Understanding Notation for R^(n x m) and Its Use in Regression Analysis

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In summary, the discussion is about the notation R^(nxm) and whether it refers to a matrix or an n x m space. It is clarified that it refers to the vector space of n x m matrices with real entries. There is also a question about the notation X = (xij) and Y = (yij) and what they represent, to which the response is that they are typically used for matrices where xij represents an element of the matrix. It is unclear if they would be i x k and i x m matrices without more context.
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AndreTheGiant
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Homework Statement



So yeah as the topic says. What does R^(nxm) mean? Is that a matrix or an n x m space. The notation is really confusing and sometimes it means something else.

I'm doing regression analysis right now and this shows up a lot. Is it a matrix when you say something like C = ℝ^(k x m). I'm not sure if that means its a k x m matrix or a k x m space. Mainly because in examples when they say that, it shows up as a k x m matrix. I'm guessing its a a matrix because of the = sign? If you had an E sign instead it would be contained in a space?

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  • #2
AndreTheGiant said:

Homework Statement



So yeah as the topic says. What does R^(nxm) mean? Is that a matrix or an n x m space. The notation is really confusing and sometimes it means something else.

I'm doing regression analysis right now and this shows up a lot. Is it a matrix when you say something like C = ℝ^(k x m). I'm not sure if that means its a k x m matrix or a k x m space. Mainly because in examples when they say that, it shows up as a k x m matrix. I'm guessing its a a matrix because of the = sign? If you had an E sign instead it would be contained in a space?
It's the vector space of n X m matrices, with real entries.
 
  • #3
Thanks! That cleared up so much...

Another quick question if you may know.

If i have the observations. (xi1,...xik,yi1,...,yim). If i set X = (xij) and Y = (yij). What are those? Is that an i x k matrix for X and an i x m matrix for Y?
 
  • #4
AndreTheGiant said:
Thanks! That cleared up so much...

Another quick question if you may know.

If i have the observations. (xi1,...xik,yi1,...,yim).
xi1,...xik would be the i-th row of a matrix with k columns.
yi1,...,yim would be the i-th row of a matrix with m columns.
AndreTheGiant said:
If i set X = (xij) and Y = (yij).
What are those?
That notation is usually used for matrices, where xij represents an arbitrary element of the matrix. Same for yij.
AndreTheGiant said:
Is that an i x k matrix for X and an i x m matrix for Y?
Probably not i x k and i x m, but <something> x k and <something> by m. I can't tell from what you have here.
 

FAQ: Understanding Notation for R^(n x m) and Its Use in Regression Analysis

What does R^(n x m) mean?

R^(n x m) refers to the set of all real-valued matrices with n rows and m columns. The superscript "R" stands for the set of real numbers, and the (n x m) indicates the dimensions of the matrix.

How is R^(n x m) different from R^n?

R^(n x m) represents matrices with both row and column dimensions, while R^n represents vectors with only a single dimension. In other words, R^(n x m) is a set of matrices, while R^n is a set of vectors.

Can the dimensions of R^(n x m) be different?

Yes, the dimensions of R^(n x m) can vary depending on the specific problem or context. For example, a matrix with 3 rows and 4 columns would be denoted as R^(3 x 4), while a matrix with 2 rows and 7 columns would be denoted as R^(2 x 7).

How is R^(n x m) used in mathematics and science?

R^(n x m) is used to represent and manipulate data in various fields such as linear algebra, statistics, and physics. Matrices can be used to model and solve a wide range of problems, including systems of equations, transformations, and data analysis.

Are there any special properties of R^(n x m)?

Yes, R^(n x m) has certain properties that are useful in mathematical operations. For example, matrices can be added, multiplied, and inverted, and these operations follow specific rules and properties. Additionally, matrices can be used to represent and perform transformations such as rotations, reflections, and scaling.

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