The main contents of the chapter consist of the following: Script m-file, editor/debugger window, cell mode, using built-in functions, using the HELP feature , window HELP screen, elementary math functions, rounding functions, discrete mathematics, trigonometric function, data analysis function. | Lecture 21 Recap Script M-file Editor/Debugger Window Cell Mode Chapter 3 “Built in MATLAB Function” Using Built-in Functions Using the HELP Feature Window HELP Screen Elementary Math Functions Rounding Functions Discrete Mathematics Trigonometric Function Data Analysis Function Maximum and Minimum Mean and Median Sums and Products Sorting Values Matrix Size Variance and Standard Deviation The standard deviation and variance are measures of how much elements in a data set vary with respect to each other Every student knows that the average score on a test is important, but you also need to know the high and low scores to get an idea of how well you did. Test scores, like many kinds of data that are important in engineering, are often distributed in a “bell”-shaped curve In a normal (Gaussian) distribution of a large amount of data, approximately 68% of the data falls within one standard deviation (sigma) of the mean (one sigma) If the range is extended to a two-sigma variation ( two . | Lecture 21 Recap Script M-file Editor/Debugger Window Cell Mode Chapter 3 “Built in MATLAB Function” Using Built-in Functions Using the HELP Feature Window HELP Screen Elementary Math Functions Rounding Functions Discrete Mathematics Trigonometric Function Data Analysis Function Maximum and Minimum Mean and Median Sums and Products Sorting Values Matrix Size Variance and Standard Deviation The standard deviation and variance are measures of how much elements in a data set vary with respect to each other Every student knows that the average score on a test is important, but you also need to know the high and low scores to get an idea of how well you did. Test scores, like many kinds of data that are important in engineering, are often distributed in a “bell”-shaped curve In a normal (Gaussian) distribution of a large amount of data, approximately 68% of the data falls within one standard deviation (sigma) of the mean (one sigma) If the range is extended to a two-sigma variation ( two sigma), approximately 95% of the data should fall inside these bounds, and if you go out to three sigma, over 99% of the data should fall in this range Usually, measures such as the standard deviation and variance are meaningful only with large data sets. Normal Distribution Random Numbers Random numbers are often used in engineering calculations to simulate measured data Measured data rarely behave exactly as predicted by mathematical models, so we can add small values of random numbers to our predictions to make a model behave more like a real system Random numbers are also used to model games of chance Two different types of random numbers can be generated in MATLAB: Uniform random numbers Gaussian random numbers Uniform Random Numbers Uniform random numbers are generated with the rand function. These numbers are evenly distributed between 0 and 1 We can create a set of random numbers over other ranges by modifying the numbers created by the rand function For example: to create a set
