# Stat 704 Data Analysis I Probability Review

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1 1 / 30 Stat 704 Data Analysis I Probability Review Timothy Hanson Department of Statistics, University of South Carolina

2 Course information 2 / 30 Logistics: Tuesday/Thursday 11:40am to 12:55pm in LeConte College 201A. Instructor: Tim Hanson, Leconte 219C. Office hours: Tuesday/Thursday 10-11:00am and by appointment. Required text: Applied Linear Statistical Models (5th Edition), by Kutner, Nachtsheim, Neter, and Li. Online notes at hansont/stat704/stat704.html Grading: homework 50%, two exams 25% each. Stat 704 has a co-requisite of Stat 712 (Casella & Berger level mathematical statistics). You need to be taking this, or have taken this already.

3 A.3 Random Variables 3 / 30 def n: A random variable is defined as a function that maps an outcome from some random phenomenon to a real number. More formally, a random variable is a map or function from the sample space of an experiment, S, to some subset of the real numbers R R. Restated: A random variable measures the result of a random phenomenon. Example 1: The height Y of a randomly selected University of South Carolina statistics graduate student. Example 2: The number of car accidents Y in a month at the intersection of Assembly and Gervais.

4 cdf, pdf, pmf Every random variable has a cumulative distribution function (cdf) associated with it: F(y) = P(Y y). Discrete random variables have a probability mass function (pmf) f(y) = P(Y = y) = F(y) F(y ) = F(y) lim F(x). (A.11) x y Continuous random variables have a probability density function (pdf) such that for a < b P(a Y b) = b a f(y)dy. For continuous random variables, f(y) = F (y). Question: Are the two examples on the previous slide continuous or discrete? 4 / 30

5 A.3 Expected value 5 / 30 The expected value, or mean of a random variable is, in general, defined as E(Y) = y df(y). For discrete random variables this is E(Y) = y f(y). y:f(y)>0 For continuous random variables this is E(Y) = y f(y)dy. (A.12) (A.14)

6 E( ) is linear 6 / 30 Note: If a and c are constants, E(a+cY) = a+ce(y). (A.13) In particular, E(a) = a E(cY) = ce(y) E(Y + a) = E(Y)+a

7 A.3 Variance The variance of a random variable measures the spread of its probability distribution. It is the expected squared deviation about the mean: var(y) = E{[Y E(Y)] 2 } Equivalently, var(y) = E(Y 2 ) [E(Y)] 2 Note: If a and c are constants, var(a+cy) = c 2 var(y) (A.15) (A.15a) (A.16) In particular, var(a) = 0 var(cy) = c 2 var(y) var(y + a) = var(y) Note: The standard deviation of Y is sd(y) = var(y). 7 / 30

8 Example 8 / 30 Suppose Y is the high temperature in Celsius of a September day in Seattle. Say E(Y) = 20 and var(y) = 10. Let W be the high temperature in Fahrenheit. Then ( ) 9 E(W) = E 5 Y + 32 = 9 5 E(Y)+32 = = 68 degrees. 5 ( ) 9 var(w) = var 5 Y + 32 = ( ) 9 2 var(y) = 3.24(10) = 32.4 degrees 2. 5 sd(y) = var(y) = 32.4 = 5.7 degrees.

9 A.3 Covariance 9 / 30 For two random variables Y and Z, the covariance of Y and Z is cov(y, Z) = E{[Y E(Y)][Z E(Z)]}. Note cov(y, Z) = E(YZ) E(Y)E(Z) (A.21) If Y and Z have positive covariance, lower values of Y tend to correspond to lower values of Z (and large values of Y with large values of Z ). Example: X is work experience in years and Y is salary in Euro. If Y and Z have negative covariance, lower values of Y tend to correspond to higher values of Z and vice-versa. Example: X is the weight of a car in tons and Y is miles per gallon.

