Course Content

Matlab Basics

1. Basic Syntax and Coding with a Text Editor

Matlab's Place and Dark Mode The Command Window Installing a Text Editor and Its Packages Finishing Off the Text Editor Creating Snippets and Writing Programs

2. Coding Foundations

Messin with Matrices Importing and Exporting Data to and from Excel For Loops If Statements Modular Programming

3. Learning Through Applications

Application: Nuclear Plant Data Analysis Application: Medical Data Analysis Generating Combinations Application: Logistics Problem The Sortrows and Find Functions

4. Visualizations

Graphing Basics Automating Graph Creation Application: Calculating 3D Trajectories While Loops Application: Graphing 3D Trajectories and Multiple Plots System Function

5. Recursion and Matrix Multiplication

Recursive Programming Understanding Matrices and Matrix Multiplication Applying Matrix Multiplication: Transforming Vectors and Coordinate Systems Applying Matrix Multiplication: Solving Systems of Equations Applying Matrix Multiplication: Derivatives and Integrals Career Prospects for a MATLAB Programmer

Application: Nuclear Plant Data Analysis

Apply what you've learned to analyze power wastewater generation data coming from three nuclear power plants, and pick up a bunch of important details along the way!

Note

The standard deviation of a random variable $X$ is defined mathematically as

\sigma = \sqrt{E[(X - \mu)^2]} = \sqrt {E[X^2] - \mu^2}

where $E$ denotes the expectation value of its argument and $\mu = E[X]$ . This assumes we know the probability distribution of $X$ .

Alternatively, when we are dealing with a set of observations $\{x_1, x_2, x_3, ..., x_n\}$ of a random variable with a presumed (but unknown) distribution, we can estimate the standard deviation with this formula:

\sigma = \sqrt{\frac1n \sum x_i^2 - \frac1n \sum x_i} = [mean(x_i^2) - mean(x_i)]^{0.5}

which is the formula coded in the video (note that $mean(x_i)$ was calculated on the line preceding the standard deviation calculation: so we reference this instead of recalculating for efficiency). A better, unbiased estimator would divide by $n - 1$ instead of $n$ . We ignore this detail here for simplicity, however it could be easily fixed in the code by multiplying the result by:

\sqrt \frac{n}{n-1}

Task

1. Understand the Goals

Begin by watching the video to grasp the program's objectives. Use the video as a source of inspiration and guidance for your approach.

2. Identify the Excel File Location

Locate the file containing the data you'll be working with.

3. Import the Excel Data

Use appropriate libraries or methods to read the data from the Excel file.

4. Analyze and Split the Data

Divide the data based on the plant location;
Compute descriptive statistics (minimum, maximum, mean, and standard deviation) for each location, focusing on both power and wastewater data across the entire year.

5. Export the Results

Save your results, including the calculated statistics, into an Excel file for further use.

6. Iterate and Improve

Attempt to design your program in a modular and structured way;
If you encounter issues, rely more on hints or code shown in the video;
As a last resort, replicate the code in the video as closely as possible.

7. Quality Control

Validate your program's output by comparing it with the results shown in the video to ensure accuracy and reliability.

However, fair warning: it's better to practice coding your own versions now while there's a safety net, as we'll start getting less explicit with the code we show beginning in the next chapter.

The video also highlights several convenient checkpoints where you can check your programming as you go along by either:

Unsilencing variables (by removing the semicolon);
Having your function temporarily output the variable in question.

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Section 3. Chapter 1

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