecast the likelihood of a specific class or occurrence. It is used when the result is binary or dichotomous, and the data can be separated linearly. Logistic regression is usually used to solve problems involving classification models. For more information, Pls visit the 1stepgrow website.
M.H Pesaran has written: 'Dynamic regression' -- subject(s): Regression analysis, Data processing
That is not true. It is possible for a data set to have a coefficient of determination to be 0.5 and none of the points to lies on the regression line.
Linear Regression is a method to generate a "Line of Best fit" yes you can use it, but it depends on the data as to accuracy, standard deviation, etc. there are other types of regression like polynomial regression.
Regression.
Not necessarily. Qualitative data could be coded to enable such analysis.
Using real-world data from a data set, a statistical analysis method known as logistic regression predicts a binary outcome, such as yes or no. A logistic regression model forecasts a dependent data variable by examining the correlation between one or more existing independent variables. Please visit for more information 1stepgrow.
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.
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.
Sounds like you are talking about a regression or regression analysis.
A regression line.