Search results “Exploratory data analysis descriptive statistics”

An introduction to exploratory data analysis that includes discussion of descriptive statistics, graphs, outliers, and robust statistics.

Views: 21939
Prof. Patrick Meyer

What is DESCRIPTIVE STATISTICS? What does DESCRIPTIVE STATISTICS mean? DESCRIPTIVE STATISTICS meaning - DESCRIPTIVE STATISTICS definition - DESCRIPTIVE STATISTICS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Descriptive statistics are statistics that quantitatively describe or summarize features of a collection of information. Descriptive statistics are distinguished from inferential statistics (or inductive statistics), in that descriptive statistics aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent. This generally means that descriptive statistics, unlike inferential statistics, are not developed on the basis of probability theory. Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented. For example in papers reporting on human subjects, typically a table is included giving the overall sample size, sample sizes in important subgroups (e.g., for each treatment or exposure group), and demographic or clinical characteristics such as the average age, the proportion of subjects of each sex, the proportion of subjects with related comorbidities etc.
Some measures that are commonly used to describe a data set are measures of central tendency and measures of variability or dispersion. Measures of central tendency include the mean, median and mode, while measures of variability include the standard deviation (or variance), the minimum and maximum values of the variables, kurtosis and skewness.
Descriptive statistics provide simple summaries about the sample and about the observations that have been made. Such summaries may be either quantitative, i.e. summary statistics, or visual, i.e. simple-to-understand graphs. These summaries may either form the basis of the initial description of the data as part of a more extensive statistical analysis, or they may be sufficient in and of themselves for a particular investigation.
For example, the shooting percentage in basketball is a descriptive statistic that summarizes the performance of a player or a team. This number is the number of shots made divided by the number of shots taken. For example, a player who shoots 33% is making approximately one shot in every three. The percentage summarizes or describes multiple discrete events. Consider also the grade point average. This single number describes the general performance of a student across the range of their course experiences.
The use of descriptive and summary statistics has an extensive history and, indeed, the simple tabulation of populations and of economic data was the first way the topic of statistics appeared. More recently, a collection of summarisation techniques has been formulated under the heading of exploratory data analysis: an example of such a technique is the box plot.
In the business world, descriptive statistics provides a useful summary of many types of data. For example, investors and brokers may use a historical account of return behavior by performing empirical and analytical analyses on their investments in order to make better investing decisions in the future.
Univariate analysis involves describing the distribution of a single variable, including its central tendency (including the mean, median, and mode) and dispersion (including the range and quantiles of the data-set, and measures of spread such as the variance and standard deviation). The shape of the distribution may also be described via indices such as skewness and kurtosis. Characteristics of a variable's distribution may also be depicted in graphical or tabular format, including histograms and stem-and-leaf display.

Views: 7532
The Audiopedia

Paper: Advanced Data Analytic Techniques
Module: Exploratory Data Analysis for Longitudinal Data
Content Writer: Souvik Bandyopadhyay

Views: 3595
Vidya-mitra

In this Python Statistics Tutorial, learn python describe statistics using pandas, NumPy and Scipy. We discuss Some Descriptive statistics in Python Using Jupyter Notebook. This is a Part of a Python Data Analysis Course.
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Jupyter Notebooks and Data Sets for Practice: https://github.com/theengineeringworld/statistics-using-python
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Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼 https://youtu.be/GMxCL0PBHzA
Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8
Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw
Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ
Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI
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Views: 85
TheEngineeringWorld

