A linear combination is a combination of several variables (or vectors) such that no variable (or vector) is multiplied by either itself or another: they may be multiplied by constants, and are combined by simple addition or subtraction.

A linear combination of x, y and z can always be written ax + by + cz, where a, b and c are constants. Note that a and b might be positive numbers, but either or both of them could also be negative or zero.

## Importance of Linear Combinations in Statistics

Linear combinations are often the first choice for a statistics model because they are simple, easy to work with, and easy to do calculations for. They also provide a fairly accurate model for many real life situations.

If you know the sample means, variance, and covariance of the individual variables you can compute the mean and variance of a linear combination.

Suppose you calculated the population mean of every variable in your linear combination. Then the population mean for your linear combination will be just the linear combination of the component variables. So if your variables x, y and z each had population means l, m, and n, the population mean of the linear combination ax + by + cz would be just al + bm + cn.

Finding the variance and covariance of linear combinations is only slightly more complicated, and involves calculating a double sum over the variables.

## References

Korch, Michal. Teaching, Linear Algebra WNE UW 3: Vector Spaces and Linear Combinations. Published Online October 10, 2015. Retrieved from https://www.mimuw.edu.pl/~m_korch/3-vector-spaces-linear-combinations/ on August 7, 2018.

Belk, James. Linear Combinations and Span. Math 213 Class Notes. Retrieved from http://faculty.bard.edu/belk/math213s14/LinearCombinationsAndSpanRevised.pdf on August 7, 2018.

Weisstein, Eric W. “Linear Combination.” From MathWorld–A Wolfram Web Resource. Retrieved from http://mathworld.wolfram.com/LinearCombination.html on August 7, 2018.

Penn State University. Lesson 2: Linear Combinations of Random Variables. From STAT 505: Applied Multivariate Statistical Analysis. Retrieved from https://onlinecourses.science.psu.edu/stat505/node/11/ on August 7, 2018.

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