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Exploratory Data Analysis - Shape and Boxplots

Oct 28, 2013

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Review. There are four features of distributions central location, spread, shape, and outliersCentral location can be measured by the mode (nominal or ordinal data) or the median or mean (interval/ratio data)In interval/ratio data, spread can be measured by the range (rarely useful), the IQR, or

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Exploratory Data Analysis - Shape and Boxplots

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Exploratory Data Analysis

Exploratory Data Analysis. Exploratory Data Analysis (EDA) Descriptive Statistics Graphical Data driven Confirmatory Data Analysis (CDA) Inferential Statistics EDA and theory driven. Before you begin your analyses, it is imperative that you examine all your variables.

3.72k views • 46 slides

Exploratory Data Analysis and Data Visualization

Exploratory Data Analysis and Data Visualization. Chapter 2 credits: Interactive and Dyamic Graphics for Data Analysis: Cook and Swayne Padhraic Smyth’s UCI lecture notes R Graphics: Paul Murrell Graphics of Large Datasets: Visualizing a Milion: Unwin, Theus and Hofmann. Outline. EDA

1.46k views • 57 slides

Exploratory Analysis of Survey Data

Exploratory Analysis of Survey Data. Lisa Cannon Luke Peterson. Presentation Outline. Density Estimation Nonparametric kernel density estimates Properties of kernel density estimators Other methods Graphical Displays NHANES data. Three features that distinguish survey data:.

722 views • 25 slides

Exploratory Data Analysis

Exploratory Data Analysis. Remark: covers Chapter 3 of the first edition Tan book in Part Organization Why Exloratory Data Analysis? Summary Statistics Visualization About Data. 1. Why Data Exploration?. A preliminary exploration of the data to better understand its characteristics.

1.31k views • 58 slides

Exploratory Data Analysis

Computing Science, University of Aberdeen. 2. Introduction. Applying data mining (InfoVis as well) techniques requires gaining useful insights into the input data firstWe saw this in the previous lectureExploratory Data Analysis (EDA) helps to achieve thisEDA offers several techniques to comprehe

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Exploratory Data Analysis

Exploratory Data Analysis. Height and Weight. Data exploration and Statistical analysis. Data checking, identifying problems and characteristics. Data exploration, categorical / numerical outcomes. Data. Analyzing a set of data. Look at the data (initial checks on the data)

1.18k views • 71 slides

Exploratory Data Analysis

Exploratory Data Analysis. Statistics 2126. Introduction. If you are going to find out anything about a data set you must first understand the data Basically getting a feel for you numbers Easier to find mistakes Easier to guess what actually happened Easier to find odd values.

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Exploratory Data Analysis

Exploratory Data Analysis. Remark: covers Chapter 3 of the Tan book in Part Organization Why Exloratory Data Analysis? Summary Statistics Visualization. 1. Why Data Exploration?. A preliminary exploration of the data to better understand its characteristics.

731 views • 34 slides

Exploratory Data Analysis

Exploratory Data Analysis. John Tukey. Developed these procedures to help one get a first look at distributions of scores. What is the shape of the distribution? Are there any suspicious scores. Stem and Leaf Display Box and Whiskers Plot. Stem and Leaf Display.

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Exploratory Data Analysis

Exploratory Data Analysis. Observations of a single variable. Example In 1798 Cavendish made 29 determinations of the density of the Earth, relative to that of water. His results are stored in R: &gt; density [1] 5.50 5.57 5.42 5.61 5.53 5.47 4.88 5.62 5.63 4.07 5.29 5.34 5.26 5.44 5.46

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Exploratory Data Analysis

Exploratory Data Analysis. Hal Varian 20 March 2006. What is EDA?. Goals Examine and summarize data Look for patterns and suggest hypotheses Provide guidance for more systematic analysis Methods of analysis Primarily graphics and tables Online reference

989 views • 33 slides

Exploratory Data Analysis

Exploratory Data Analysis. Introduction. Applying data mining (InfoVis as well) techniques requires gaining useful insights into the input data first We saw this in the previous lecture Exploratory Data Analysis (EDA) helps to achieve this EDA offers several techniques to comprehend data

1.07k views • 16 slides

Exploratory Data Analysis

Exploratory Data Analysis. Lecture overview. Data analysis template Exploratory Data Analysis (EDA) The role of EDA Doing EDA Interpreting EDA results. Discover patterns in data. Why is it important to find patterns? What counts as a pattern? What techniques can we use to find patterns?

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Exploratory Data Analysis Continued

Exploratory Data Analysis Continued. Exploratory Data Analysis. Set of techniques The flexibility to respond to the patterns revealed by successive iterations in the discovery process is an important attribute Free to take many paths in revealing mysteries in the data

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Exploratory Data Analysis

Explore in detail about Exploratory Data Analysis. Checkout: http://bit.ly/2Pfinm3 Any Queries, Call us@ 91 9884412301 / 9600112302 #DataScience #DataMining #Training #MachineLearning #AI #DeepLearning #CredoSystemz #Chennai #Python #hadoop #traininginstitute #datasciencetraining

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Exploratory Data Analysis and Data Visualization

Exploratory Data Analysis and Data Visualization. Credits: ChrisVolinsky - Columbia University. Outline. EDA Visualization One variable Two variables More than two variables Other types of data Dimension reduction. EDA and Visualization.

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EXPLORATORY DATA ANALYSIS

EXPLORATORY DATA ANALYSIS. (EDA). WHAT IS EDA?. The analysis of datasets based on various numerical methods and graphical tools. Exploring data for patterns, trends, underlying structure, deviations from the trend, anomalies and strange structures.

2.57k views • 52 slides

Exploratory Data Analysis

Exploratory Data Analysis. July 19, 2004 NGA Workshop. Step 1: For each earthquake, fit data to the following model (functional form used by Sadigh et al.). M = Earthquake magnitude R j = Closest distance to fault All soil types are lumped together.

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Exploratory Data Analysis

Exploratory Data Analysis. PS372 Spring 2010. The four features of distributions. Central Location – where are most of the observations? Spread – how far apart are the observations? Shape – Symmetric or skewed? Outliers – are any observations very far from the rest?.

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Exploratory analysis of neighbourhood data

Exploratory analysis of neighbourhood data. Census and administrative data. Lecture overview *Change to Census Registration System* Objectives of lecture Introductory questions Exploratory (spatial) data analysis Neighbourhood Statistics CASWEB/CommonGIS.

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