Probability Distributions
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Lecture 22: Bivariate Normal Distribution Distribution
6.5 Conditional Distributions General Bivariate Normal Let Z1; Z2 ∼ N (0; 1), which we will use to build a general bivariate normal Lecture 22: Bivariate Normal Distribution distribution. 1 1 2 2 f (z1; z2) = exp − (z1 + z2 ) Statistics 104 2π 2 We want to transform these unit normal distributions to have the follow Colin Rundel arbitrary parameters: µX ; µY ; σX ; σY ; ρ April 11, 2012 X = σX Z1 + µX p 2 Y = σY [ρZ1 + 1 − ρ Z2] + µY Statistics 104 (Colin Rundel) Lecture 22 April 11, 2012 1 / 22 6.5 Conditional Distributions 6.5 Conditional Distributions General Bivariate Normal - Marginals General Bivariate Normal - Cov/Corr First, lets examine the marginal distributions of X and Y , Second, we can find Cov(X ; Y ) and ρ(X ; Y ) Cov(X ; Y ) = E [(X − E(X ))(Y − E(Y ))] X = σX Z1 + µX h p i = E (σ Z + µ − µ )(σ [ρZ + 1 − ρ2Z ] + µ − µ ) = σX N (0; 1) + µX X 1 X X Y 1 2 Y Y 2 h p 2 i = N (µX ; σX ) = E (σX Z1)(σY [ρZ1 + 1 − ρ Z2]) h 2 p 2 i = σX σY E ρZ1 + 1 − ρ Z1Z2 p 2 2 Y = σY [ρZ1 + 1 − ρ Z2] + µY = σX σY ρE[Z1 ] p 2 = σX σY ρ = σY [ρN (0; 1) + 1 − ρ N (0; 1)] + µY = σ [N (0; ρ2) + N (0; 1 − ρ2)] + µ Y Y Cov(X ; Y ) ρ(X ; Y ) = = ρ = σY N (0; 1) + µY σX σY 2 = N (µY ; σY ) Statistics 104 (Colin Rundel) Lecture 22 April 11, 2012 2 / 22 Statistics 104 (Colin Rundel) Lecture 22 April 11, 2012 3 / 22 6.5 Conditional Distributions 6.5 Conditional Distributions General Bivariate Normal - RNG Multivariate Change of Variables Consequently, if we want to generate a Bivariate Normal random variable Let X1;:::; Xn have a continuous joint distribution with pdf f defined of S. -
Applied Biostatistics Mean and Standard Deviation the Mean the Median Is Not the Only Measure of Central Value for a Distribution
Health Sciences M.Sc. Programme Applied Biostatistics Mean and Standard Deviation The mean The median is not the only measure of central value for a distribution. Another is the arithmetic mean or average, usually referred to simply as the mean. This is found by taking the sum of the observations and dividing by their number. The mean is often denoted by a little bar over the symbol for the variable, e.g. x . The sample mean has much nicer mathematical properties than the median and is thus more useful for the comparison methods described later. The median is a very useful descriptive statistic, but not much used for other purposes. Median, mean and skewness The sum of the 57 FEV1s is 231.51 and hence the mean is 231.51/57 = 4.06. This is very close to the median, 4.1, so the median is within 1% of the mean. This is not so for the triglyceride data. The median triglyceride is 0.46 but the mean is 0.51, which is higher. The median is 10% away from the mean. If the distribution is symmetrical the sample mean and median will be about the same, but in a skew distribution they will not. If the distribution is skew to the right, as for serum triglyceride, the mean will be greater, if it is skew to the left the median will be greater. This is because the values in the tails affect the mean but not the median. Figure 1 shows the positions of the mean and median on the histogram of triglyceride. -
