Statistics: Recent Episodes
Susan Elizabeth Cooper-Nguyen
These podcasts are for students interested in learning or reviewing topics in an introductory statistics class. Podcast topics include describing and summarizing data both graphically and numerically, probability, various distributions, confidence intervals, tests of significance, and exploration of bivariate data. This show focuses more on practice with using a graphing calculator and Microsoft Excel and less of the formulaic approach. Some of the podcasts are raw (unedited) clips from a live classroom. This course, taught on campus twice per week for 100 minutes, was recorded at the Lancaster Campus of Harrisburg Area Community College. This course requires the use of a graphing calculator such as a TI-83 or TI-84 and Excel.
In this video I tell the story of Joy Milne and her ability to smell Parkinson's disease and use part of that story as an intro to learning the characteristics of the binomial probability distribtion. We learn how to create binomial probability distributions using excel and the binomial formula..
In this video I tell the story of Joy Milne and her abiity to smell Parkinson's disease and we use part of her story to discover the characteristics of a binomial probablity distribution.We also learn how to create a binomial probablity distribution using excel and a using the binomial formula..
Big review of basic laws of probability and counting principles . This is a recording in front of a live classroom.
Here I present some of the basic rules for counting techniques needed in an intro to probability course.
We explore the birthday problem: What is the probability that at least 2 people share the same birthday in a classroom of 25 people?
This is part of 4 of 4 on the basic laws of probability. This podcast focuses on conditional probability and the test for independent events.
These notes on probability will be broken into a 4 part video.This is part one of the guidedd notes on probability
In this video I begin to introduce the laws of probability very simply through looking at cards, dice, and coin problems..
In this video I show how easy it is is to compute residuals and make a residual plot in excel.
In this video I show a fast and easy way to retrieve the scatterplot and correlation coefficient and examine the strength and direction of the association of a scatter-plot using excel.
In this video I pick a problem from the textbook to work through with you and we calculate residuals and make a residual plot. We then use the residual plot to decide if the linear model is appropriate for the data.You may want to pause the video and enter the data and practice computing residuals along with me.
In this video I pick a problem from the textbook to work through with you and we calculate residuals and make a residual plot. We then use the residual plot to decide if the linear model is appropriate for the data.You may want to pause the video and enter the data and practice computing residuals along with me.
This a live clip from an actual classroom where we learn how to compute residuals using a TI-83 and learn the definition of a residual.I also show how to compute the sum of the squared residuals.
In this lesson we explore the association between two variable via looking at scatter-plots and computing the correlation coefficient. This is shown using a TI-83.Lesson6 guided notes.
This is a clip from a live classroom where we do a warm up problem together and review how to numerically and graphichally summarize a data set via making a boxplot and histogram on TI-83 and we use the TI-83 to gather numerical descriptors.
This is a clip form a live classroom where I work through some problems in the textbook involving
z scores and boxplots.We review how to find outliers as well.
In this video I pull a couple of examples out of our textbook to work through (for extra practice)involving z scores and boxplots.
This is a clip from a live classrooom where we work through a homework problem together involving Chebyshev's theorem.
This is a clip fom a live classroom where we work on some problems together out of the textbook and practice using Empirical rule and Chebyshev's inequaltiy.
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)
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.
Why do we need to learn and study statistics? In this podcast I begin to answer that question and I introduce some statistical jargon and terminolgy and provide an intro to ways of displaying data.
Lesson 1; guided notes