1. Probability and Distributions A Brief Introduction

2. Random Variables Random Variable (RV): A numeric outcome that results from an experiment For each element of an experiment’s sample space, the random variable can take on exactly one value Discrete Random Variable: An RV that can take on only a finite or countably infinite set of outcomes Continuous Random Variable: An RV that can take on any value along a continuum (but may be reported “discretely”) Random Variables are denoted by upper case letters (Y) Individual outcomes for RV are denoted by lower case letters (y)

3. Probability Distributions Probability Distribution: Table, Graph, or Formula that describes values a random variable can take on, and its corresponding probability (discrete RV) or density (continuous RV) Discrete Probability Distribution: Assigns probabilities (masses) to the individual outcomes Continuous Probability Distribution: Assigns density at individual points, probability of ranges can be obtained by integrating density function Discrete Probabilities denoted by: p(y) = P(Y=y) Continuous Densities denoted by: f(y) Cumulative Distribution Function: F(y) = P(Y≤y)

4. Discrete Probability Distributions

5. Continuous Random Variables and Probability Distributions Random Variable: Y Cumulative Distribution Function (CDF): F(y)=P(Y≤y) Probability Density Function (pdf): f(y)=dF(y)/dy Rules governing continuous distributions:  f(y) ≥ 0  y   P(a≤Y≤b) = F(b)-F(a) =  P(Y=a) = 0  a

6. Expected Values of Continuous RVs

7. Means and Variances of Linear Functions of RVs

8. Normal (Gaussian) Distribution Bell-shaped distribution with tendency for individuals to clump around the group median/mean Used to model many biological phenomena Many estimators have approximate normal sampling distributions (see Central Limit Theorem) Notation: Y~N(  2 ) where  is mean and  2 is variance Obtaining Probabilities in EXCEL: To obtain: F(y)=P(Y≤y) Use Function: =NORMDIST(y, , ,1) Virtually all statistics textbooks give the cdf (or upper tail probabilities) for standardized normal random variables: z=(y-  )/  ~ N(0,1)

9. Normal Distribution – Density Functions (pdf)

10. Integer part and first decimal place of z Second Decimal Place of z

11. Chi-Square Distribution Indexed by “degrees of freedom ( )” X~   Z~N(0,1)  Z 2 ~    Assuming Independence: Obtaining Probabilities in EXCEL: To obtain: 1-F(x)=P(X≥x) Use Function: =CHIDIST(x, ) Virtually all statistics textbooks give upper tail cut-off values for commonly used upper (and sometimes lower) tail probabilities

12. Chi-Square Distributions

13. Critical Values for Chi-Square Distributions (Mean=, Variance=2 )

14. Student’s t-Distribution Indexed by “degrees of freedom ( )” X~t Z~N(0,1), X~   Assuming Independence of Z and X: Obtaining Probabilities in EXCEL: To obtain: 1-F(t)=P(T≥t) Use Function: =TDIST(t, ) Virtually all statistics textbooks give upper tail cut-off values for commonly used upper tail probabilities

16. Critical Values for Student’s t-Distributions (Mean=, Variance=2 )

17. F-Distribution Indexed by 2 “degrees of freedom (    )” W~F  X 1 ~   , X 2 ~    Assuming Independence of X 1 and X 2 : Obtaining Probabilities in EXCEL: To obtain: 1-F(w)=P(W≥w) Use Function: =FDIST(w, 1  2 ) Virtually all statistics textbooks give upper tail cut-off values for commonly used upper tail probabilities

19. Critical Values for F-distributions P(F ≤ Table Value) = 0.95