1. How Different Is the T Distribution from the Normal?
Statistics 101–106 Lecture 7 (20 October 98) c David Pollard Page 1 Read M&M §7.1 and §7.2, ignoring starred parts. Reread M&M §3.2. The eects of estimated variances on normal approximations. t-distributions. Comparison of two means: pooling of estimates of variances, or paired observations. In Lecture 6, when discussing comparison of two Binomial proportions, I was content to estimate unknown variances when calculating statistics that were to be treated as approximately normally distributed. You might have worried about the effect of variability of the estimate. W. S. Gosset (“Student”) considered a similar problem in a very famous 1908 paper, where the role of Student’s t-distribution was first recognized. Gosset discovered that the effect of estimated variances could be described exactly in a simplified problem where n independent observations X1,...,Xn are taken from (, ) = ( + ...+ )/ a normal√ distribution, N . The sample mean, X X1 Xn n has a N(, / n) distribution. The random variable X Z = √ / n 2 2 Phas a standard normal distribution. If we estimate by the sample variance, s = ( )2/( ) i Xi X n 1 , then the resulting statistic, X T = √ s/ n no longer has a normal distribution. It has a t-distribution on n 1 degrees of freedom. Remark. I have written T , instead of the t used by M&M page 505. I find it causes confusion that t refers to both the name of the statistic and the name of its distribution. As you will soon see, the estimation of the variance has the effect of spreading out the distribution a little beyond what it would be if were used. -
1 One Parameter Exponential Families
1 One parameter exponential families The world of exponential families bridges the gap between the Gaussian family and general dis- tributions. Many properties of Gaussians carry through to exponential families in a fairly precise sense. • In the Gaussian world, there exact small sample distributional results (i.e. t, F , χ2). • In the exponential family world, there are approximate distributional results (i.e. deviance tests). • In the general setting, we can only appeal to asymptotics. A one-parameter exponential family, F is a one-parameter family of distributions of the form Pη(dx) = exp (η · t(x) − Λ(η)) P0(dx) for some probability measure P0. The parameter η is called the natural or canonical parameter and the function Λ is called the cumulant generating function, and is simply the normalization needed to make dPη fη(x) = (x) = exp (η · t(x) − Λ(η)) dP0 a proper probability density. The random variable t(X) is the sufficient statistic of the exponential family. Note that P0 does not have to be a distribution on R, but these are of course the simplest examples. 1.0.1 A first example: Gaussian with linear sufficient statistic Consider the standard normal distribution Z e−z2=2 P0(A) = p dz A 2π and let t(x) = x. Then, the exponential family is eη·x−x2=2 Pη(dx) / p 2π and we see that Λ(η) = η2=2: eta= np.linspace(-2,2,101) CGF= eta**2/2. plt.plot(eta, CGF) A= plt.gca() A.set_xlabel(r'$\eta$', size=20) A.set_ylabel(r'$\Lambda(\eta)$', size=20) f= plt.gcf() 1 Thus, the exponential family in this setting is the collection F = fN(η; 1) : η 2 Rg : d 1.0.2 Normal with quadratic sufficient statistic on R d As a second example, take P0 = N(0;Id×d), i.e. -
Characteristics and Statistics of Digital Remote Sensing Imagery (1)
Characteristics and statistics of digital remote sensing imagery (1) Digital Images: 1 Digital Image • With raster data structure, each image is treated as an array of values of the pixels. • Image data is organized as rows and columns (or lines and pixels) start from the upper left corner of the image. • Each pixel (picture element) is treated as a separate unite. Statistics of Digital Images Help: • Look at the frequency of occurrence of individual brightness values in the image displayed • View individual pixel brightness values at specific locations or within a geographic area; • Compute univariate descriptive statistics to determine if there are unusual anomalies in the image data; and • Compute multivariate statistics to determine the amount of between-band correlation (e.g., to identify redundancy). 