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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?
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.
Sounds like you are talking about a regression or regression analysis.
A regression line.
r = 0
Whenever you are given a series of data points, you make a linear regression by estimating a line that comes as close to running through the points as possible. To maximize the accuracy of this line, it is constructed as a Least Square Regression Line (LSRL for short). The regression is the difference between the actual y value of a data point and the y value predicted by your line, and the LSRL minimizes the sum of all the squares of your regression on the line. A Correlation is a number between -1 and 1 that indicates how well a straight line represents a series of points. A value greater than one means it shows a positive slope; a value less than one, a negative slope. The farther away the correlation is from 0, the less accurately a straight line describes the data.
The point lies 1 unit below the regression line.