MATLAB: Analyze Rainfall Data - Standardized Precipitation Index (SPI) скачать в хорошем качестве

MATLAB: Analyze Rainfall Data - Standardized Precipitation Index (SPI)

In this video, I go through how to analyze rainfall data to determine the standardized precipitation index (SPI). I discuss how to go about analyzing data to understand it. I talk about the importance of working with experts on the context of the data throughout the process, when feasible. I also demonstrate how to validate your results. Here is a link to the data used and program developed in this video: https://www.mathworks.com/matlabcentr... In a previous video I go through how to change a large text file (.txt) that imports into MATLAB as strings in cells to accessible (separated/organized) set of data consisting of cell arrays with strings and numbers. I received a set of data from a subscriber. In this video I go through the process of familiarizing myself with the data set and figuring out how to make it accessible so I could analyze it for the given problem. Watch previous:    • MATLAB: Importing/Cleaning .txt file (stri...   Check this playlist of videos about techniques to import and analyze Excel data in MATLAB:    • Everything MATLAB + Excel tutorial and exa...   I also have a MATLAB tutorial playlist if you are looking for help with anything from the basics to more complex ideas:    • How to Create Programs in MATLAB (tutorial)   #MATLAB #learntocode #engineeringstudent #engineeringstudents #engineering #engineer #coding #code #programming #program #cleandata #analyzedata Chapters: 00:00 Introduction 00:08 Discussing previous video (import and clean data) 00:26 Rainfall data analysis approaches 01:19 Starting to analyze the data (where in code) 01:50 Standardized Precipitation Index (SPI) - probability of event 02:54 Calculating probability inside for loop 03:48 Handling bad data (NaN) 04:48 Adding descriptive statistics (mean, median, min, max) 05:02 Handling bad data (too large of values to be realistic) 06:45 Reviewing data to ensure no bad data 07:18 Handling bad data (probability of 0 or 1 unrealistic, so remove) 08:40 Bar graph (visualize data analysis) 09:35 Use real world context to validate results 12:30 Thank you for watching!