Here’s the regression test project. This will be due the day of the quiz for bivariate data, on Friday, May20.

Regression TEST Project

Here’s the regression test project. This will be due the day of the quiz for bivariate data, on Friday, May20. Regression TEST Project This is a link to the Against all Odds Video discussing Describing Relationships – in other words, Scatterplots. After viewing this presentation, you should be able to do the homework for the day – Problems 3.1, 3.6, 3.7, 3.9, 3.10, 3.12, 3.20, 3.22. It is due at the end of class. CLICKING ON . . . → Read More: Statistics – Against all Odds VIDEO on scatterplots Think picking all the top-seeded teams as the Final Four in your March Madness bracket is your best bet for winning the office pool? Think again. Illinois undergraduate students Ammar Rizwan (left) and Emon Dai developed the BracketOdds website (bracketodds.cs.illinois.edu) to help March Madness fans determine the relative probability of their chosen team combinations appearing . . . → Read More: A little Statistics Article I thought was interesting… Today, after the presentations, I gave you a little intro to scatterplots using data from pizza. I’ll reintroduce and re-discuss everything from today in the coming weeks. but for right now, here’s a more thorough discussion of scatterplots, borrowed from stattrek.com A scatterplot is a graphic tool used to display the relationship between two quantitative . . . → Read More: Statistics – Scatterplots We listened to most of you give your short presentations on your confidence interval choices today – they were very good and well done. For those of you who have not yet given me your papers, I will accept them either by email or paper until midnight tonight. My email is adick@wsd.net. GET . . . → Read More: Stats – presentations & projects today This is an example of the project due next time in Statistics. You should have the following components in order to receive full credit: An introduction Your survey question(s) An explanation of how the data was collected – THIS NEEDS TO BE SOME RANDOM METHOD, and the sample size needs to be at least 40, . . . → Read More: Statistics – Who’s Driving: an example of confidence intervals in statistics This is a discussion of what we have to do if the sample size is too small to construct a regular confidence interval – borrowed from stattrek.com In the previous lesson, we showed how to estimate a confidence interval for a proportion when the sample included at least 10 successes and 10 failures. This requirement . . . → Read More: Statistics – THIS IS FOR SARA! What do we do if the sample is too small? This lesson describes how to construct a confidence interval for a sample proportion, p. Estimation Requirements The approach described in this lesson is valid whenever the following conditions are met: The sampling method is simple random sampling. The sample includes at least 10 successes and 10 failures. (Some texts say that 5 successes and 5 . . . → Read More: Statistics – Confidence Intervals for proportions, borrowed from stattrek.com Statisticians use a confidence interval to describe the amount of uncertainty associated with a sample estimate of a population parameter. How to Interpret Confidence Intervals Consider the following confidence interval: We are 90% confident that the population mean is greater than 100 and less than 200. Some people think this means there is a 90% . . . → Read More: Statistics – a primer on confidence intervals, from stattrek.com A confidence interval consists of a central estimate, as well a surrounding margin of error. This is more thoroughly discussed below – borrowed from stattrek.com. In a confidence interval, the range of values above and below the sample statistic is called the margin of error. For example, suppose we wanted to know the percentage of . . . → Read More: Statistics – more on confidence Intervals: the Margin of Error |