10 Covariance is linear 10 / 30 If a 1, c 1, a 2, c 2 are constants, cov(a 1 + c 1 Y, a 2 + c 2 Z) = c 1 c 2 cov(y, Z) (A.22) Note: by definition cov(y, Y) = var(y). The correlation coefficient between Y and Z is the covariance scaled to be between 1 and 1: corr(y, Z) = cov(y, Z) var(y)var(z) (A.25a) If corr(y, Z) = 0 then Y and Z are uncorrelated.

11 Independent random variables 11 / 30 Informally, two random variables Y and Z are independent if knowing the value of one random variable does not affect the probability distribution of the other random variable. Note: If Y and Z are independent, then Y and Z are uncorrelated, corr(y, Z) = 0. However, corr(y, Z) = 0 does not imply independence in general. If Y and Z have a bivariate normal distribution then cov(y, Z) = 0 Y, Z independent. Question: what is the formal definition of independence for (Y, Z)?

12 Linear combinations of random variables 12 / 30 Suppose Y 1, Y 2,...,Y n are random variables and a 1, a 2,...,a n are constants. Then [ n ] n E a i Y i = a i E(Y i ). (A.29a) i=1 i=1 That is, E [a 1 Y 1 + a 2 Y 2 + +a n Y n ] = a 1 E(Y 1 )+a 2 E(Y 2 )+ +a n E(Y n ). Also, [ n ] var a i Y i = i=1 n n a i a j cov(y i, Y j ) i=1 j=1 (A.29b)

13 13 / 30 For two random variables (A.30a & b) E(a 1 Y 1 + a 2 Y 2 ) = a 1 E(Y 1 )+a 2 E(Y 2 ), var(a 1 Y 1 + a 2 Y 2 ) = a 2 1 var(y 1)+a 2 2 var(y 2)+2a 1 a 2 cov(y 1, Y 2 ). Note: if Y 1,...,Y n are all independent (or even just uncorrelated), then [ n ] n var a i Y i = ai 2 var(y i). i=1 i=1 Also, if Y 1,...,Y n are all independent, then ( n ) n n cov a i Y i, c i Y i = a i c i var(y i ). i=1 i=1 i=1 (A.31) (A.32)

14 Important example Suppose Y 1,...,Y n are independent random variables, each with mean µ and variance σ 2. Define the sample mean as Ȳ = 1 n n i=1 Y i. Then E(Ȳ) = E ( 1 n Y n Yn ) = 1 n E(Y 1)+ + 1 n E(Yn) = 1 n µ+ + 1 n µ ( ) 1 = n n µ = µ. var(ȳ) = var ( 1 n Y n Yn ) = 1 n 2 var(y 1)+ + 1 n 2 var(yn) ( ) 1 = (n) n 2σ2 = σ2 n. (Casella & Berger pp ) 14 / 30

15 A.3 Central Limit Theorem 15 / 30 The Central Limit Theorem takes this a step further. When Y 1,...,Y n are independent and identically distributed (i.e. a random sample) from any distribution such that E(Y i ) = µ and var(y) = σ 2, and n is reasonably large, Ȳ N ( µ, σ 2 ), n where is read as approximately distributed as. σ2 Note that E(Ȳ) = µ and var(ȳ) = n as on the previous slide. The CLT slaps normality onto Ȳ. Formally, the CLT states n( Ȳ µ) D N(0,σ 2 ). (Casella & Berger pp )

16 Section A.4 Gaussian & related distributions 16 / 30 Normal distribution (Casella & Berger pp ) A random variable Y has a normal distribution with mean µ and standard deviation σ, denoted Y N(µ,σ 2 ), if it has the pdf { 1 f(y) = exp 1 ( ) } y µ 2, 2πσ 2 2 σ for < y <. Here, µ R and σ > 0. Note: If Y N(µ,σ 2 ) then Z = Y µ σ N(0, 1) is said to have a standard normal distribution.