2 Statistics of Digital Images It is necessary to calculate fundamental univariate and multivariate statistics of the multispectral remote sensor data. This involves identification and calculation of – maximum and minimum value –the range, mean, standard deviation – between-band variance-covariance matrix – correlation matrix, and – frequencies of brightness values The results of the above can be used to produce histograms. Such statistics provide information necessary for processing and analyzing remote sensing data. A “population” is an infinite or finite set of elements. A “sample” is a subset of the elements taken from a population used to make inferences about certain characteristics of the population. (e.g., training signatures) 3 Large samples drawn randomly from natural populations usually produce a symmetrical frequency distribution. Most values are clustered around the central value, and the frequency of occurrence declines away from this central point. -
Basic Econometrics / Statistics Statistical Distributions: Normal, T, Chi-Sq, & F
Basic Econometrics / Statistics Statistical Distributions: Normal, T, Chi-Sq, & F Course : Basic Econometrics : HC43 / Statistics B.A. Hons Economics, Semester IV/ Semester III Delhi University Course Instructor: Siddharth Rathore Assistant Professor Economics Department, Gargi College Siddharth Rathore guj75845_appC.qxd 4/16/09 12:41 PM Page 461 APPENDIX C SOME IMPORTANT PROBABILITY DISTRIBUTIONS In Appendix B we noted that a random variable (r.v.) can be described by a few characteristics, or moments, of its probability function (PDF or PMF), such as the expected value and variance. This, however, presumes that we know the PDF of that r.v., which is a tall order since there are all kinds of random variables. In practice, however, some random variables occur so frequently that statisticians have determined their PDFs and documented their properties. For our purpose, we will consider only those PDFs that are of direct interest to us. But keep in mind that there are several other PDFs that statisticians have studied which can be found in any standard statistics textbook. In this appendix we will discuss the following four probability distributions: 1. The normal distribution 2. The t distribution 3. The chi-square (2 ) distribution 4. The F distribution These probability distributions are important in their own right, but for our purposes they are especially important because they help us to find out the probability distributions of estimators (or statistics), such as the sample mean and sample variance. Recall that estimators are random variables. Equipped with that knowledge, we will be able to draw inferences about their true population values. -
Linear Regression
eesc BC 3017 statistics notes 1 LINEAR REGRESSION Systematic var iation in the true value Up to now, wehav e been thinking about measurement as sampling of values from an ensemble of all possible outcomes in order to estimate the true value (which would, according to our previous discussion, be well approximated by the mean of a very large sample). Givenasample of outcomes, we have sometimes checked the hypothesis that it is a random sample from some ensemble of outcomes, by plotting the data points against some other variable, such as ordinal position. Under the hypothesis of random selection, no clear trend should appear.Howev er, the contrary case, where one finds a clear trend, is very important. Aclear trend can be a discovery,rather than a nuisance! Whether it is adiscovery or a nuisance (or both) depends on what one finds out about the reasons underlying the trend. In either case one must be prepared to deal with trends in analyzing data. Figure 2.1 (a) shows a plot of (hypothetical) data in which there is a very clear trend. The yaxis scales concentration of coliform bacteria sampled from rivers in various regions (units are colonies per liter). The x axis is a hypothetical indexofregional urbanization, ranging from 1 to 10. The hypothetical data consist of 6 different measurements at each levelofurbanization. The mean of each set of 6 measurements givesarough estimate of the true value for coliform bacteria concentration for rivers in a region with that urbanization level. The jagged dark line drawn on the graph connects these estimates of true value and makes the trend quite clear: more extensive urbanization is associated with higher true values of bacteria concentration. -
