In statistics, linear regression is an approach to modeling the relationship between a scalar variable y and one or more variables denoted X. In linear regression, models of the unknown parameters are estimated from the data using linear functions. Such models are called linear models. Most commonly, linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X. Less commonly, linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X, which is the domain of multivariate analysis
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Quadratic regression models are often used in economics areas such as utility function , forecasting, cost-befit analysis, etc. This JavaScript provides parabola regression model. This site also presents useful information about the characteristics of the fitted quadratic function.
In order to solve problems involving quadratic regression, it is necessary to
- know how to enter data into your graphing calculator for completing modeling problems
- know how to solve quadratic equations
- know how to calculate a quadratic equation that best fits a set of given data
- write and solve an equation for the problem
here are some examples of linear regression and quadratic regression by using Microsoft Excel :
Beer's Law Scatter Plot and Linear Regression
Tiltratiion of 50ml of 0.1 M HCl with 0.1 M NaOH
Linear Best Fit
Quadratic Regression
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