What is EXPLORATORY DATA ANALYSIS? What does EXPLORATORY DATA ANALYSIS mean? EXPLORATORY DATA ANALYSIS meaning - EXPLORATORY DATA ANALYSIS definition - EXPLORATORY DATA ANALYSIS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analysis (IDA), which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA.
Tukey defined data analysis in 1961 as: "rocedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data."
Tukey's championing of EDA encouraged the development of statistical computing packages, especially S at Bell Labs. The S programming language inspired the systems 'S'-PLUS and R. This family of statistical-computing environments featured vastly improved dynamic visualization capabilities, which allowed statisticians to identify outliers, trends and patterns in data that merited further study.
Tukey's EDA was related to two other developments in statistical theory: robust statistics and nonparametric statistics, both of which tried to reduce the sensitivity of statistical inferences to errors in formulating statistical models. Tukey promoted the use of five number summary of numerical data—the two extremes (maximum and minimum), the median, and the quartiles—because these median and quartiles, being functions of the empirical distribution are defined for all distributions, unlike the mean and standard deviation; moreover, the quartiles and median are more robust to skewed or heavy-tailed distributions than traditional summaries (the mean and standard deviation). The packages S, S-PLUS, and R included routines using resampling statistics, such as Quenouille and Tukey's jackknife and Efron's bootstrap, which are nonparametric and robust (for many problems).
Exploratory data analysis, robust statistics, nonparametric statistics, and the development of statistical programming languages facilitated statisticians' work on scientific and engineering problems. Such problems included the fabrication of semiconductors and the understanding of communications networks, which concerned Bell Labs. These statistical developments, all championed by Tukey, were designed to complement the analytic theory of testing statistical hypotheses, particularly the Laplacian tradition's emphasis on exponential families.

Views: 4200
The Audiopedia

Calculating Descriptive Statistics
Overview
Hello again and welcome back to Exploratory Data Analysis with R. 24x7 in this module we'll learn how to calculate descriptive statistics for our data. First we'll begin with an introduction to descriptive statistics, that is describing the characteristics of our data in meaningful ways. Next we'll learn about the types of analysis that we can perform on our data. Then we'll learn how to avoid making errors and invalid claims with our descriptive statistics. Finally, we'll see a demo where we'll put all these concepts together.
Introduction
Descriptive statistics describe data in meaningful ways. In exploratory data analysis, we're typically trying to quickly assess the location, spread, shape, and interdependence of our data. These types of descriptive statistics are often referred to as summary statistics because they summarize the shape and feel of the data. For example, in this table, we have the summary statistics for a single variable, that is the movie runtime variable in our movies data set. We have the Minimum, which is the lowest value in the column when the values are sorted in ascending order. First Quartile, which is the value that cuts off the first 25% of the values, the Median which is the value that separates the lower half from the upper half of the values, the Mean which is the arithmetic average of all of the values in the column, 3rd Quartile which is the cutoff value for the 75th percentile of the values, and maximum which is the highest value in the column. By looking at these summary statistics we can quickly learn about the location and spread of our data. We don't want to go too deep into statistics for this course, but in order to discuss descriptive statistics we need to understand a few basic terms from statistics. First we have observations, which are essentially the rows in the table. They are referred to as observations because in statistics we're typically concerned with observations of some kind of physical phenomenon. For example, if we have a temperature sensor, each recorded temperature over time would correspond to an observation of the temperature. Equivalently, we could also have transactions, like pizza sales transactions, or entities, like feature length films as the phenomena we are observing. However, for our purposes, we'll refer to them all generically as observations. Next, we have variables, which are the columns in the table. They are called variables because their values vary across each observation. For example, in the table to the right, Date, Customer, Product, and Quantity are all variables that can change value across each row in the table. There are two types of variables, first we have qualitative variables. Qualitative variables contain categorical values, for example, customers and products. In addition, they have no nature sense of order. We can, however, impose an arbitrary order upon them, like using the alphabetical sort order of their names. However, this is just an artificial, not a natural means of sorting them. Qualitative variables are often referred to as nominal variables because they are named values. Finally, we have quantitative variables. Quantitative variables contain numeric values, for example, the quantity of products sold. In addition they do posses a natural sense of order, for example, 2 pizzas sold is more than 1 pizza sold. Quantitative variables can also be subdivided into either discrete values, that is whole numbers represented as integers, or continuous values, that is all possible points on the real number line, typically represented as decimal precision numeric values. In addition, quantitative variables can also be subdivided into ordinal, interval, and ratio subtypes. However, these subdivisions, their differences, and statistical limitations are outside of the scope of this course. There are several types of statistical analysis we can do, which are dependent upon the number of variables. We can perform univariate, that is single variable analysis, or bivariate, that is two variable analysis, and the type of variables, whether we have qualitative, that is categorical values, or quantitative, that is numerical values. We'll dig deeper into each of these types of analysis next (Loading).