Random Variables and Applications
Random Variables and Applications OPRE 6301 Random Variables. As noted earlier, variability is omnipresent in the busi- ness world. To model variability probabilistically, we need the concept of a random variable. A random variable is a numerically valued variable which takes on different values with given probabilities. Examples: The return on an investment in a one-year period The price of an equity The number of customers entering a store The sales volume of a store on a particular day The turnover rate at your organization next year 1 Types of Random Variables. Discrete Random Variable: — one that takes on a countable number of possible values, e.g., total of roll of two dice: 2, 3, ..., 12 • number of desktops sold: 0, 1, ... • customer count: 0, 1, ... • Continuous Random Variable: — one that takes on an uncountable number of possible values, e.g., interest rate: 3.25%, 6.125%, ... • task completion time: a nonnegative value • price of a stock: a nonnegative value • Basic Concept: Integer or rational numbers are discrete, while real numbers are continuous. 2 Probability Distributions. “Randomness” of a random variable is described by a probability distribution. Informally, the probability distribution specifies the probability or likelihood for a random variable to assume a particular value. Formally, let X be a random variable and let x be a possible value of X. Then, we have two cases. Discrete: the probability mass function of X specifies P (x) P (X = x) for all possible values of x. ≡ Continuous: the probability density function of X is a function f(x) that is such that f(x) h P (x < · ≈ X x + h) for small positive h. -
The Central Limit Theorem (Review)
Introduction to Confidence Intervals { Solutions STAT-UB.0103 { Statistics for Business Control and Regression Models The Central Limit Theorem (Review) 1. You draw a random sample of size n = 64 from a population with mean µ = 50 and standard deviation σ = 16. From this, you compute the sample mean, X¯. (a) What are the expectation and standard deviation of X¯? Solution: E[X¯] = µ = 50; σ 16 sd[X¯] = p = p = 2: n 64 (b) Approximately what is the probability that the sample mean is above 54? Solution: The sample mean has expectation 50 and standard deviation 2. By the central limit theorem, the sample mean is approximately normally distributed. Thus, by the empirical rule, there is roughly a 2.5% chance of being above 54 (2 standard deviations above the mean). (c) Do you need any additional assumptions for part (c) to be true? Solution: No. Since the sample size is large (n ≥ 30), the central limit theorem applies. 2. You draw a random sample of size n = 16 from a population with mean µ = 100 and standard deviation σ = 20. From this, you compute the sample mean, X¯. (a) What are the expectation and standard deviation of X¯? Solution: E[X¯] = µ = 100; σ 20 sd[X¯] = p = p = 5: n 16 (b) Approximately what is the probability that the sample mean is between 95 and 105? Solution: The sample mean has expectation 100 and standard deviation 5. If it is approximately normal, then we can use the empirical rule to say that there is a 68% of being between 95 and 105 (within one standard deviation of its expecation). -
Calculating Variance and Standard Deviation
VARIANCE AND STANDARD DEVIATION Recall that the range is the difference between the upper and lower limits of the data. While this is important, it does have one major disadvantage. It does not describe the variation among the variables. For instance, both of these sets of data have the same range, yet their values are definitely different. 