Math Review Sheet, Fall 2008

Transcription

1 1 Descriptive Statistics Math Review Sheet, Fall 2008 First we need to know about the relationship among Population Samples Objects The distribution of the population can be given in one of the following forms: By graph Histogram Stemplot Bar graph Pie chart Summary numbers (condensed information): 1. Sample mean or median (the center) 2. Sample variance or sample standard deviation, or quartiles (the spread) You need to know the formulas for these quantities (special attention to the formula for s. Mathematical formula Probability A probability space (system) is defined through a triplet that includes a sample space, a collection of events and a probability measure. Make sure you understand the concepts of outcomes and events (corresponding to elements and subsets for a set). An outcome is an event but an event is not limited to an outcome. When we talk about probability of something, we refer to events, which contain outcomes but can be more complicated since they combine different outcomes in different ways. Some of the crucial concepts are: 1. Unions and intersection: the probabilities of them in regard to the probabilities of individual events involved are governed by the probability axioms (you need to be familiar with them). An important and intuitive formula is P(A B) = P(A) + P(B) P(A B) 2. Counting problems: these are some of the most challenging problems in this course. However, they are not our main focus. One particular question students ask all the time is which formula we should use: permutation or combination. The fact is that in many cases both would work, so there is not a clear answer. A useful strategy we can follow is to break the selection procedure into several stages, where each stage is clearly defined and manageable, then one can use the product rule to calculate the probability in question.

2 2 3. Conditional probability and independence should be studied together. You can interpret the formula P(A B) P(A B) = P(B) by noting that if A and B are independent then P(A B) = P(A) based on the above and the definition of independence. 4. Bayes theorem gives a way to compute the conditional probabilities from other conditional probabilities with roles interchanged: P(A j B) = P(B A j )P(A j ) k Random Variables: Discrete and Continuous i=1 P(B A i )P(A i ) Note: In formulas involving rv s, we use upper case letters for rv s and lower case letters for the values they assume. If you see a function with only lower case letter variables, you are dealing with a function of deterministic variables. The probability distribution is all you can ask for in a random variable. If you opt for functions or graphs, you can choose from Probability mass function (pmf) for discrete r.v., or probability density function (pdf) for continuous r.v.. Cumulative distribution function (cdf), and you need to pay attention to where the dots and circles are. Make sure you know how to convert these two functions for the same distribution. The expected value, or the expectation, or the mean of a quantity that depends on the rv (with the dependence specified by a formula), is the first important quantity you can compute based on the distribution. The formulas are E[h(X)] = h(x) p(x) for discrete rv where p(x) is the pmf E[h(X)] = D h(x) f (x)dx for continuous rv where f (x) is the pdf The variance measures the variability and the formula is V[h(X)] = E[h(X) 2 ] [E(h(X))] 2 Here is a list of distribution formulas we may need in the final: 1. Discrete: Binomial pmf: b(x;n, p) = n p x (1 p) n x, x Poisson pmf: p(x;λ) = e λ λ x. x! Notice in the formula the regions where the mass function assumes zero.

