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Regression Analysis: Mathematical Methods in Data Modeling

Regression analysis is a statistical method for determining the best-fitting equation to model a data set. It involves linear regression, prediction of new values, verification of hypotheses, and fitting data points to a straight line to minimize errors. Using the least squares method, you can calculate the slope, y-intercept, and make predictions for values within or beyond the data range through interpolation and extrapolation. Learn the tricks of the trade to enhance curve fitting accuracy and utilize Excel functions like SLOPE(), INTERCEPT(), LINEST(), and TREND(). Multivariate analysis further explores regression in Excel for comprehensive data analysis.

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Regression Analysis: Mathematical Methods in Data Modeling

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  1. 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 Regression

  2. DY DX 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

  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. 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 = 772.03x + 10810 If a states % of the population with a college degree is 20%, then they can expect an average income level of y = 772.03(20) + 10810 = $26,250 If a states average income level is $30,000, then what % of its population has a college degree? x = (30,000 – 10810)/772.03 = 24.9% Multivariate AnalysisRegression

  14. 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. Multivariate Analysis

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