Video Academy | Environmental area
The statistical analysis techniques presented in these videos are applicable to all subject areas, specifically focusing onexamplesof typeenvironmental.
Simple and Stratified Sampling​
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Learn to select the appropriate sampling method (simple or stratified) based on your data's structure.
This video compares both techniques and demonstrates their application using Statistics for Data Analysis.
Using the Iris dataset, we show that stratifying by species improves sample representativeness.
We also introduce the new Stratified Sampling Add-On (Version 30), which automates this process, resulting in more accurate and efficient analyses.
Factor Analysis (webinar in inglese)​
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If you're working with questionnaire data that includes scaled questions, where responses range from 1 to 5 or from "strongly disagree" to "strongly agree", but you're not sure how to make the most of it, watch the video from our webinar: you'll discover how to turn this data into valuable insights for your research!
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In the video, we demonstrate an example using a dataset from a survey on wildfires.
Custom Tables and Graphs​
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In this video, you'll learn a critical skill: how to efficiently convert raw data into actionable insights, significantly saving you time.​
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We'll demonstrate the steps to customize tables and generate charts, including histograms and pie charts. These visuals are instantly ready for reports, as you can export them to PowerPoint, Word, and Excel in just seconds!
Watch the Tables Module
Principal Component Analysis (PCA)​
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Do you need to extract the key information or simplify a complex dataset? The Principal Component Analysis (PCA) technique can be incredibly helpful
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In this videowe’ll explore PCA through a practical example using Statistics for Data Analysis
Topics Covered in this Video:
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Introductory Context: we'll discover the main objective of PCA and its wide range of application fields.
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PCA Theory: learn when and with which criteria you can correctly apply this technique.
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Practical Example: see PCA in action with a hands-on application to ensure a concrete understanding.
Data Preparation
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Data Preparation is the absolute foundation upon which all sound statistical analysis rests.
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Without a rigorous preparation process, your decision-making can become unreliable and ineffective. That's why it's critical to understand and master this fundamental skill.
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Key Benefits of Accurate Data Preparation
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Increased Analytical Accuracy: ensures your results are precise and trustworthy.
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Deeper Data Understanding: allows you to uncover hidden trends and patterns.
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Reduced Risk: minimizes the chance of making decisions based on flawed information.
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Greater Efficiency: streamlines the entire analysis and decision-making workflow.
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Throughout this video, we will walk you through a practical example in the environmental field, using Statistics for Data Analysis.
How to choose a statistical Test (webinar in English)
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When statistical analysis havesample dataas a starting point,statistical testsare of fundamental importance in order toextendthe results to a largerpopulation.
How to navigate the choice of the most appropriate statistical test to maximize the probability ofhighlightingin particulareffectand understanding whether it is actually present in the population?
thegoalof the presentation will be to show, throughpractical examples with Statistics for Data Analysis power by SPSS, techniques for choosing the most appropriate statistical test, based on the data at hand.
These are thetopicswe will cover during the presentation:
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Parametric tests
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Non-parametric tests
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Comparison between parametric and non-parametric tests
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The t-test
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During this video, after an overview on the topic ofstatistical inference, let's take a closer look at one of the most popular tests:the t-test (or Student's test).
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The T-test is one of the best known because it serves for compare equality statisticsmiddle school of two populations or of the same population with respect to a certain reference value.
Target of this presentation is to understand when, in which cases and what are the restrictions to be able tousethis kind ofparametric test.
Subjects:
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Summary of the rules for theverification of statistical inference
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Notes on differences between parametric and non-parametric tests
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Hiring of the t-test
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Criteria to decide when to use the parametric test versus a non-parametric procedure
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Practical exampleon a monitoring of the radioactivity present on mosses after the Chernobiyl disaster
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The t-test is found inBasic module
The Analysis of Variance for Environmental Data
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In the world we live in, the link between statistical analysis and environmental and health protection is increasingly evident.
Indeed, it is the result of statistical research, which has provided incontrovertible proof of the damage that climate change, atmospheric and noise pollution cause not only to man, but also to the entire environment.
By virtue of the increasing importance that statistics is assuming in this area, during this presentation we are showing a general overview of some statistical synthesis and survey techniques that can be used in the analysis of environmental data, such as for example the Analysis of Variance (ANOVA) .
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Subjects:
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Individualizationof outliers
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Checkof the data
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Explorationof the dataset
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Introductionto the analysis of variance
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Practical exampleon an air monitoring dataset
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Look at the features of the moduleStatistics Base​