Math and Arithmetic
Statistics

Is the least-squares regression line resistant?

293031

Top Answer
User Avatar
Wiki User
2009-10-20 04:33:54
2009-10-20 04:33:54

No, it is not resistant.
It can be pulled toward influential points.

1
๐Ÿ™
0
๐Ÿคจ
0
๐Ÿ˜ฎ
0
๐Ÿ˜‚
0

Related Questions

User Avatar

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

User Avatar

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

User Avatar

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.

User Avatar

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.

User Avatar

Regression techniques are used to find the best relationship between two or more variables. Here, best is defined according to some statistical criteria. The regression line is the straight line or curve based on this relationship. The relationship need not be a straight line - it could be a curve. For example, the regression between many common variables in physics will follow the "inverse square law".

User Avatar

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

User Avatar

It is often called the "Least Squares" line.

User Avatar

line that measures the slope between dependent and independent variables

User Avatar

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.

User Avatar

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.

User Avatar

once an equation for a regression is derived it can be used to predict possible future

User Avatar

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.

User Avatar

Regression are classified as - Full / Complete Regression -- Entire application is regressed - Regional regression -- Tests performed around defect fixes or code changes

User Avatar

It guarantees that the slope and intercept are minimized.

User Avatar

Finding the line of best fit is called linear regression.

User Avatar

If the regression sum of squares is the explained sum of squares. That is, the sum of squares generated by the regression line. Then you would want the regression sum of squares to be as big as possible since, then the regression line would explain the dispersion of the data well. Alternatively, use the R^2 ratio, which is the ratio of the explained sum of squares to the total sum of squares. (which ranges from 0 to 1) and hence a large number (0.9) would be preferred to (0.2).

User Avatar

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.

User Avatar

of, pertaining to, or determined by regression analysis: regression curve; regression equation. dictionary.com

User Avatar

The point lies one unit above the regression line.

User Avatar

Unit regression testing Regional regression testing Full regression testing

User Avatar

The point lies directly on the regression line.

User Avatar

The value depends on the slope of the line.

User Avatar

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.

User Avatar

G-Shock is a sportswatch line by Casio that are designed to be water-resistant and shock (impact) resistant.

User Avatar

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.


Copyright ยฉ 2020 Multiply Media, LLC. All Rights Reserved. The material on this site can not be reproduced, distributed, transmitted, cached or otherwise used, except with prior written permission of Multiply.