In this video I explain the difference between Chebyshev's inequality and the Empirical rule for bell shaped data.

In this lesson we will explore the definiton of a z score and how that relates to percentile rank. We will also explore a data set to see if there are any outliers in the data. To determine if there are outliers we will create a modified boxplot on a TI-83 but we will also use the fence criterion.

(lesson5 guided notes)

In this video, which is a clip from a LIVE classroom, I review how to find variance and standard deviation by hand and also go over the mathematical notation for measures of central tendency and dispersion.

In this video I show how to retrieve mean, median, mode and standard deviation at the touch of a button using excel(with megastat add in). We take a data set (people's test scores) and practice making stem and leaf and boxplots in excel. We discuss mean, median, and mode and the relationship to skewed distributions as well..

In this video we discuss the meaning of various numerical summaries such as mean, median , mode, standard deviation, and variance. I also demonstrate how to obtain those descriptive statistics by formula and using technology.(Lesson3 guided notes)

In this video I make a line chart, bar chart , and pie chart using excel.

This is a clip from a live classroom so you will hear me interacting with students as I guide them through some of the statistical jargon that is being used and I also review how to make data displays such as pie charts and histograms using both excel and TI83.

You can make boxplots, stem and leaf plots, dot plots , boxplots, and histograms at the touch of a button in excel and I will show you how in this video.

I demonstrate how to use a TI-83 and excel(with megastat)l to report numerical summaries such as mean and median and standard deviation of a data set quickly and easily.

In this podcast we take a data set and create a histogram on the TI-83 to display the income distribution and discuss the shape of the distribution. Sometimes you can tell whether a distribution is skewed right , skewed left, or fairly symmetric just by looking at a histogram but sometimes it's not that obvious.