Multiple Linear Regression (2 factors, 1 output)

In summary, The speaker is working on a job project involving creating a linear regression with the form Y = a * x1 + b * x2 + c. They have a picture of their data set with grayed out space that was not tested and are attempting to find the parameters using the formula \beta = (X'X)^{-1}X'Y. They are seeking help to see if their approach is possible.
  • #1
ChaoticLlama
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Hello all;

I'm doing work for my job, and I've forgotten my statistics =(.

I first want to know if what I'm trying to do is possible.

I want to create a linear regression of the form Y = a * x1 + b * x2 + c.

http://imgur.com/Q4vGP"

As you can see, there is space that is grayed out (those tests were not performed). I was originally instructed to run my experiment by holding x1 constant and varying x2, and holding x2 constant and varying x1. (I made a dummy data set to make it easier to analyze).

I'm attempting to find the parameters (a,b, & c) using the trusty formula [tex]\beta = (X'X)^{-1}X'Y[/tex].

Thanks for your help!
 
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  • #2
http://imgur.com/mciGw"
 
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FAQ: Multiple Linear Regression (2 factors, 1 output)

What is multiple linear regression?

Multiple linear regression is a statistical method used to analyze the relationship between multiple independent variables and one dependent variable.

What are the assumptions of multiple linear regression?

The assumptions of multiple linear regression include: linearity, normality, homoscedasticity, independence of errors, and absence of multicollinearity.

How is the strength of the relationship between variables measured in multiple linear regression?

The strength of the relationship between variables is measured by the coefficient of determination (R-squared). This value ranges from 0 to 1, with a higher value indicating a stronger relationship.

What is the purpose of using multiple linear regression?

The purpose of multiple linear regression is to understand the relationship between a dependent variable and multiple independent variables, and to make predictions or estimates based on this relationship.

What is the difference between simple linear regression and multiple linear regression?

Simple linear regression involves only one independent variable, while multiple linear regression involves two or more independent variables. Multiple linear regression allows for a more complex understanding of the relationship between variables, but also requires more data and assumptions to be met.

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