linear algebra has a no. of applications like in any power plant you put all the variables things in linear equations and change them according to your required output of power plant which is surely be the voltages and currents following in the output circuit.
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Linear regression can be used in statistics in order to create a model out a dependable scalar value and an explanatory variable. Linear regression has applications in finance, economics and environmental science.
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.
Linear means in order Non-Linear means Organic
Positive Linear Relationships are is there is a relationship in the situation. In some equations they aren't linear, but other relationships are, that's a positive linear Relationship.
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.