90, 90, 90, 98, 90 Range = 8 1, 6, 8, 1, 9, 5 Range = 8 To better describe the variation, we will introduce two other measures of variation—variance and standard deviation (the variance is the square of the standard deviation). These measures tell us how much the actual values differ from the mean. The larger the standard deviation, the more spread out the values. The smaller the standard deviation, the less spread out the values. This measure is particularly helpful to teachers as they try to find whether their students’ scores on a certain test are closely related to the class average. To find the standard deviation of a set of values: a. Find the mean of the data b. Find the difference (deviation) between each of the scores and the mean c. Square each deviation d. Sum the squares e. Dividing by one less than the number of values, find the “mean” of this sum (the variance*) f. Find the square root of the variance (the standard deviation) *Note: In some books, the variance is found by dividing by n. In statistics it is more useful to divide by n -1. EXAMPLE Find the variance and standard deviation of the following scores on an exam: 92, 95, 85, 80, 75, 50 SOLUTION First we find the mean of the data: 92+95+85+80+75+50 477 Mean = = = 79.5 6 6 Then we find the difference between each score and the mean (deviation). -
Annex : Calculation of Mean and Standard Deviation
Annex : Calculation of Mean and Standard Deviation • A cholesterol control is run 20 times over 25 days yielding the following results in mg/dL: 192, 188, 190, 190, 189, 191, 188, 193, 188, 190, 191, 194, 194, 188, 192, 190, 189, 189, 191, 192. • Using the cholesterol control results, follow the steps described below to establish QC ranges . An example is shown on the next page. 1. Make a table with 3 columns, labeled A, B, C. 2. Insert the data points on the left (column A). 3. Add Data in column A. 4. Calculate the mean: Add the measurements (sum) and divide by the number of measurements (n). Mean= ∑ x +x +x +…. x 3809 = 190.5 mg/ dL 1 2 3 n N 20 5. Calculate the variance and standard deviation: (see formulas below) a. Subtract each data point from the mean and write in column B. b. Square each value in column B and write in column C. c. Add column C. Result is 71 mg/dL. d. Now calculate the variance: Divide the sum in column C by n-1 which is 19. Result is 4 mg/dL. e. The variance has little value in the laboratory because the units are squared. f. Now calculate the SD by taking the square root of the variance. g. The result is 2 mg/dL. Quantitative QC ● Module 7 ● Annex 1 A B C Data points. 2 xi −x (x −x) X1-Xn i 192 mg/dL 1.5 2.25 mg 2/dL 2 188 mg/dL -2.5 6.25 mg 2/dL2 190 mg/dL -0.5 0.25 mg 2/dL2 190 mg/dL -0.5 0.25 mg 2/dL2 189 mg/dL -1.5 2.25 mg 2/dL2 191 mg/dL 0.5 0.25 mg 2/dL2 188 mg/dL -2.5 6.25 mg 2/dL2 193 mg/dL 2.5 6.25 mg 2/dL2 188 mg/dL -2.5 6.25 mg 2/dL2 190 mg/dL -0.5 0.25 mg 2/dL2 191 mg/dL 0.5 0.25 mg 2/dL2 194 mg/dL 3.5 12.25 mg 2/dL2 194 mg/dL 3.5 12.25 mg 2/dL2 188 mg/dL -2.5 6.25 mg 2/dL2 192 mg/dL 1.5 2.25 mg 2/dL2 190 mg/dL -0.5 0.25 mg 2/dL2 189 mg/dL -1.5 2.25 mg 2/dL2 189 mg/dL -1.5 2.25 mg 2/dL2 191 mg/dL 0.5 0.25 mg 2/dL2 192 mg/dL 1.5 2.25 mg 2/dL2 2 2 2 ∑x=3809 ∑= -1 ∑ ( x i − x ) Sum of Col C is 71 mg /dL 2 − 2 ∑ (X i X) 2 SD = S = n−1 mg/dL SD = S = 71 19/ = 2mg / dL The square root returns the result to the original units . -
UNIT NUMBER 19.4 PROBABILITY 4 (Measures of Location and Dispersion)
“JUST THE MATHS” UNIT NUMBER 19.4 PROBABILITY 4 (Measures of location and dispersion) by A.J.Hobson 19.4.1 Common types of measure 19.4.2 Exercises 19.4.3 Answers to exercises UNIT 19.4 - PROBABILITY 4 MEASURES OF LOCATION AND DISPERSION 19.4.1 COMMON TYPES OF MEASURE We include, here three common measures of location (or central tendency), and one common measure of dispersion (or scatter), used in the discussion of probability distributions. (a) The Mean (i) For Discrete Random Variables If the values x1, x2, x3, . , xn of a discrete random variable, x, have probabilities P1,P2,P3,....,Pn, respectively, then Pi represents the expected frequency of xi divided by the total number of possible outcomes. For example, if the probability of a certain value of x is 0.25, then there is a one in four chance of its occurring. The arithmetic mean, µ, of the distribution may therefore be given by the formula n X µ = xiPi. i=1 (ii) For Continuous Random Variables In this case, it is necessary to use the probability density function, f(x), for the distribution which is the rate of increase of the probability distribution function, F (x). For a small interval, δx of x-values, the probability that any of these values occurs is ap- proximately f(x)δx, which leads to the formula Z ∞ µ = xf(x) dx. −∞ (b) The Median (i) For Discrete Random Variables The median provides an estimate of the middle value of x, taking into account the frequency at which each value occurs.