Is the least-squares regression line resistant?
No, it is not resistant.
It can be pulled toward influential points.
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 line Given a linear regression equation of = 20 - 1.5x, where will the point (3, 15) fall with respect to the regression line? Below the line
(mean x, mean y) is always on the regression line.
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… Read More
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 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".
by regrsioning it.
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… Read More
correlation we can do to find the strength of the variables. but regression helps to fit the best line
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.
It is often called the "Least Squares" line.
line that measures the slope between dependent and independent variables
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.
once an equation for a regression is derived it can be used to predict possible future
A regression line.
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… Read More
In software testing regression testing must consist of fixed set of test to create a base line is it true or false?
Regression are classified as - Full / Complete Regression -- Entire application is regressed - Regional regression -- Tests performed around defect fixes or code changes
If the regression sum of squares is large relative to the error sum of squares is the regression equation useful for making predictions?
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… Read More
Finding the line of best fit is called linear regression.
If the coefficient of determination for a data set containing 12 points is 0.5 6 of the data points must lie on the regression line for the data set.?
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.
Your Mom HARHAR LOser
The point lies one unit above the regression line.
The sign is negative.
Linear regression looks at the relationship between two variables, X and Y. The regression line is the "best" line though some data you that you or someone else has collected. You want to find the best slope and the best y intercept to be able to plot the line that will allow you to predict Y given a value of X. This is usually done by minimizing the sum of the squares. Regression Equation is… Read More
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.
The point lies directly on the regression line.
The value depends on the slope of the line.
what is the equation of the regression line for the given data(Age, Number of Accidents) (16, 6605), (17, 8932), (18, 8506), (19, 7349), (20, 6458), (21, 5974)
How does a linear regression allow us to better estimate trends costs and other factors in complex situations?
You question is how linear regression improves estimates of trends. Generally trends are used to estimate future costs, but they may also be used to compare one product to another. I think first you must define what linear regression is, and what the alternative forecast methods exists. Linear regression does not necessary lead to improved estimates, but it has advantages over other estimation procesures. Linear regression is a mathematical procedure that calculates a "best fit"… Read More
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.
For a line graph, its equation is: y = mx + c where 'm' is the gradient of the line and 'c' is the intercept - which gives the value of y when x = 0. In linear regression, the line of best fit (y = α + βx where α is the intercept-term) is found so that the distance of each point from this line is a minimum. Sometimes people will go for a… Read More
Usually it refers to the straight line regression of a variable against time.
Suppose you have two variables X and Y, and a set of paired values for them. You can draw a line in the xy-plane: say y = ax + b. For each point, the residual is defined as the observed value y minus the fitted value: that is, the vertical distance between the observed and expected values. The least squares regression line is the line which minimises the sum of the squares of all the… Read More
Mohammad F. Qadir has written: 'Using percentile regression for estimating the maximum species richness line' -- subject(s): Statistical methods, Regression analysis, Species diversity
There is no line that shows the correlation between two data sets. The correlation is a variable that ranges between -1 and +1. You may be thinking about regression which, although related, is not the same thing. There is no line that shows the correlation between two data sets. The correlation is a variable that ranges between -1 and +1. You may be thinking about regression which, although related, is not the same thing. There… Read More
If you plot data points on a graph the rarely will form a straight line. Least squares is a method of finding a line 'close' to all the data points instead of just guessing and drawing a line that looks good. If you have a line, then there is an algebraic formula to find the distance from each point to that line. Then using statistics, you can make the statistically averaged distance from each data… Read More
of, pertaining to, or determined by regression analysis: regression curve; regression equation. dictionary.com
Not necessarily. In a scatter plot or regression they would not.
What measures the percentage of total variation in the response variable that is explained by the least squares regression line?
coefficient of determination
The point lies 1 unit below the regression line.
Let's say that you fit a simple regression line y = mx + b to a set of (x,y) data points. In a typical research situation the regression line will not touch all of the points; it might not touch any of them. The vertical difference between the y-co-ordinate of one of the data points and the y value of the regression line for the x-co-ordinate of that data point is called a residual. There… Read More
Unit regression testing Regional regression testing Full regression testing
You have a set of data points (x1,y1), (x2,y2), ..., (xn,yn), and you have assumed a line model, y = mx + b + e, where e is random error. You have fit the regression model to obtain estimates of the slope, m, and the intercept, b. Let me call them m and b. Now you can calculate yi - mxi - b for i = 1, 2, ... n. Notice that, for each i… Read More
There are numerous ways to do this. I think the easiest is to put the data in excel and have excel show the trend line, equation, andcorrelation coefficient. Excel gives you several options to choose for the trend line analysis. The other way is if it is a linear relationship, you can do the linear regression analysis following the steps listed in the related link. If you are not familiar with regression analysis, it may… Read More
You carry out regression which requires using formulae which are not easy to write in a non-mathematical browser. Consult any elementary statistical text book and look for linear regression. See the related link.
The point lies one unit below the regression line.
the negative sign on correlation just means that the slope of the Least Squares Regression Line is negative.
If the regression line is y equals 4 minus 3x then do the variables have a positive association or negative Or both?
The point lies 1 unit below the regression line.
Alpha is not generally used in regression analysis. Alpha in statistics is the significance level. If you use a TI 83/84 calculator, an "a" will be used for constants, but do not confuse a for alpha. Some may, in derivation formulas for regression, use alpha as a variable so that is the only item I can think of where alpha could be used in regression analysis. Added: Though not generally relevant when using regression for… Read More