Already have an account? Get multiple benefits of using own account!
Login in your account..!
Remember me
Don't have an account? Create your account in less than a minutes,
Forgot password? how can I recover my password now!
Enter right registered email to receive password!
Regression Lines
It has already been discussed that there are two regression lines and they show mutual relationship between two variable . The regression line Yon X gives the most probable value of y of given value of x whereas the regression line x on y gives the most probable values of y
Why there are two Regression Lines:
First Reason: For two mutually related series there are two regression lines. First line of regression is X on Y and second line of regression is X on Y.
While constructing line of regression of X on Y, Y is treated as independent variable whereas X is treated as dependent variable. This line gives most probable values of X for given values of X for given values of Y. In the same way line of regression of Y on variable. This line gives the most probable values of Y for given values of X. Practically X and Y both variables may be required to be estimated, hence there is necessity of two regression lines. One for best estimation of X and other for Y
Second Reason: The regression lines are those best fit lines which are drawn on least squares assumption. Under least square method the line which are to be drawn should be in that manner so that the total of the squares of the deviations of the various points is minimum. The deviation of the various points of actual values up to the regression online can be measured by two ways (a) Horizontally i.e. parallel to X axis and (b) Vertically i.e. parallel to Y axis .Hence for minimising the total of squares separately there should be two regression lines.
The regression line Y and X is drawn in such a way that it minimises total of squares of the vertical deviations. In the same way regression line X on Y is drawn in such a way that it minimises the total squares of the horizontal deviations. Hence it is essential to have two regressio line under the assumptions of least square method.
1. For each of the following variables: major, graduate GPA, and height: a. Determine whether the variable is categorical or numerical. b. If the variable is numerical, deter
Simple Regression: The Teacher Preparation Research Team conducted a study of college students who took the Praxis II-a teacher certification examination. Some variables from
The Truly Canadian Restaurant stocks a private red table wine that it purchases from a local winery in the Niagara Falls region. The daily demand for the wine at the restaurant is
Q. Find the inverse Laplace transform of Y (s) = s-4/s 2 + 4s + 13 +3s+5/s 2 - 2s -3. Q. Use the Laplace transform to solve the initial value problem y''+ y = cos(3t), y(0) =
Investigate the use of fixed and percentile meshes when applying chi squared goodness-of-t hypothesis tests. Apply the oversmoothing procedure to the LRL data. Compare the res
Ask queFrom these studies, which of the following may be considered a variable that can have a probability distribution? [I] Percentage of Sub-Saharan Africans that smoke [II] Perc
Q. Compute the output of correlation? The following figure shows (a) a 3-bit image of size 5-by-5 image in the square, with x and y coordinates specified, (b) a Laplacian
Ask Describe What-if Analysis
1. If you are calculating a correlation coefficient testing the relationship between height and weight, state the null and alternative hypotheses. 2. What kind of relationship d
Properties of correlation
Get guaranteed satisfaction & time on delivery in every assignment order you paid with us! We ensure premium quality solution document along with free turntin report!
whatsapp: +91-977-207-8620
Phone: +91-977-207-8620
Email: [email protected]
All rights reserved! Copyrights ©2019-2020 ExpertsMind IT Educational Pvt Ltd