Views: 43
24x7 Learning

See here for the course website, including a transcript of the code and an interactive quiz for this segment:
http://dgrtwo.github.io/RData/lessons/lesson4/segment4/

Views: 18668
Data Analysis and Visualization Using R

This presentation introduces the task of describing a sample of persons from whom data has been acquired.

Views: 357
Reginald York

Data Exploration with R: One and Two Categorical Variables

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Tyler Moore

MathsResource.com | Statistics | Exploratory Data Analysis

Views: 1445
Maths Resource

www.ozanozcan.us

Views: 243955
ozanteaching

Once your data is ready for analysis, you need to obtain the descriptive statistics.
Video with examples to show how to obtain in R:
#summary stats (mean,median, min, max, sd, quantile, range, skewness, kurtosis, #not for mode) for one variable - vector and dataframe
#individual stats for observations in a vector/dataframe
#individual stats for subset of variables in a dataframe
#summary stats for a continuous variable over a factor/group
#frequency table applicable for factors

Views: 10135
Phil Chan

The objective of the Exploratory Data Analysis is to familiarise with the dataset, suggesting approaches to analyse the data and helping explain the results.
There is a tendency of rushing to apply classical statistical methods to datasets, like the analysis of variance and regression, without examining the data. Exploring the data provides insight on variable characteristics and data structure, which can guide the application of confirmatory methods. The purpose of this presentation is to introduce common exploratory methods.

Views: 641
emirwati

Descriptive Inferential and Exploratory Analytics

Views: 104
Tarah Technologies

Commands used: sum, mean, tab, fre, hist, scatter.

Views: 14375
FHSSResearchSupport

In this video we will learn how to do exploratory data analysis of the data. We will learn how to use Proc means, Proc Freq, Proc gplot, Proc Univariate to do EDA. You are expected to have understanding of basic statistical modeling.
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Views: 19106
Analytics University

Learn more about exploratory data analysis with Python: https://www.datacamp.com/courses/statistical-thinking-in-python-part-1
Yogi Berra said, "You can observe a lot by watching." The same is true with data. If you can appropriately display your data, you can already start to draw conclusions from it.
I'll go even further: exploring your data is a crucial step in your analysis. When I say exploring your data, I mean organizing and plotting your data, and maybe computing a few numerical summaries about them.
This idea is known as exploratory data analysis, or EDA, and was developed by one of the greatest statistitians of all time, John Tukey. He wrote a book entitled Exploratory Data Analysis in 1977 where he laid out the principles. In that book, he said, "Exploratory data analysis can never be the whole story, but nothing else can serve as the foundation stone." I wholeheartedly agree with this, so we will begin our study of statistical thinking with EDA.
Let's consider an example.
Here, we have a data set I acquired from data.gov containing the election results of 2008 at the county level in each of the three major swing states of Pennsylvania, Ohio, and Florida. Those are the ones that largely decide recent elections in the US. This is how they look when I open the file with my text editor. They are a little prettier if we look at them with in a Pandas DataFrame, in this case only looking at the columns of immediate interest, the state, county, and share of the vote that went to Democrat Barack Obama.
We could stare the these numbers, but I think you'll agree that it is pretty hopeless to gain any sort of understanding from doing this. Alternatively, we could charge in headlong and start defining and computing parameters and their confidence intervals, and do hypothesis tests. You will learn how to do all of these things in this course and its sequel. But a good field commander does not just charge into battle without first getting a feel for the terrain and sizing up the opposing army. So, like the field commander, we should explore the data first.
In this chapter, we will discuss graphical exploratory data analysis. This involves taking data from tabular form, like we have here in the DataFrame, and representing it graphically. You are presenting the same information, but it is in a more human-interpretable form.
For example, we take the Democratic share of the vote in the counties of all of the three swing states and plot them as a histogram. The height of each bar is the number of counties that had the given level of support for Obama. For example, the tallest bar is the number of counties that had between 40% and 50% of its votes cast for Obama.
Right away, because there is more area in the histogram to the left of 50%, we can see that more counties voted for Obama's opponent, John McCain, than voted for Obama.
Look at that. Just by making one plot, we could already draw a conclusion about the data, which would have been extraordinarily tedious by hand counting in the DataFrame.
Now let's review some of the basic ideas behind EDA with a couple exercises.

