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Lecture 4

Lecture 4. Confidence Intervals. Lecture Summary. Last lecture, we talked about summary statistics and how “good” they were in estimating the parameters Risk, bias, and variance Sampling distribution

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Lecture 4

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  1. Lecture 4 Confidence Intervals

  2. Lecture Summary • Last lecture, we talked about summary statistics and how “good” they were in estimating the parameters • Risk, bias, and variance • Sampling distribution • Another quantitative measure of how “good” the statistic is called confidence intervals (CI) • CIs provide an interval of certainty about the parameter

  3. Introduction • Up to now, we obtained point estimates for parameters from the sample • Examples: sample mean, sample variance, sample median, sample quantile, IQR, etc. • They are called point estimates because they provide one single point/value/estimate about the parameter • Mathematically: a single point! • However, suppose we want a range of possible estimates for the parameter, an interval estimatelike [ • Mathematically: and

  4. Two-Sided Confidence Intervals • Data/Sample: • is the parameter • Two-Sided Confidence Intervals: A -confidence interval is a random interval, from the sample where the following holds • Interpretation: the probability of the interval covering the parameter must exceed • It is NOT the probability of the parameter being inside the interval!!! Why? Confidence Level

  5. Comments about CIs • Pop quiz 1: What is the confidence level, for confidence interval? • Thus, for any level CI would be a valid (but terrible) CI • Pop quiz 2: What is the confidence level, , for CI where is any number? • Pop quiz 3: Suppose you have two confidence intervals and . If the first CI is shorter than the second, what does this imply? • If is the same for both intervals, what would this imply about the short interval (in comparison to the longer interval)? • Main point: given some confidence level , you want to obtain the shortest CI

  6. CIs for Population Mean • Case 1: If the population is Normal and is known CI: Hint: Use sampling distribution of • Case 2: If the population is not Normal and is known Approximate CI: Hint: Use CLT of

  7. What if the variance,,is unknown?

  8. t Distribution • Formal Definition: A random variable has a t-distribution with degrees of freedom, denoted as , with the probability density function where is a gamma function • Useful Definition:Consider the following random variable where and and are independent. Then, • “Quick and Dirty” Definition: If , i.i.d., then Notice that you can transform into a standard Normal You will prove the relation between the two in the homework From lecture 3, is with some constant multipliers

  9. Property of the t Distribution • The t-distribution has a fatter tail than the normal distribution (see picture from previous slide) • Consequences: The “tail” probabilities for the t distribution is bigger than that from the normal distribution! • If the degrees of freedom goes to , then Proof: CLT! • This means that with large sample size , we can approximate with a standard normal distribution for large • General rule of thumb for how large should be:

  10. CIs for Population Mean • Case 3: If the population is Normal and variance is unknown CI: Hint: Use the “quick and dirty” version of the t-distribution • Case 4: If the population is not Normal and variance is unknown (i.e. the “realistic” scenario) Approximate CI: Hint: Use CLT! • Demo in class

  11. Summary of CIs for the Population Mean Fixed width CI Variable width CI

  12. CIs for Population Variance • Case I: If the population is Normal and all parameters are unknown [] • Hint: Use the sampling distribution for • Case II: (Homework question) If the population is Normal and the population mean is known. • Hint: Use and the sampling distribution related to it!

  13. Lecture Summary • Another quantitative measure of how “good” the statistic is called confidence intervals (CI) • CIs provide an interval of certainty about the parameter • We derived results for the population mean and the population variance, under various assumptions about the population • Normal vs. not Normal • known variance vs. unknown variance

  14. Extra Slides

  15. One-Sided Confidence Intervals • One-Sided Confidence Intervals: A -confidence interval is a random interval, from the sample where the following holds • One-Sided Confidence Intervals: A -confidence interval is a random interval, from the sample where the following holds

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