17 Sums of independent normals Note: If a and c are constants and Y N(µ,σ 2 ), then a+cy N(a+cµ, c 2 σ 2 ). Note: If Y 1,...,Y n are independent normal such that Y i N(µ i,σ 2 i ) and a 1,...,a n are constants, then ( n n a i Y i = a 1 Y 1 + +a n Y n N a i µ i, i=1 i=1 n i=1 a 2 i σ2 i Example: Suppose Y 1,...,Y n are iid from N(µ,σ 2 ). Then (Casella & Berger p. 215) Ȳ N ) (µ, σ2. n ). 17 / 30

18 A.4 χ 2 distribution 18 / 30 def n: If Z 1,...,Z ν iid N(0, 1), then X = Z Z 2 ν χ 2 ν, chi-square with ν degrees of freedom. Note: E(X) = ν & var(x) = 2ν. Plot of χ 2 1, χ2 2, χ2 3, χ2 4 PDFs:

19 A.4 t distribution 19 / 30 def n: If Z N(0, 1) independent of X χ 2 ν then T = Z X/ν t ν, t with ν degrees of freedom. Note that E(T) = 0 for ν 2 and var(t) = ν t 1, t 2, t 3, t 4 PDFs: ν 2 for ν

20 A.4 F distribution 20 / 30 def n: If X 1 χ 2 ν 1 independent of X 2 χ 2 ν 2 then F = X 1/ν 1 X 2 /ν 2 F ν1,ν 2, F with ν 1 degrees of freedom in the numerator and ν 2 degrees of freedom in the denominator. Note: The square of a t ν random variable is an F 1,ν random variable. Proof: [ ] 2 tν 2 Z = = Z 2 χ 2 ν /ν χ 2 ν/ν = χ2 1 /1 χ 2 ν/ν = F 1,ν. Note: E(F) = ν 2 /(ν 2 2) for ν 2 > 2. Variance is function of ν 1 and ν 2 and a bit more complicated. Question: If F F(ν 1,ν 2 ), what is F 1 distributed as?

21 Relate plots to E(F) = ν 2 /(ν 2 2) 21 / 30 F 2,2, F 5,5, F 5,20, F 5,200 PDFs:

22 A.6 normal population inference 22 / 30 A model for a single sample Suppose we have a random sample Y 1,...,Y n of observations from a normal distribution with unknown mean µ and unknown variance σ 2. We can model these data as Y i = µ+ǫ i, i = 1,...,n, where ǫ i N(0,σ 2 ). Often we wish to obtain inference for the unknown population mean µ, e.g. a confidence interval for µ or hypothesis test H 0 : µ = µ 0.

23 Standardize Ȳ to get t random variable 23 / 30 Let s 2 = 1 n 1 n i=1 (Y i Ȳ)2 be the sample variance and s = s 2 be the sample standard deviation. Fact: (n 1)s2 σ 2 = 1 n σ 2 i=1 (Y i Ȳ)2 has a χ 2 n 1 distribution (easy to show using results from linear models). Fact: Ȳ µ σ/ has a N(0, 1) distribution. n Fact: Ȳ is independent of s2. So then any function of Ȳ is independent of any function of s 2. Therefore [ ] Ȳ µ σ/ n 1 n σ 2 i=1 (Yi Ȳ)2 n 1 (Casella & Berger Theorem 5.3.1, p. 218) = Ȳ µ s/ n t n 1.

24 Building a confidence interval 24 / 30 Let 0 < α < 1, typically α = Let t n 1 (1 α/2) be such that P(T t n 1 ) = 1 α/2 for T t n 1. t n 1 quantiles t n 1 density 1 α α 2 α 2 t n 1(1 α 2) 0 t n 1(1 α 2)

25 Confidence interval for µ 25 / 30 Under the model Y i = µ+ǫ i, i = 1,...,n, where ǫ i N(0,σ 2 ), ( 1 α = P t n 1 (1 α/2) Ȳ µ ) s/ n t n 1(1 α/2) ( = P s t n 1 (1 α/2) Ȳ µ s ) t n 1 (1 α/2) n n ( = P Ȳ s t n 1 (1 α/2) µ Ȳ + s ) t n 1 (1 α/2) n n

26 Confidence interval for µ 26 / 30 So a (1 α)100% random probability interval for µ is Ȳ ± t n 1 (1 α/2) s n where t n 1 (1 α/2) is the (1 α/2)th quantile of a t n 1 random variable: i.e. the value such that P(T < t n 1 (1 α/2)) = 1 α/2 where T t n 1. This, of course, turns into a confidence interval after Ȳ = ȳ and s 2 are observed, and no longer random.