Views: 10874
DataCamp

Dr. Brian Caffo from Johns Hopkins presents a lecture on "Exploratory Data Analysis."
Lecture Abstract
Exploratory data analysis (EDA) is the backbone of data science and statistical analysis. EDA is the process of summarizing characteristics of a data set using tools such as graphs and statistical models. EDA is a principal method for creating new hypotheses or determining basic empirical support for evolving existing hypotheses.
EDA often yields key insights, especially those provided by plots and graphs, where key insights often hit you right between the eyes. In addition, new technology, such as interactive graphics, is greatly enabling EDA. However, care must be taken in EDA to not over-interpret the degree of confirmatory force of conclusions and to avoid attaching strict inferential interpretations to results.
This lecture covers the basics of EDA, summarizes some key tools and discusses its role in inference.
View slides
https://drive.google.com/open?id=0B4IAKVDZz_JUbTVYWVlwZHZkUzA
About the Speaker
Brian Caffo, PhD received his doctorate in statistics from the University of Florida in 2001 before joining the faculty at the Johns Hopkins Department of Biostatistics, where he became a full professor in 2013. He has pursued research in statistical computing, generalized linear mixed models, neuroimaging, functional magnetic resonance imaging, image processing and the analysis of big data. He created and led a team that won the ADHD-200 prediction competition and placed twelfth in the large Heritage Health prediction competition. He was the recipient the Presidential Early Career Award for Scientist and Engineers, the highest award given by the US government for early career researchers in STEM fields. He co-created and co-directs the SMART (www.smart-stats.org) group focusing on statistical methodology for biological signals. He also co-created and co-directs the Data Science Specialization, a popular MOOC mini degree on data analysis and computing having over three million enrollments. Dr. Caffo is the director of the graduate programs in Biostatistics and is the recipient of the Golden Apple teaching award and AMTRA mentoring awards.
Join our weekly meetings from your computer, tablet or smartphone.
Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars

Views: 1346
BD2K Guide to the Fundamentals of Data Science

Exploratory data analysis by Amarnadh Paritala

Views: 152
Amarnadh paritala

John McKenzie demonstrates how StatTools can be easily used for managing your data set, deriving descriptive statistics, exploratory data analysis, determining normality, inferential statistics (both parametric and non-parametric), time series, regression, logistic regression and quality control.
Originally Recorded: August 2008

Views: 7112
PalisadeCorp

Moving Beyond R and Exploratory Data Analysis
Overview
Hello and welcome back to this final module in Exploratory Data Analysis with R and in this module we'll look at a few topics that move beyond R and exploratory data analysis. First, we'll start with other types of data analysis we can perform beyond exploratory data analysis. Next, we'll see a demo of two additional types of analysis we can perform, that is linear regression analysis and cluster analysis. Then we'll learn about alternatives to R for performing exploratory data analysis. Finally, we'll wrap things up by concluding the module and the course as a whole.
Beyond Exploratory Data Analysis
I really want to emphasize that what I've shown you in this course so far is just the tip of the iceberg. I can't stress how much more R provides beyond what we've seen in terms of data analysis, data visualization, and programming features. First, there are several other major categories of data analysis beyond what we saw in this course. We mostly looked at descriptive statistical analysis, that is, describing the features of our data in meaningful ways, and exploratory data analysis, the topic of this course. Descriptive statistical analysis is generally the least complex and difficult to perform. Exploratory data analysis is generally next in terms of difficulty or complexity. However, there are four other main categories of data analysis beyond the two we've touched upon in this course, which we'll briefly discuss in order of relative difficulty or complexity. Third, we have inferential data analysis, that is testing a hypothesis about the world by collecting a sample of data and generalizing it to a larger population. This is the type of data analysis that is taught in introductory college statistics courses. If you've taken one of these courses, you'll probably recognize terms like null hypothesis and P values. These are terms frequently used in inferential data analysis. Fourth, we have predictive data analysis, that is using current or historical data to make predictions about future or otherwise unknown values. This type of data analysis is typically taught in higher level college statistics and machine learning courses. It is rapidly growing in popularity in the business world where it is commonly referred to as predictive analytics. Fifth, we have causal data analysis, that is determining how modifying one variable while holding all other variables constant changes some outcome variable. This type of data analysis is typically taught in graduate level college statistics and scientific research courses. It is commonly found in clinical research, for example, using randomized, double blind, placebo controlled studies to determine the efficacy of pharmaceuticals on certain human diseases. Finally, we have mechanistic data analysis, that is creating a mathematical model that captures the exact changes in all variables as one variable is modified. This type of analysis is typically taught in advanced college courses in engineering and physical sciences. It is only possible in systems that have variables that can be modeled as deterministic, that is, non-probabilistic sets of equations. For example, an engineer might create a mechanistic model of an automobile accelerating when the gas pedal is pressed using data from various sensors in the automobile. R has commands, algorithms, and extension packages that can assist you in performing all six of these types of data analysis.

