Multiple Regression (Statistics, SPSS)?

Question

Multiple Regression (Statistics, SPSS)?

Answer 1

when you want to check whether one variable has any relationship with several others.

Answer 2

You can do multiple regeressional analysis by SPSS very much easily if you know this software well. but if you don't know it, you should take some help from the SPSS research services to make this job easy for you. They are all the professionals and can help you well.

Answer 3

You would use multiple regression statistics when you are pretty sure that a set of data is a function of more than one variable, or can be better represented by a polynomial than by other functions. for example, the cost of owning an automobile can be represented as a function of initial price, time, distance traveled, hours of engine running time, and number of engine starts. Regressing on each if these variables independently will not give you a true picture of othe cost dependency, but a multiple linear regression will yield a clearer picture.

You can postulate that

F = a + bw + cx + dy + ez

where

F = running total of money spent

a = initial cost (including "hidden" initial costs of other variables)

w = mileage

x = time of ownership

y = engine hours (from an hour meter), and

z = engine starts (from a counter on the ignition)

You build your database, of course, by recording all the variables each time you have an expenditure, along with the expenditure.

solution of the regression is as follows:

| n.. ∑w... ∑x... ∑y... ∑z.. . | |a| |∑F|

|∑w ∑w^2 ∑wx. ∑wy. ∑wz.. | |b| |∑wF|

|∑x. ∑wx.. ∑x^2 ∑xy.. ∑xz.. | |c| |∑xF|

|∑y. ∑wy. ∑xy.. ∑y^2 ∑yz.. | |d| |∑yF|

|∑z. ∑wz.. ∑xz.. ∑yz.. ∑z^2 | |e| |∑eF|



This sets up easily on a spreadsheet for solution by elimination.



So far, I've not come up with an analog for the correlation factor, so I add columns to the spreadsheet to compare predicted values to actual values, and take the standard deviation of the error as a sort of measure of fit.



Graphing and visualizations are hard to come by.



edit:

Sorry, the SPSS didn't register until after I typed all the above drivel.

Answer 4

The primary use of regression is to test whether the betas affect the depedent variable. Correlation (checking whether the betas are related to each other) is a large part of it and the source of many errors, but not the only important element of regression. Residual analysis (magnitude of errors over the sample) and testing whether individual betas themselves are significant are also critical to developing an accurate model.

Answer 5

You would use multiple regression to test how multiple independent variables predict a dependent variable. LIke others said, the independent variables are denoted by the betas. For example regression best fit line example would be y = (beta1)*x1 + (beta2)*x2 + intercept...you can add more independent variables if necessary. In single regression it is only y= (beta1)*x + intercept. Or one indepedent variable. SPSS is a software that you can use to run a multiple regression. Partial Correlations can be made between the independent variables.