write the formula
for Regression
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Linear regression is a way to model the relationship between two variables. ... The equation has the form Y= a + bX, where Y is the dependent variable (that's the variable that goes on the Y axis), X is the independent variable (i.e. it is plotted on the X axis), b is the slope of the line and a is the y-intercept.
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The Linear Regression Equation
Linear regression is a way to model the relationship between two variables. You might also recognize the equation as the slope formula. The equation has the form Y= a + bX, where Y is the dependent variable (that’s the variable that goes on the Y axis), X is the independent variable (i.e. it is plotted on the X axis), b is the slope of the line and a is the y-intercept.
the linear regression equation
The first step in finding a linear regression equation is to determine if there is a relationship between the two variables. This is often a judgment call for the researcher. You’ll also need a list of your data in x-y format (i.e. two columns of data—independent and dependent variables).
Warnings:
Just because two variables are related, it does not mean that one causes the other. For example, although there is a relationship between high GRE scores and better performance in grad school, it doesn’t mean that high GRE scores cause good grad school performance.
If you attempt to try and find a linear regression equation for a set of data (especially through an automated program like Excel or a TI-83), you will find one, but it does not necessarily mean the equation is a good fit for your data. One technique is to make a scatter plot first, to see if the data roughly fits a line before you try to find a linear regression equation.
How to Find a Linear Regression Equation: Steps
Step 1: Make a chart of your data, filling in the columns in the same way as you would fill in the chart if you were finding the Pearson’s Correlation Coefficient.
SUBJECT AGE X GLUCOSE LEVEL Y XY X2 Y2
1 43 99 4257 1849 9801
2 21 65 1365 441 4225
3 25 79 1975 625 6241
4 42 75 3150 1764 5625
5 57 87 4959 3249 7569
6 59 81 4779 3481 6561
Σ 247 486 20485 11409 40022
From the above table, Σx = 247, Σy = 486, Σxy = 20485, Σx2 = 11409, Σy2 = 40022. n is the sample size (6, in our case).
Step 2: Use the following equations to find a and b.
find a linear regression equation
a = 65.1416
b = .385225
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