The IRE Resource Center is a major research library containing more than 23,250 investigative stories — both print and broadcast. Add to that more than 3,000 tipsheets from our national conferences on how to cover specific beats or do specific stories and you have a resource that no reporter or editor should be without. These stories and tipsheets are searchable online or by contacting the Resource Center directly (573-882-3364 or rescntr@ire.org) where a researcher can help you pinpoint what you need. Browse or search the tipsheet section of our library below. Logged-in members can view the tipsheets free online:
Search results for "regression" ...
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Practical machine learning: Tips, tricks and real-world examples for machine learning in the newsroom
Learn how to apply machine learning in the newsroom from examples from real-world projects, including building intelligent regular expressions with maximum entropy models; using Bayesian classifiers to filter documents; and linear and logistic regression.
Tags: Machine learning; news reporting; entropy models; filters; linear regression; logistic regression
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Predicting the News
Webster uses concrete examples on how analyses, including regressions and predictive indexes, can reveal stories, and methodology for how to make such an analysis.
Tags: regression analysis; predictive index; data; analysis; prediction
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Math for Journalists: Stats you need to know
Donald's presentation discusses why the use of statistics is important for journalists, and how stats enable the reporter to say the most with the data they're using. The tipsheet talks you through some of the lingo you need to know when dealing with stats, and provide a list of helpful resources.
Tags: stats; regression; dependent variables; independent variables; data; mean; median; range; rank
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Statistics for stories
Donald and LaFleur provide tips on how to analyze, organize and understand all things data-related in order to find statistics within the information. They give detailed explanations of types of data that reporters will encounter, and explain how to work with and break-down all of the different parts.
Tags: statistics; data; investigative reporting; statistical analysis; Gini coefficient; mean; median; range; rank; regression; continuous data; categorical data
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Statistics For the Newsroom
This comprehensive guide to incorporating statistics into a story is a good refresher for seasoned reporters and a great introduction to CAR newbies. LaFleur covers different types of data, different forms of analysis (mean, median, regression, rank, correlation, etc), ideas for beat reporters, and instructions for calculating indexes.
Tags: statistics; beat reporting; data analysis; math
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Investigating Education: Story Topics to Turn Education Into a Watchdog Beat
Ciotta lists topics and provides example questions to get a reporter started with an investigations as well as possible sources. Topics include: safety and security, school violence, zero tolerance, violence, test scores, regression, and fiscal stories.
Tags: education; safety and security; school violence; zero tolerance; violence; test scores; regression; fiscal stories
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Stats for stories: Some pluses and minuses that may divide you from multiples of other journalists
This tipsheet gives an outline of basic and advanced statistics tools. It includes simple ways of calculating percentages to regression analysis.
Tags: statistics; databases; regression; averages; median; mode; indexes
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A pattern of suspicion: Analyzing racial profiling data
Lehren gives a brief overview on how to get started in analyzing the data for a story on racial profiling, noting external and internal benchmarks to use for comparisons and pitfalls to avoid. He notes two studies that are of particular help to journalists and available free on the Internet.
Tags: racial profiling; CAR; computer-assisted reporting; race data; logistic regression
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Education statistics tipsheet
Method explains Chi squares, the Pearson correlation coefficient and regression in terms of education statistics. Diagrams and a step-by-step approach help illustrate these concepts.
Tags: None
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Adventures in Trend Spotting
Finding trend lines in environmental data can get tricky, and a bit to high brow for some readers. Clemings shows how he found a trend in air pollution levels by using a simple Excel formula and then graphing the results.
Tags: environment; trends; data; excel; CAR; air pollution; pollution; ozone; averages; statistics; regressions; graphs