1. Regression
Regression: Mathematical method for determining the best equation that reproduces a data set
Linear Regression: Regression method applied with a linear model (straight line)
Uses
Prediction of new X,Y values
Understanding data behavior
Verification of hypotheses/physical laws

2. Regression The Linear Model Y = mX + b Y = Dependent variable
X = Independent variable
m = slope = DY/DX
b = y-intercept (point where line crosses y-axis at x=0)
X1=1, Y1=2.4
X2=20, Y2=10 DX DY

3. Regression
Fitting the data: finding the equation for the straight line that does the best job of reproducing the data.

4. Regression
Residual: Difference between measured and calculated Y-values

5. Regression Analysis
Use the least square method to “best fit” a straight line through the data points.
A straight line is described by its slope and “y”-intercept in a x-y plot.
Need to determine the numerical values of the slope and the “y”-intercept from the data.
This is equivalent to adding a trendline to your scatter plot in EXCEL.

6. Regression Analysis
The least square method consists of defining a difference, called the residual, between the regression line and a data point along a measured “x” value.
Then add up the squared residuals for all data points.
Adjusting the slope and the “y”-intercept of the regression line so that the sum of squared residuals, called regression error, has the smallest value.

7. Regression Analysis
The covariance appears in the calculation of the correlation coefficient between the measurements of two variables.
Let us denote the two variables as “x” and “y”.
Their measurements are the “x” data set and the “y” data set.

8. Regression Analysis
The slope of the regression line is given by the ratio of the covariance between the “x” and “y” data sets and of the variance of the “x” data set.
You then use the equation of the line to determine the y-intercept. You MUST use the mean of x and the mean of y for this equation since your data points are likely not on the regression line.

9. Regression Analysis
Once we determined the slope and the “y” intercept of the regression line, we have a mathematical relation that ties the “x” variable to the “y” variable.
We can use this relation to predict values of “y” given a “x” value that are not on the data sets.

10. Regression Analysis
Interpolation – the process by which we use the regression line to predict a value of the “y” variable for a value of the “x” variable that is not one of the data points but is within the range of the data set.
The “x” and “y” points will lie on the regression line.

11. Regression Analysis
Extrapolation – the process by which we use the regression line to predict a value of the “y” variable for a value of the “x” variable that is outside of the range of the data set.
The “x” and “y” points also lie on the regression line but outside of the range of the data set.

12. Tricks of the Trade
A curve can be partitioned into sections and “best” fitted a different curve in each section.
Use scaling as a mean to increase the accuracy of the “fitted” curve.

13. Multivariate Analysis Regression
Prediction: Once the best fit line has been determined, the equation can be used to predict new values of Y for any given X and vice versa. (Interpolation/Extrapolation)
y = x
If a states % of the population with a college degree is 20%, then they can expect an average income level of
y = (20) = $26,250
If a states average income level is $30,000, then what % of its population has a college degree?
x = (30,000 – 10810)/ = 24.9%

14. Multivariate Analysis
Excel Functions and Tools
SLOPE() - Returns the slope when passed X, Y data..
INTERCEPT() - Returns the intercept when passed X, Y data..
LINEST() - Returns the slope and intercepts when passed X, Y data..
TREND() - Returns predicted values in a linear trend when passed X, Y data..
Trendline (from the Chart menu) Returns the trendline, equation, and correlation coefficient for a set of X,Y data.