these days much of mathematics comes down to how well you know your calculator. Calculators are by far the greatest invention to come into my life. As many of you have already found out… It’s why I am what I am now. Statistical analysis is a pretty broad topic, covering things from standard deviation to sums to todays topic, SSE. Given a plot of data (x,y), the SSE stands for the Sum of the Squares of the Error, and they represent the failure of a given line to fit that data. If you jump into Excel or some other graphing utility and plot a series of data you may see a pattern emerge. Most utilities provide a method to find a line that fits the data (usually in the linear mx + b format), and you will obviously use this form today.

Lets get started…

First things first, put your data into your calculator

{1,2,2,3,4,5,5,6}->L1
{2,1,3,2,9,7,10,11}->L2

Okay, Now you will run the linear regression function and pass it these two lists

**STAT] > [>] > [CALC] > [4] LinReg(ax+b)**

LinReg(ax+b)

And your output should look like this:

LinReg
y=ax+b
a=2.068181818
b=-1.613636364

Alright, now we have to create list of the values of our regression function

**[Y=]**

Now we are going to drop our regression equation into the Y1 slot, so please clear it out. Then follow this step.

**[VARS] > [5] Statistics > [>] > [>] EQ > [1] RegEQ**

Now you should see the Y1 equation filled in with our function. Quit out of the Y= screen and get back to the command line.

**[VARS] > [>] Y-Vars > [1] Function… > [1] Y1**

You should see Y1 on your command line… follow these steps and you will soon have your SSE.

Y1(L1)->L3
L3-L2->L4
L4^2->L5
sum(L5)

DONE!

**21.77272727**

I have written a little application to do this calculation on demand and boy is it nifty!

*PS. the sum function is under List > Math*

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