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It is called the line of best fit because it tends to satisfy all the possible points in consideration at the same time with minimal variation.

Q: Why you call regression line as line of fit?

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yes.

False.

A trend line is graphed from a linear, exponential, logarithmic or other equation, and trys to fit the sorted data that you have. But it may or may not be correlated. The line of best fit is the trend line that best fits your data, having a high correlation. R closer to 1.

A straight line equation

(a) Correlation coefficient is the geometric mean between the regression coefficients. (b) If one of the regression coefficients is greater than unity, the other must be less than unity. (c) Arithmetic mean of the regression coefficients is greater than the correlation coefficient r, provided r > 0. (d) Regression coefficients are independent of the changes of origin but not of scale.

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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.

linear regression

Finding the line of best fit is called linear regression.

A correlation coefficient is a value between -1 and 1 that shows how close of a good fit the regression line is. For example a regular line has a correlation coefficient of 1. A regression is a best fit and therefore has a correlation coefficient close to one. the closer to one the more accurate the line is to a non regression line.

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.

correlation we can do to find the strength of the variables. but regression helps to fit the best line

The answer depends on the quantities and the nature of the relationship. It can be a line-of-best-fit (or regression line), or a formula.

(mean x, mean y) is always on the regression line.

It will minimise the sum of the squared distances from the points to the line of best fit, measured along the axis of the dependent variable.

A line of best fit is a technique used in statistics. It is a line that represents the relationship between data points showing two variables. It is "best" according to some user-specified criteria. The least squares regression line is the most popularly used line of best fit but it is not the only option.

There are two regression lines if there are two variables - one line for the regression of the first variable on the second and another line for the regression of the second variable on the first. If there are n variables you can have n*(n-1) regression lines. With the least squares method, the first of two line focuses on the vertical distance between the points and the regression line whereas the second focuses on the horizontal distances.

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