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April StatPREP Webinar

Date: Thursday, April 23

Time: 1 p.m. EDT, 12 p.m. CDT, 11 a.m. MDT, 10 a.m. PDT

Hosts: Jennifer Ward & Danny Kaplan

The third anniversary of the StatPREP Little Apps is approaching. To celebrate, we’re launching a brand new set of apps with a new look and new functionality. Among the changes are a dramatically increased choice of data sets (including those keyed to textbooks often used by StatPREP instructors) and the ability to upload your own CSV files, a feature that lets you freeze a display and show it side-by-side with the current display. The original apps will continue to be available at their current web addresses, but we encourage you to switch to the new ones and try out their new capabilities.

Registration link: https://maa.zoom.us/webinar/register/WN_i5fGJe00TLi8SQkRKlY4KA

March StatPREP Webinar

The March webinar is presented by Kari Lock Morgan. She will discuss resampling methods. It is on March 26, at 10 am PDT, 11 am MDT, 12 pm (noon) CDT,  1pm EDT. Check back for the description and sign up link.

SataPREP February Webinar – Delayed to March 13

Date Time: March 13, 2020 11:00 AM Eastern Time (US and Canada)
Topic: StatPREP Webinar: Course Curricula & StatPREP

Danny Kaplan and Kate Kozak will introduce the three new StatPREP companion tools for Statistics Using Technology by Kozak, OpenIntro Statistics by Diez, et al, and Elementary Statistics by Triola. Do you use one of these books in your introductory statistics classes? If so, tune in to this webinar to learn how StatPREP can help you teach data-centric stats! If you don’t use these books, you can still learn where the StatPREP material can be used with your textbook.

To register for the webinar, go to the site https://zoom.us/webinar/register/WN_qosBOYJ1Szq-gWfzTkklTQ

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.