Introduction
Excel is a spreadsheet application written and distributed by Microsoft for Microsoft Windows and Mac OS X.Its features includes calculation ,and graphing tools,and also It has been a very widely applied spreadsheet for these platforms, especially since version 5 in 1993.It is useful in these days especially when you are working in companies.
Linear Regression
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 thedata using linear function
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 ygiven 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.
Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications. This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.
Linear regression has many practical uses. Most applications of linear regression fall into one of the following two broad categories:
- If the goal is prediction, or forecasting, linear regression can be used to fit a predictive model to an observed data set of y and X values. After developing such a model, if an additional value of X is then given without its accompanying value of y, the fitted model can be used to make a prediction of the value of y.
- Given a variable y and a number of variables X1, ..., Xp that may be related to y, then linear regression analysis can be applied to quantify the strength of the relationship between yand the Xj, to assess which Xj may have no relationship with y at all, and to identify which subsets of the Xj contain redundant information about y, thus once one of them is known, the others are no longer informative.
Linear regression models are often fitted using the least squares approach, but they may also be fitted in other ways, such as by minimizing the “lack of fit” in some other norm, or by minimizing a penalized version of the least squares loss function as in ridge regression. Conversely, the least squares approach can be used to fit models that are not linear models. Thus, while the terms “least squares” and linear model are closely linked, they are not synonymous.
Quadratric Regression
Quadratic regression, like linear regression, is finding the quadratic equation that best fits a set of data. To do this, one must have at least three points, and it is best if they have a sort of quadratic correlation.
A set of data has a quadratic correlation if when the data points are plotted, the trend increases or decreases exponentially or the data rises then falls or falls then rises.| Plot the Points |
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| This is the data set {(1,4);(2,7);(4,12);(5,20);(10,6)}. |
To find the quadratic of best fit, it is best to use a calculator, since the algorithm for finding such an equation would be too complex and time consuming. Although I prefer TI, our school uses Casio, so I shall outline what to do for that calculator.
http://timothyfoster.wikidot.com/notebook:7
- Go to the Table and input the data. Independent goes into L1, and dependent goes into L2.
- Go to the Statistics, and press Graph, Grpah1.
- It appears as a graph. The types of regression models are found at the bottom of the screen.
- Press F3 for the x^2 regression (quadratic).
- The equation appears, and you can copy and draw it from there.
