Editor's note: This series of blogs addresses the questions we are most frequently asked at SAS Press! It worth spending some time on this. Arguably, this is one of the most important parts of the book. The table of contents and outline provide the blue print of your book –

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In my ongoing quest to track The Popularity of Data Analysis Software, I’ve finally decided to change the title to use the newer term “data science”. The 2017 version of Gartner’s Magic Quadrant for Data Science Platforms was just published, … Continue reading →

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Have you been using the SAS/Graph Gmap procedure to plot your data on maps for years, but never knew you could add roads to your maps?!? Follow along in this blog post, and I'll teach you how... But before we get started, here's a picture of a nice aer...

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Sometimes SAS programmers ask about how to analyze quantiles with SAS. Common questions include: How can I compute 95% confidence intervals for a median in SAS? How can I test whether the medians of two independent samples are significantly different? How can I repeat the previous analyses with other percentiles,

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What?!? You mean a period (.) isn't the only SAS numeric missing value? Well, there are 27 others: .A .B, to .Z and ._ (period underscore). Your first question might be: "Why would you need more than one missing value?" One situation where multiple missing values are useful involves survey data. Suppose

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Many introductory courses in probability and statistics encourage students to collect and analyze real data. A popular experiment in categorical data analysis is to give students a bag of M&M® candies and ask them to estimate the proportion of colors in the population from the sample data. In some classes,

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Are your friends passing around clever memes (supposedly) featuring something your favorite actor said, or sharing news articles that you think might be "fake news"? If there's even a hint of data analyst in you, then you probably check the actual data, and confirm or disprove the supposed facts yourself. I

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A categorical response variable can take on k different values. If you have a random sample from a multinomial response, the sample proportions estimate the proportion of each category in the population. This article describes how to construct simultaneous confidence intervals for the proportions as described in the 1997 paper

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