Chapter 7: 31 October 2006

We finished our discussion of confidence intervals by talking about one-sided intervals. We saw examples using both the T and the standard-normal tables.

Section 001
Section 002

Chapter 7: 26 October 2006

We continued our discussion on confidence intervals, and introduced the t distribution. The t distribution is used when we don't have the population standard deviation, and instead use the sample standard deviation s. All other assumptions remain in calculating the confidence intervals.

As expected, Lecture123 in Section 2 crashed. But, I have been able to recover most of it, so it is in two parts. The easiest way is to just listen to Section 001 lecture.

Section 001
Section 002 - part 1
Section 002 - part 2

Chapter 7: 24 October 2006

Today's topic was on interval estimation. Specifically, we talked about confidence intervals. We made assumptions on the distributional form, and that we knew σ. In the next class, we will relax some of these assumptions.

As with technology, things did go wrong. I completely lost Section 001 lecture. Thankfully, the Section 002 lecture worked. So, please use that to listen to your lectures.

Section 002

Review - Exam 2: 19 October 2006

Exam 2:

• 25 questions
• 6-8 questions from Chapter 1-3, rest from 4-6.
• 4-5 interpretation type questions. Know your definitions.

Section 001
Section 002

Chapter 6: 17 October 2006

We finished our discussion of sampling discussions by discussing properties of estimators.

• An estimator is unbiased if on average the value of the estimator is equal to the parameter it is estimating.
• An estimator is consistent if the larger the sample size, the closer is the value of the estimator to the parameter it is estimating.
• An estimator is efficient, if it has the smallest variance among other unbiased estimators.

Section 001
Section 002

Additional Problems for Exam 2 - Section 001

Chapter 6: 12 October 2006

We continued our discussion on sampling distributions. We saw four important points:

1. The average of all the sample means is equal to the population mean μ. That is, E(Xbar) = μ
2. The variance of the sample mean is equal to the variance of the population divided by the sample size. That is, σ²_xbar= σ²/n.
3. When the population is normally distributed, the sample mean distribution is also normal. That is, if X ~ N, Xbar ~ N.
4. When we don't know the distribution of the population, the distribution of the sample mean is approximately normally distributed for large sample sizes. This is called the central limit theorem.

Section 001
Section 002

Chapter 6: 10 October 2006

Today's lecture, or at least I tried to, was on Sampling Distributions. The idea behind sampling distributions is to understand the behavior of the sample mean. By doing that, we can then be able to predict the population mean more accurately. As an exercise, I asked each group to calculate the population mean (N=8). I then asked each group to take samples of size n=7, and for each sample, calculate the sample mean. You should have observed the following results:

The average of the sample means, i.e., E(Xbar) = μ, the population mean. In the next class, we will talk about other properties of the sampling distribution of the sample mean. Today's Section 001 lecture may be a little off the chart. Those who attended know why, I hope :-)

Section 001
Section 002

Chapter 4: 5 October 2006

We continued our discussion on the Normal Distribution. I have posted several additional problems for Exam 2, and also please look at the sample problems of my Wednesday, September 13, 2006 blog post. That contains more questions on the Normal Distribution. Here are the lectures:

Section 001
Section 002

Exam 1 Results

Count:	337
Average:	39.5
Median:	40.0
Maximum:	50.0
Minimum:	19.3
Standard Deviation:	5.87

Glossary

I have now added a link to a glossary of terms used in our class. This list is not complete, but I will continue to add terms as the semester progresses.

Chapter 4: 3 October 2006

Today, we continued our discussion on random variables, and specifically, continuous random variables. We started our discussion on Normal Distribution. Specifically, we saw the properties of the Normal distribution, and how to convert to Standard Normal, and then use the tables to determine probabilities. The lectures are here:

Section 001
Section 002

I also asked you to look over Exam 1. Please give me specific examples of the types of questions you are having trouble with it, and how I can help.