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By Forman Sinnickson Acton

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This behavior is analogous to measurement in the presence of noise, so random variables are ideal for representing noisy data. Even when the random variable, d, is in the box, we may know something about it. It may have a tendency to take on certain values more often than others. For example, suppose that d represents the number of H (hydrogen) atoms in a CH4 (methane) molecule that are of the heavy variety called deuterium. Then d can take on only the discrete values 0 through 4, with d ¼ 0 representing the no deuterium state and d ¼ 4 representing the all deuterium state.

8 Examples of the Normal probability density functions. (Left) Normal probability density functions with the same variance (s2 ¼ 52) but different means. (Right) Normal probability density functions with the same mean (20) but different variances. MatLab scripts eda03_06 and eda03_07. 2). Environmental Data Analysis with MATLABW 48 On the other hand, the Normal probability density function does have limitations. One limitation is that it is defined on the unbounded interval À1 < d < þ1, while in many instances data are bounded.

We then copy time into the column vector t, and temperature into the column vector d. Knowing how much data was actually read is useful, so we query the size of D with the size() function. It returns a vector of the number of rows and columns, which we break out into the variables L and M and display. MatLab informs us that we read in a table of L ¼ 110430 rows and M ¼ 2 columns. 6 years of data, at one observation per hour. 0417 of a day. The temperature data seems to have been recorded with the precision of hundredths of a  C.

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