3 3 2. Continuous: Uniform distribution (x µ ) 1 2 Normal distribution: f (x) = 2πσ 2 e 2σ 2. Notice that the standardization procedure P[ X < x]=p[ Z < x µ ], where X has σ normal distribution with mean µ and standard deviation σ, and Z has the standard normal distribution. The cdf for the standard normal is Φ(z) which is given in table A.3. Exponential distribution: f (x) = λe λx for x 0 Joint distribution: pmf p(x, y) for discrete, usually given in a table, and pdf f (x, y) for continuous, often given by a formula. How do we obtain the CDF from either a pmf or a pdf? The marginal pmf or pdf describes the distribution if we don t care about one of the variables (that is, we cannot distinguish different values for one of the variables). Here is an easy way to see: the marginal pdf of X does NOT depend on y, therefore it is obtained by integrating the pdf f (x, y) with respect to y so all y-values are covered and the y-dependence vanishes. Conditional pdf: this is in the same spirit as conditional probability. For example, the conditional pdf of Y given that X = x is f Y X (y x) = f (x, y) f X (x) Expectation and Covariance: the complication is the covariance Cov(X,Y) = E[(X µ X )(Y µ Y )] and the correlation is ρ X,Y = Cov(X,Y) σ X σ Y will have 1 ρ X,Y 1. and we see that the scaling factors are there so we When we have several rv s, we are interested in the combination behavior and one of the simple cases is the linear combination of several rv s. If we are looking at a particular average involving the square root of a very large number of independent rv s with the same distribution, it will behave like a normal rv. This is one of the most important theorems in probability theory: the Central Limit Theorem. For other linear combinations, the expectation of the linear combination is the linear combination of the expectations, but when we look the variability, we need to look at the variance, rather than the standard deviation and we need to be careful about the signs:

4 4 V (a 1 X 1 +L+ a n X n ) = a 1 2 V (X 1 ) +L+ a n 2 V (X n ). Remember that in order to use this we need independence of the rv s. Point Estimation The general notation is ˆ θ for an estimator of the population parameter θ. There are different estimators for the same parameter, and we would like to choose one for a particular application. Features you need to look for are: unbiased and minimum variance. Sometimes you just cannot find one with all the desired features. A statistic is a quantity computed from the sample data. Different random samples will result in different values for the statistic. The main focus of the latter part of the course is to use statistics from sample data to infer properties of the population under study. Statistical Inference There are two approaches here: confidence interval and test of significance, each can be applied to the following problems: inference about the mean, the proportion and the variance of a population, inference about the difference between two population means and difference between two population proportions. 1. Confidence Intervals for One Sample: The basic form of confidence intervals is ˆ θ ± z * SE or ˆ θ ± t * SE where z * and t * are the critical z-value and t -value based on the confidence level and the type of intervals, for z-test and t -test respectively, and SE is the standard error. Inference about Large sample Sample not large µ, Normal distribution z-test. z α or z α / 2, SE = s/ n t -test. t α,n 1 or t α / 2,n 1, SE = s/ n µ, Distribution unknown Same as above N/A

5 5 p (proportion) Similar to the above except p ˆ (1 p ˆ ) SE = n Use binomial distribution 2. Test of Hypothesis for One Sample: The statistic is θ θ 0 SE, where θ is the value of the statistic from a sample and θ 0 is the null value. SE is the standard error with formula in the above table. The test to be used is also specified in the above table. The first step is always about stating the hypotheses. There is a decision to be made about the form of the alternative hypothesis, which is usually determined from the context of the problem. There are two equivalent approaches to test the hypotheses if you want to use your data to question the null hypothesis at the given level α: (a). Compute the statistic and compare with the corresponding critical value; (b). Compute the P- value and then compare with α. 3. Confidence Interval and Test of Hypothesis Involving Two Samples: First you have to decide if you should use a two-sample test or a paired data test. In the latter case the problem is similar to the one sample test as long as you keep the pairs. In the true two-sample test, the major change is the definition of the Standard Error. For the difference between the means: SE = s s 2 2. This is used in both the n 1 n 2 confidence interval and the two-sample t-procedure. For differences in proportions, we use different formulas for standard errors. In confidence interval computations, we use p ˆ SE = 1 (1 p ˆ 1 ) p + ˆ 2 (1 p ˆ 2 ), n 1 n 2 and in significance tests for comparing two proportions, with null hypothesis assuming that the proportions are the same, we use SE = where ˆ p is the pooled proportion. p ˆ (1 p ˆ )( ), n 1 n 2

6 6 4. Computation of the probability of the type-ii errors: the formulas will be supplied but you need to understand the definition of the type-ii errors and why we care about them. Good luck and have nice winter break!