Views: 16
24x7 Learning

0:01 - Introduction
10:55 - Exploratory Data Analysis
28:17 - Predictive Modelling (logistic regression, decision trees, bootstrap forest, neural networks)
55:00 - Online Resources (books, tutorials, trial licenses)
JMP is a tool that help students, teachers and researchers to lead the innovation in many scientific fields through a visual and interactive exploitation of data and an accessible use of applied statistics.
More information : www.jmp.com/why
Get your 30-days trial for free : www.com.com/try

Views: 90
Paolo Chiappa - JMP Academic -

Exploratory data analysis by Amarnadh Paritala

Views: 140
Amarnadh paritala

Modern Statistics, Exploratory Data Analysis, and Design of Experiments for Improving Reliability of Aging Engineering Structures
by Jeffrey Fong
3 April @ NPL

Views: 6338
National Physical Laboratory

Exploratory data analysis by Amarnadh Paritala

Views: 130
Amarnadh paritala

Exploratory statistics or how to easily pull information into large datasets.
Go further: https://help.xlstat.com/customer/portal/articles/2062361
30-day free trial: https://www.xlstat.com/en/download
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Stat Café - Question of the Day is a playlist aiming at explaining simple or complex statistical features with applications in Excel and XLSTAT based on real life examples.
Do not hesitate to share your questions in the comments. We will be happy to answer you.
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Produced by: Addinsoft
Directed by: Nicolas Lorenzi
Script by: Jean Paul Maalouf

Views: 1955
XLSTAT

Link to the Course:
statistics-by-mj.teachable.com/p/stats-bundle/
Coupon Code for Course: YOU23 to get 23% off.
Or Try 3 Courses for Free:
statistics-by-mj.teachable.com/p/free-trial-stats-lite/
In this course we go through:
1) Descriptive Stats
2) Data Location
3) Spread of Data
4) Shape of Data
5) Some actuarial exam questions

Views: 12878
MJ the Student Actuary

In this video you will learn exploring data using Proc Univariate, Proc corr
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Views: 4151
Analytics University

Links of Data set and case study used in the above video.
1. https://drive.google.com/open?id=1cFxxzm6mT1Ong4sO-qMrXHiGx_vfncb5
2. https://drive.google.com/open?id=1qGFUSLbp_T7Ei8GPxcDFLaLwQcsO0gpl

Views: 4607
Dr. Shailesh Kaushal

Exploratory data analysis is an approach to analyzing data sets to summarize main characteristics of the data.
Exploratory data Analysis can help to determine whether the statistical technique that we are considering for data analysis are appropriate.

