| Lecture Notes | Audio/Video | Notes |
First Day |
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Chapter 1(pdf) |
Introduction | An introduction to Statistical Concepts, and how we test theories. Specifically, we introduce the concepts of Null and Alternative hypothesis, and discuss under what circumstances we choose one over the other. |
| Type I and II | Today, we discussed errors in decision making. We saw the difference between Type I (α) and Type II (β) errors. An example was used to illustrate how the decision rule affects these errors. We concluded by examining the concept of p-values. We will continue this discussion in the next lecture. | |
| P-Values | In today's lecture, we discussed p-values. We saw that once p-values were calculated, we could decide on which hypothesis to conclude by comparing it to the type I error. This lecture concludes the overview on the decision making process. That is, we discussed how to set up the hypothesis, collect data, analyze the results, and come to a conclusion. We will continue our discussion with sampling. | |
| Sampling | Today, we discussed sampling, and the importance of collecting good data. We discussed four different probability sampling methods: Simple random sampling, stratified sampling, cluster sampling, and systematic sampling. We also discussed Qualitative and Quantitative data. | |
Chapter 3(pdf) |
Measures of Location | Today, we saw different measures of summarizing data. We discussed measures of location like mean, median, and mode. We saw the need to define and look at data in different ways. |
| Measures of Variation | Today, we continued our
discussion on measures of location,
percentiles, and started measures of variation. Specifically, we saw
Range, Interquartile Range (IQR), Variance and Standard Deviation. We
discussed limiations of the range, and IQR, and started discussing
Variance. |
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| Standardization | Today, we completed chapter 3 by talking about Standard Deviation, and Coefficient of Variation. We also discussed linear transformations, and a special case, standardization. | |
Exam 1 Review |
Review | |
Exam 1 |
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Chapter 4(pdf) |
Probability Distributions | Today, we discussed random variables, and probability distributions. We saw examples of discrete distributions, and started discussing the Normal distribution. |
| Normal Distribution | 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. | |
| Normal Distributions (Cont.) | We continued our discussion on the Normal Distribution. | |
Chaper 6(pdf) |
Sampling Distributions | 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. |
| Sampling Distributions (Cont.) |
We continued our discussion on sampling distributions.
We saw four important points:
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| Estimators |
We finished our discussion of sampling discussions by
discussing properties of estimators.
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Exam 2 Review |
Review | |
Exam 2 |
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Chapter 7(pdf) |
Interval Estimation | 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. |
| Confidence Intervals | 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. | |
| Confidence Intervals (Cont.) | 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. | |
Chapter 8(pdf) |
Hypothesis Testing | Today, we started our discussion on Hypothesis Testing. We saw the three types of hypothesis, and definition of rejection region, critical values, and p-values. |
| Hypothesis Testing (Cont.) | We complete our discussion on hypothesis testing here. | |
Exam 3 Review |
Review: Hypothesis Testing | |
| Review: Confidence Intervals | ||
Exam 3 |
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Chapter 13(pdf) |
Linear Regression | We started our discussion on Linear Regression and Correlation. We saw examples of scatter plots, and correlation coefficients. |
| Linear Regression (Cont.) | We continued our discussion on Linear Regression and Correlation. We saw that the linear regression models are generally valid only for the range of data observed. | |
| Linear Regression (Cont.) | We conclude Linear Regression and Correlation in this chapter | |
Final Review |
Final Review | The Lecture here refers to Sample Exam 4, which is available on Vista. |
Exam 4 |
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Exam 5 |
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| Test | Chapters |
| Quiz 1 | 1 |
| Quiz 2 | 3 |
| Exam 1 | 1, 3 |
| Quiz 3 | 4 |
| Quiz 4 | 5 |
| Quiz 5 | 6 |
| Exam 2 | 1, 3-6 |
| Quiz 6 | 7 |
| Quiz 7 | 8 |
| Exam 3 | 1, 3-8 |
| Quiz 8 | 13 |
| Quiz 9 | 14 |
| Exam 4 | 1, 3-8, 13-14 |
| Exam 5 | 1, 3-8, 13-14 |