| 1. Introduction.mp4 | 6.7 MB | ||
| 1. Preparation of Tables.mp4 | 272.3 MB | ||
| 1. Preparation of data for IDW.mp4 | 72.3 MB | ||
| 1. Theoretical background of Innovative Trend modeling in R using Time Series data.mp4 | 91.4 MB | ||
| 1. Visualization of Innovative Trends at 95% CL in Excel.mp4 | 356.5 MB | ||
| 1.1 ITA_Graph.xlsx | 18.8 KB | ||
| 1.1 Module 2.pptx | 464.5 KB | ||
| 1.1 Module 3.pptx | 464.6 KB | ||
| 1.1 Module 5.pptx | 457.5 KB | ||
| 1.2 Module 4.pptx | 722.9 KB | ||
| 1.3 Module 1.pptx | 475.7 KB | ||
| 2. Calculation of ITA in R.mp4 | 74.8 MB | ||
| 2. Importance of Trend Analysis.mp4 | 18.6 MB | ||
| 2. Preparation of Figures Part 1.mp4 | 147.8 MB | ||
| 2. Set working directory and read excel and CSV file in R.mp4 | 62.4 MB | ||
| 2. Transforming Excel Spreadsheet Data into Point Features in ArcGIS.mp4 | 74.6 MB | ||
| 2.1 Code 1 Read Excel and CSV file.txt | 716.8 B | ||
| 2.1 Code 4 ITA.txt | 921.6 B | ||
| 2.1 Module 6.pptx | 543.4 KB | ||
| 29HE.1943-5584.0000556.pdf | 858.1 KB | ||
| 3. Calculating Annual and Seasonal rainfall from monthly rainfall data.mp4 | 116.2 MB | ||
| 3. Definition of Trend Analysis.mp4 | 16.7 MB | ||
| 3. IDW for ITA slope in ArcGIS.mp4 | 80.5 MB | ||
| 3. Preparation of Figures Part 2.mp4 | 207.3 MB | ||
| 3.1 Code 2 Convert Monthly to Annual and Seasonal.txt | 1.4 KB | ||
| 4. IDW for descriptive statistics in ArcGIS.mp4 | 56.7 MB | ||
| 4. Layout of Manuscript.mp4 | 74.6 MB | ||
| 4. Relevance in Environmental Monitoring.mp4 | 12.1 MB | ||
| 4. Separate excel file columns into multiple column in R.mp4 | 27.1 MB | ||
| 4.1 Code 7 Separated data.txt | 921.6 B | ||
| 5. Combined multiple excel files into one in R.mp4 | 49.7 MB | ||
| 5. Types of Environmental Data.mp4 | 20.8 MB | ||
| 5.1 Code 6 Combined data.txt | 1.3 KB | ||
| 6. Calculating missing data using MICE techniques in R.mp4 | 59 MB | ||
| 6. Where to Obtain Data.mp4 | 38.1 MB | ||
| 6.1 Code 3 Missing data Mice.txt | 1.2 KB | ||
| 7. Preparing BOXPLOT to detect outliers of dataset in R.mp4 | 60 MB | ||
| 7.1 Code 8 Boxplot.txt | 1.3 KB | ||
| 8. Calculating the descriptive Statistics in R.mp4 | 67.2 MB | ||
| An Integrated Approach for Delineating and Characterizing.pdf | 503.8 KB | ||
| Bonus Resources.txt | 409.6 B | ||
| Challenges of sustainable groundwater management for large scale.pdf | 14.4 MB | ||
| Comparison of Classical Mann–Kendal Test and Graphical Innovative Trend Analysis.pdf | 1.9 MB | ||
| Exploring driving forces of large‑scale unsustainable.pdf | 4.6 MB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| Groundwater Depletion Zonation.pdf | 1.1 MB | ||
| Groundwater_depletion_susceptibility_zonation_using TOPSIS model.pdf | 8.4 MB | ||
| Investigation of spatio‑temporal variability of meteorological drought.pdf | 9.2 MB | ||
| Meteorological drought analysis using Standardized Precipitation.pdf | 6.6 MB | ||
| Probability and variability analysis of rainfall characteristics of Dinhata in Koch Behar district of West Bengal.pdf | 633.2 KB | ||
| Rainfall Insight in Bangladesh and India.pdf | 3 MB | ||
| Spatio-Temporal Changes of Rainfall.pdf | 1.2 MB | ||
| Spatio-temporal characterization of rainfall in Bangladesh an innovative trend and discrete wavelet transformation approaches.pdf | 4.9 MB | ||
| trendchange.pdf | 133.1 KB | ||
| ▲ 55 total files | |||
Innovative Trend Analysis (ITA) for Time Series Data
https://FreeCourseWeb.com
Published 3/2024
Created by Dr. Jayanta Das
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 25 Lectures ( 2h 52m ) | Size: 2.2 GB
Using Excel, R, and ArcGIS
What you'll learn:
Understand the core concepts of trend analysis and its pivotal role in environmental monitoring to make data-driven decisions for sustainable development.
Gain proficiency in using Excel, R, and ArcGIS as your primary tools for dissecting complex time series data.
Learn to clean, organize, and transform environmental data, handle missing values and outliers, and ensure data quality for accurate trend analysis.
Develop skills in innovative trend modeling with R, learn to calculate and interpret the slope of trend analysis, and unveil hidden patterns in timeseries data.
Become adept at visualizing trends, creating significant level plots, and identifying concealed trends within your data through sophisticated Excel techniques.
Explore the integration of spatial statistics into environmental trend analysis and master spatial interpolation methods to visualize trends in geographic data.
Learn to integrate analysis results and visuals from Excel, R, and ArcGIS to prepare tables, figures, and comprehensive interpretations ready for publication.
Requirements:
No programming or statistical Knowledge needed.
You will learn everything you need to know.
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