Views: 793
My Easy Statistics

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2qyTs1d].
This video introduces the Titanic disaster data set and discusses some exploratory analysis on the data. The aim of this video is to recap what you learned so far on a real data set, as well as show-case some data visualization examples.
• Download the data set and understand the data structure
• Extract some summary statistics from the data set
• Visualize the data and find correlations between variables
For the latest Application development video tutorials, please visit
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Views: 12900
Packt Video

Analytics trainings and Data Analysis using SPSS training at PACE, for more details and Downloadable recorded videos visit www.pacegurus.com. Corporate training and consulting for Statistical Models and Data Analytics. contact +91 98480 12123

Views: 4360
Vamsidhar Ambatipudi

This clip explains how to produce some basic descrptive statistics in R(Studio). Details on http://eclr.humanities.manchester.ac.uk/index.php/R_Analysis. You may also be interested in how to use tidycerse functionality for basic data analysis: https://youtu.be/xngavnPBDO4

Views: 109918
Ralf Becker

Tutorial on using maps, descriptive statistics, and histograms to analyze the distribution of two variables. Demonstrates the use of the Exploratory Data Analysis tools with brushing and linking.
Interested in learning more from me? Salem State University offers a Bachelor of Science in Cartography and GIS. We also offer a graduate Certificate and a Master of Science in Geo-Information Science. Learn more at https://www.salemstate.edu/academics/colleges-and-schools/college-arts-and-sciences/geography

Views: 5114
Marcos Luna

In statistics, exploratory data analysis (EDA) is an technique to analyze data set to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analysis (IDA), which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed.

Views: 234
Mission Learning Nepal

Introduction to R
Introduction
Hi. In this course you'll learn how to perform Exploratory Data Analysis with the programming language R. When you're finished with this course you'll be able to use exploratory data analysis techniques and R to solve day to day developer tasks, like transforming data files, detecting anomalies, and visualizing patterns and data. So let's get started. As an overview, first we'll start with an introduction to the R programming language. We'll learn what it is, why it has become so popular for data analysis, and how to perform basic programming tasks using R. Next we'll learn how to use R to load, transform, clean, and export data. This is usually the most difficult and time consuming task in any data analysis, but it's a very important step and an extremely useful skill to have. Then we'll learn how to calculate descriptive statistics using R. Descriptive statistics are numerical quantities that provide us with the basic shape and feel of the data they describe. Next, we'll learn how to visualize data using the basic R plotting system. Data visualization is an extremely useful technique for finding patterns and data sets by representing the attributes of the data via visual means. Finally, we'll look at several steps that go beyond R and exploratory data analysis. First we'll cover a few alternatives to using R for performing exploratory data analysis, then we'll look at a few data analysis techniques that can be performed with R that go beyond exploratory data analysis. The only prerequisite for this course is that you have experience with at least one C-like programming language. Languages like C++, C#, Java, JavaScript or Python are all sufficient to understand the concepts in this course. As long as you have a basic understanding of programming constructs, control structures, and data structures, you should do just fine. The intended audience for this course are developers who work with data on a daily basis and want to have the skills necessary to explore and analyze this data quickly and efficiently, data analysts with a bit of programming experience who want to learn how to perform exploratory data analysis using R, or anyone in information technology with basic programming experience and a desire to learn how to transform data into actionable knowledge. Whether you realize it or not, there's a flood of data coming our way. In fact, the flood is already here and it's growing exponentially each year. In the next decade or so, you're going to see two completely different outcomes for people, businesses, and governments based on whether they learn to use these data to their advantage or not. Essentially these people, businesses, and governments are either going to sink in the sea of data or they're going to learn to swim. This is which it's important that we learn how to work with data and transform it into actionable knowledge. We want to be the people that are swimming in the data driven economy, not the ones that are sinking. This same sentiment has been expressed by experts across various industries. In fact, articles like these pop up on a daily basis these days. We now live in an economy where data is extremely inexpensive to produce, store, and process. We essentially have more data than we know what to do with. So the scarce resource in this data driven economy are people with the skills and tools to work with and extract value from data (Loading). So you might be thinking to yourself, I'm not a statistician or a data scientist, so how does this apply to me? Well, as a software developer I often perform log file analysis; analyze software for performance issues, analyze code metrics for code quality, detect anomalies in source data, transform or clean data files to make them usable, and help decision makers make decisions based upon data. It's much easier to extract value from data if you have the skills and the tools necessary to transform, analyze, and visualize data.

