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Compact Guide chapters 5, 6, & 7

It’s a new year and time for a new batch of chapters from A Compact Guide to Classical Inference. These chapters complete the setup for a dramatic development to come in the next release, where the simple formulation of statistical inference which will unify all the different settings found in the introductory course–difference between two means, difference between two proportions, slope of a regression line–into a pair of simple formulas and a test statistic F that directly handles both hypothesis testing and the construction of confidence intervals.

Chapters 1 through 4 brought us to the point where we can measure variation and model variation in the response variable as a function of explanatory variables.

Now, Chapter 5 uses the model function in a very simple way to find model values, the output of the model when the function is applied Read more

Chapters 2 & 3: Compact Guide to Classical Inference

Today we’re releasing another two chapters of the Compact Guide to Classical Inference. Today’s chapters lay much of the foundation for the compact approach. Chapter 2 describes briefly the organization of data and introduces two simple notions not usually found in traditional approaches: identifying a specific response variable and creating an indicator variable when the response is categorical. Simple as indicator variables are, using them enables problems involving proportions to be folded directly onto the settings for quantitative response variables. Just this simple step reduces the number of inferential settings by half! (Later,  we’ll see how it also handles the situation usually, and unnecessarily, treated with chi-square.)

Chapter 3 is very short: measuring variation. As you’ll see, a central unifying theme of inference is measuring the amount of variation in the response variable and comparing that … well you’ll have to wait for Chapters 4 and 5 for that!

The variance is the star here. No longer relegated to being an intermediate step in calculating a standard deviation, the variance shines on its own.

The textbook method for computing the variance involves subtracting the mean from each data value. As an innovation, Chapter 3 shows the variance solely in terms of comparing pairs of values. This little added insight into a familiar statistical quantity alone justifies reading the chapter.

December webinar

December StatPREP Webinar
Date: Dec 10, 3pm EST , 2 pm CST, 1 pm MST, noon PST
  • Who: Donna LaLonde & Danny Kaplan
  • Donna is  the Director of Strategic Initiatives and Outreach at the American Statistical Association and the ASA liaison on  the StatPREP Board. Danny is one of the founders of StatPREP and a professor at Macalester College in Saint Paul, Minnesota.
  • Title: The Compact Guide to Classical Inference: Why?
  • Description: In December 2019, StatPREP.org starts the serial publication of a short and highly specialized book: The Compact Guide to Classical Inference. Keeping in mind that StatPREP is about centering statistics education on real data, this book is an anomaly: the introduction of a reframed mathematical approach to statistical inference. The reframed approach unifies into a simple, single standard procedure all the inferential settings found in intro stats and carries forward naturally to apply to “advanced” settings such as multiple regression. The webinar will be a conversation between Donna and Danny about how the many formulae and probability tables of historical statistics can be unified into a single simple test statistic, F, and an expression for the confidence interval in terms of F.

 

November Consulting Days: 12th and 15th

The StatPREP consulting days for this month are :

  • Tuesday 12 Nov
  • Friday 15 Nov.

Consulting days are one of the ways StatPREP provides ongoing support <!–more—-> to project participants.

  • Trying a new data set in your class, but need some help getting everything organized?
  • A lesson or activity from the workshops you want to implement, but don’t remember the details?
  • Wondering how the parts of statistics fit together?
  • Need a bit of help with R or Markdown or deploying data or documents to the web?
  • Just want to talk some things out?

Sign up ON THIS CALENDAR for a 30 minute session or two sessions back to back. (You’ll land on the calendar for today, so page forward to get to the actual consulting day.) Our doors are open all day, from 8am Eastern to 5pm Pacific.

We meet via web conference, at https://appear.in/mosaic-web. All you need is a browser on a computer with microphone (and at your option) camera.

Try it out!

Dan Adrian’s Happy Apps

Dan has written a new set of apps to help in teaching intro stats. In this post, Dan lists and briefly describes his “Happy Apps.”

How do the Happy Apps relate to the StatPREP project’s Little Apps? The Little Apps are rooted in displays of data; statistical concepts are always directly put in the context of such displays. The Happy Apps have graphics oriented to methods and the statistical theory behind them. These are two different pedagogies for teaching statistics.

And now, here’s Dan Adrian …

Introducing the Happy Apps collection, a baker’s dozen of R Shiny apps for introductory statistics at the undergraduate (or AP Stats) level. Each is a dynamic visual aid addressing a specific concept or topic in intro stats. They make nice visual aids to add to a lecture, or teachers Read more