Linear algebra deals with mathematical transformations that are linear. By definition they must preserve scalar multiplication and additivity.
T(u+v)= T(u) + T(v)
T(R*u)=r*T(u) Where "r" is a scalar
For example. T(x)=m*x where m is a scalar is a linear transform. Because
T(u+v)=m(u+v) = mu + mv = T(u) + T(v)
T(r*u)=m(r*u)=r*mu=r*T(u)
A consequence of this is that the transformation must pass through the origin.
T(x)=mx+b is not linear because it doesn't pass through the origin. Notice at x=0, the transformation is equal to "b", when it should be 0 in order to pass through the origin. This can also be seen by studying the additivity of the transformation.
T(u+v)=m(u+v)+b = mu + mv +b which cannot be rearranged as T(u) + T(v) since we are missing a "b". If it was mu + mv + b + b it would work because it could be written as (mu+b) + (mv+b) which is T(u)+T(v). But it's not, so we are out of luck.
Strength and direction of linear relation. Closer to 1 is positive linear association, closer to -1 is positive negative association and closer to 0 means no linear relation. Remember that 0 does not mean that there is no relation - just no linear relation.
the sample mean is used to derive the significance level.
When you use linear regression to model the data, there will typically be some amount of error between the predicted value as calculated from your model, and each data point. These differences are called "residuals". If those residuals appear to be essentially random noise (i.e. they resemble a normal (a.k.a. "Gaussian") distribution), then that offers support that your linear model is a good one for the data. However, if your errors are not normally distributed, then they are likely correlated in some way which indicates that your model is not adequately taking into consideration some factor in your data. It could mean that your data is non-linear and that linear regression is not the appropriate modeling technique.
as one variable increases the other variable decreases
b. Calculating the mean cost of one individual unit in a production run of 10,000 units
Linear means a straight line.
In linear algebra, there is an operation that you can do to a matrix called a linear transformation that will get you answers called eigenvalues and eigenvectors. They are to complicated to explain in this forum assuming that you haven't studied them yet, but their usefulness is everywhere in science and math, specifically quantum mechanics. By finding the eigenvalues to certain equations, one can come up with the energy levels of hydrogen, or the possible spins of an electron. You really need to be familiar with matrices, algebra, and calculus though before you start dabbling in linear algebra.
There are many different areas within algebra: linear algebra, algebraic structures, algebraic geometry, vector algebra and so on. Some properties are valid in only some of these and not in others. You need to understand what the properties mean and perhaps keep in mind one or two examples where the property is valid.
In Algebra, "is" means Equal (=).
Igual>>> ALGEBRA
"And" in algebra usually means addition.
It usually refers to an introductory algebra class.
It mean to Subtract (-)
D can mean anything, it's only substituting a number that you have to find out. That's how you do basic algebra
If your in English Class it Means that you should be in your Algebra Class.
In algebra, 8n means to multiply n and 8. (8xN)
algebra involves equations with numbers a variables and your goal is to solve for the variable