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November 29, 2005
Intro to Regression Analysis
Posted by bparke at 10:33 PM | Comments (0)
November 17, 2005
Review
Posted by bparke at 11:29 PM | Comments (0)
November 15, 2005
Review
Posted by bparke at 11:23 PM | Comments (0)
Difference in Two Means
Posted by bparke at 11:15 PM | Comments (0)
November 10, 2005
Hypoithesis Testing and Confidence Intervals
Problem 9.14
Problem 9.14 (continued)
Posted by bparke at 11:16 PM | Comments (0)
Stata 3
Stata 3 is due Thursday, December 1.
Posted by bparke at 08:32 AM | Comments (0)
November 08, 2005
Hypothesis Testing
The null hypothesis is what we are left with if the evidence is not strong enough to reject it in favor of the alternative hypothesis. We can make two kinds of errors.
A binomial example illustrates the concepts.
A similar Type I / Type II analysis can be conducted for a normal distribution.
Almost all empirical work in economics uses two-tailed alternative hypotheses.
Our ultimate goal is testing hypotheses about regression parameters.
Posted by bparke at 11:02 PM | Comments (0)
November 03, 2005
Confidence Intervals for a Sample Proportion
Estimating the probability parameter for a binomial distribution and constructing a confidence interval for that parameter are a classic application of what we have studied so far.
Posted by bparke at 08:26 PM | Comments (0)
Second Day of Statistics
The general theory:
My famous boat/net/target explanation for confidence intervals:
The basis for using Student's t distribution to determine the adjustment to the value from the standard normal table that is necessary because the sample variance of the data is used as an estimate of the unknown population variance.
Posted by bparke at 08:18 PM | Comments (0)
Problem 8.10
Part b asks you to work the problem backwards. In part a you are given the probability level and calculate a confidence interval. In part b you work backward to find that number given the confidence interval.
Posted by bparke at 08:13 PM | Comments (0)
November 01, 2005
First Day of Statistics
On the day after Halloween, we formally started our study of statistics.
Probability theory tells us how the world would look given the true model. Statistics is the process of making inferences about the true model given what we observe.
The Central Limit Theorem is the most important tool in statistics.
The classic problem is asking "What is mu?" Our estimate of mu should have two properties, unbiasedness and efficiency.
Estimators that take a weighted average with unequal weights are unbiased, but not efficient.
Once we have our estimate of mu, we construct a confidence interval that makes a formal probability statement about how accurate our estimate is.
In classical statistics, mu is a fixed, but unknown parameter. The confidence interval is random because the sample mean is random. There is a 95% probability that the random 95% confidence interval includes the fixed, but unknown mu.
Posted by bparke at 10:50 PM | Comments (0)