# Introduction to Statistical Methods with MATLAB

## Course Overview

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Course Outline Exploring Data Visualize data sets, calculate descriptive group statistics, and explore data distributions.   Fitting a Curve to Data Perform linear and nonlinear regression to fit a curve

## Visualizing Data Sets

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Before trying to perform calculations or draw conclusions from data, it is helpful to get a qualitative feel for the data. Visualization is often a useful method when beginning to

## Measures of Centrality and Spread

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Measures of Centrality From the histograms of height data shown to the right, it appears that women’s heights are centered on approximately 160 cm, whereasmen’s heights are centered on approximately

## Distributions

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Histograms and Data Distributions  A histogram can give a qualitative feel for the shape of a data set. The exact shape of the distribution is given by the distribution’s probability

## Review – Exploring Data

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Visualizing Data histogram Bar plot of frequencies of data values. boxplot   Box-and-whisker plot based on median and quartiles. scatter  Plot relationship between two variables. Measures of Centrality and Spread Mean

## Linear Regression

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Suppose you suspect there is a relationship between two variables, x and y. The simplest relationship (and the one you can usually assume as a starting point) is that of a straight line,or y=ax+b.

## Evaluating Goodness of Fit

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How Well Does the Curve Fit the Data?  There are many ways of evaluating the quality of a fit. The fit function returns information on the quality of the fit

## Nonlinear Regression

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Linear vs. Nonlinear Linear regression can fit a nonlinear curve to the data, as long as the model is linear in the coefficients, or parameters. For example, fitting a cubic

## Review – Fitting a Curve to Data

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Linear and Nonlinear Regression The fit function can perform linear or nonlinear regression. Linear regression is when the model is linear in the parameters. For example: Y=p1x3+p2x2+p3x+p4 Otherwise, the regression is

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