27 Standardizing with Ȳ instead of µ 27 / 30 Note: If Y 1,...,Y n iid N(µ,σ 2 ), then: and n ( ) Yi µ 2 χ 2 σ n, i=1 n ( Yi Ȳ i=1 σ ) 2 χ 2 n 1. First one is straightforward from properties of normals and definition of χ 2 ν; second one is intuitive but not straightforward to show until linear models...

28 Confidence interval example 28 / 30 Say we collect n = 30 summer daily high temperatures and obtain ȳ = and s = To obtain a 90% CI, we need, where α = 0.10 yielding t 29 (1 α/2) = t 29 (0.95) = (Table B.2), ( ) ±(1.699) (74.91, 80.42). 30 Interpretation: With 90% confidence, the true mean high temperature is between and degrees.

29 SAS example: Page / 30 n = 63 faculty voluntarily attended a summer workshop on case teaching methods (out of 110 faculty total). At the end of the following academic year their teaching was evaluated on a 7-point scale (1=really bad to 7=outstanding). proc ttest in SAS gets us a confidence interval for the mean.

30 SAS code ******************************** * Example 2, p. 645 (Chapter 15) ********************************; data teaching; input rating attend\$ if attend= Attended ; * only keep those who attended; datalines; 4.8 Attended 6.4 Attended 6.3 Attended 6.0 Attended 5.4 Attended 5.8 Attended 6.1 Attended 6.3 Attended 5.0 Attended 6.2 Attended 5.6 Attended 5.0 Attended 6.4 Attended 5.8 Attended 5.5 Attended 6.1 Attended 6.0 Attended 6.0 Attended 5.4 Attended 5.8 Attended 6.5 Attended 6.0 Attended 6.1 Attended 4.7 Attended 5.6 Attended 6.1 Attended 5.8 Attended 4.8 Attended 5.9 Attended 5.4 Attended 5.3 Attended 6.0 Attended 5.6 Attended 6.3 Attended 5.2 Attended 6.0 Attended 6.4 Attended 5.8 Attended 4.9 Attended 4.1 Attended 6.0 Attended 6.4 Attended 5.9 Attended 6.6 Attended 6.0 Attended 4.4 Attended 5.9 Attended 6.5 Attended 4.9 Attended 5.4 Attended 5.8 Attended 5.6 Attended 6.2 Attended 6.3 Attended 5.8 Attended 5.9 Attended 6.5 Attended 5.4 Attended 5.9 Attended 6.1 Attended 6.6 Attended 4.7 Attended 5.5 Attended 5.0 NotAttend 5.5 NotAttend 5.7 NotAttend 4.3 NotAttend 4.9 NotAttend 3.4 NotAttend 5.1 NotAttend 4.8 NotAttend 5.0 NotAttend 5.5 NotAttend 5.7 NotAttend 5.0 NotAttend 5.2 NotAttend 4.2 NotAttend 5.7 NotAttend 5.9 NotAttend 5.8 NotAttend 4.2 NotAttend 5.7 NotAttend 4.8 NotAttend 4.6 NotAttend 5.0 NotAttend 4.9 NotAttend 6.3 NotAttend 5.6 NotAttend 5.7 NotAttend 5.1 NotAttend 5.8 NotAttend 3.8 NotAttend 5.0 NotAttend 6.1 NotAttend 4.4 NotAttend 3.9 NotAttend 6.3 NotAttend 6.3 NotAttend 4.8 NotAttend 6.1 NotAttend 5.3 NotAttend 5.1 NotAttend 5.5 NotAttend 5.9 NotAttend 5.5 NotAttend 6.0 NotAttend 5.4 NotAttend 5.9 NotAttend 5.5 NotAttend 6.0 NotAttend ; proc ttest data=teaching; var rating; run; 30 / 30

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