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Q: Is there any relation between regression and linear equation?
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Related questions

What is regression coefficient and correlation coefficient?

The strength of the linear relationship between the two variables in the regression equation is the correlation coefficient, r, and is always a value between -1 and 1, inclusive. The regression coefficient is the slope of the line of the regression equation.


Given a linear regression equation of equals 20 - 1.5x where will the point 3 15.5 fall with respect to the regression line?

on the lineGiven a linear regression equation of = 20 - 1.5x, where will the point (3, 15) fall with respect to the regression line?Below the line


What is Full Regression?

Regression :The average Linear or Non linear relationship between Variables.


Is the regression equation a mathematical equation that defines the relationship between two variables?

No. It is an estimated equation that defines the best linear relationship between two variables (or their transforms). If the two variables, x and y were the coordinates of a circle, for example, any method for calculating the regression equation would fail hopelessly.


The value 11.7 represents the of the graph of the following linear regression equation?

slope


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 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 are the application of linear equation?

They are used in statistics to predict things all the time. It is called linear regression.


What are linear and nonlinear regression?

A linear equation is an equation that in math. It is a line. Liner equations have no X2. An example of a linear equation is x-2 A linear equation also equals y=mx+b. It has a slope and a y-intercept. A non-linear equation is also an equation in math. It can have and x2 and it is not a line. An example is y=x2+3x+4 Non linear equations can be quadratics, absolute value or expodentail equations.


What is the relation between straight lines and linear equations?

The graph, in the Cartesian plane, of a linear equation is a straight line. Conversely, a straight line in a Cartesian plane can be represented algebraically as a linear equation. They are the algebraic or geometric equivalents of the same thing.


What characteristic makes regression line of best fit?

The equation of the regression line is calculated so as to minimise the sum of the squares of the vertical distances between the observations and the line. The regression line represents the relationship between the variables if (and only if) that relationship is linear. The equation of this line ensures that the overall discrepancy between the actual observations and the predictions from the regression are minimised and, in that respect, the line is the best that can be fitted to the data set. Other criteria for measuring the overall discrepancy will result in different lines of best fit.


What does the y-intercept represent in a linear regression equation?

It represents the value of the y variable when the x variable is zero.