Views: 95
24x7 Learning

Description
This tutorial will focus on analyzing a dataset and building statistical models from it. We will describe and visualize the data. We will then build and analyze statistical models, including linear and logistic regression, as well as chi-square tests of independence. We will then apply 4 machine learning techniques to the dataset: decision trees, random forests, lasso regression, and clustering.
Abstract
I would be happy to conduct an introductory level tutorial on exploring a dataset with the pandas/StatsModels/scikit-learn framework:
1. Descriptive statistics. Here we will describe each variable depending on its type, as well as the dataset overall.
2. Visualization for categorical and quantitative variables. We will learn effective visualization techniques for each type of variable in the dataset.
3. Statistical modeling for quantitative and categorical, explanatory and response variables: chi-square tests of independence, linear regression and logistic regression. We will learn to test hypotheses, and to interpret our models, their strengths, and their limitations.
4. I will then expand to the application of machine learning techniques, including decision trees, random forests, lasso regression, and clustering. Here we will explore the advantages and disadvantages of each of these techniques, as well as apply them to the dataset.
This would be a very applied, introductory tutorial, to the statistical exploration of a dataset and the building of statistical models from it. I would be happy to send you the ipython notebook for this tutorial as well.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.

Views: 2025
PyData

Let's go on a journey through univariate analysis and learn about descriptive statistics in research!

Views: 42448
ChrisFlipp

Tutorial for using SPSS 16 to run descriptive statistics for categorical and continuous variables, a 2-way contingency table for categorical variables, and chi-squared analysis, and a correlation analysis for 2 continuous variables.
These videos are not intended to teach you how to calculate, comprehend, or interpret statistics. These videos are merely a tool to introduce you to some basic SPSS procedures.
Download the sample data at the KSU Psych Lab web page:
http://www.kennesaw.edu/psychology/videos/lab/sample_data.xlsx
Subtitles available: click on the CC button toward the bottom right of the video.
Menu available for jumping to chapters in the flash video posted on the KSU Psych Lab website:
http://psychology.hss.kennesaw.edu/resources/psychlab/

Views: 139147
Terry Jorgensen

To view the full course, please visit: https://goo.gl/F66AL6

Views: 476
Matthew Renze

Exploratory Data Analysis in SPSS version 20 training by Vamsidhar Ambatipudi

Views: 448
Vamsidhar Ambatipudi

Both unordered and ordered stem and leaf plots are made with 25 data points in an effort to motivate Exploratory Data Analysis. Also, I expound a bit on my "philosophy of learning" - and try to convince you, my gentle listener, that everyone is able to excel in statistics with only a lot of grit and dedicated practice.

Views: 687
Matt Wiley

This video shows you how to use Excel's worksheet functions to replicate the descriptive statistics tool in the Data analysis tool pack in Excel.

Views: 485
Gary Hutson

Subject: Social Work Education
Paper:Research Methods and Statistics
Module: Univariate Analysis & Bivariate Analysis
Content Writer: Dr. Graciella Tavares

Views: 23158
Vidya-mitra

This Webinar presents descriptive statistical features with applications using the XLSTAT statistical software.
Discover our products: https://www.xlstat.com/en/solutions
Download the data: https://help.xlstat.com/customer/portal/kb_article_attachments/125605/original.xlsx?1513264876%E2%80%8B
30-day free trial: https://www.xlstat.com/en/download
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Introduction to descriptive statistics - 1 hour
Data analysis tools can be grouped into several categories, each category aiming at answering a certain type of questions associated to a certain type of data. Descriptive statistics allow to summarize information contained in datasets using simple numbers such as the mean or the standard deviation; and charts such as box plots and scatter plots.
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Produced by: Addinsoft
Directed by: Jean Paul Maalouf

Views: 524
XLSTAT

© 2018 Quotations on life pictures

Selling in special circumstances. shares you bought at different times and prices in one company shares through an investment club shares after a company merger or takeover employee share scheme shares. Jointly owned shares and investments. If you sell shares or investments that you own jointly with other people, work out the gain for the portion that you own, instead of the whole value. There are different rules for investment clubs. What to do next. Deduct costs. Apply reliefs.