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# Stats Final

TermDefinition
Treatments A specific experimental condition applied to the units. ie) temp, fetilizer, amount of water
Factors It is a variable whose effect on the response is of interest such as Temp. and drug.
Level A specific value of the factor: Temp is 100*/ Dose is 10mg.
Confounding or lurking variable a hidden variable that effects the study, negatively. Bias
Principles of experimental design control, randomize, repeat/replication
completely randomized designs (CRD) all experimental units are allocated at random among treatments
block or stratified design subjects divided into groups or bloacks prior to experiments, to test hypotheses about differences between the groups
matched pairs design choose pairs of subjects that are closely matched ie) same sex, height, age and race. within each pair, randomly assign who will receive each treatment.
simple random sample a srs of size n is chosen in a way that every subset of n indicivuals has an equal chance to be the sample actually selected
stratified random sample the population is divided into sub population or strata. select a srs from each strata.
sigma of xbar (standard deviation of x) = sigma / sq.rt. of n
multiPstage sampling (2 stage sampling) stratified sampling without sampling from every group. random groups are sampled.
st. dev. of sampling distr. of p sq. rt. of p*(1-p)/n
P(at least...) 1 - P(...) P(at least one) = 1 - (1-P) P(at least two) = 1 - [P(0)+P(1)]
P (A or B) = P(A) + P(B) - P(A and B)
P (neither A nor B) = 1 - P (A or B)
Conditional Property/Dependent Event: P(A|B) = P(A*B) / P(B)
Random Variable Variable whos value is a numerical outcome of a random phenomenon
Discrete Random Variable X has a finite number of possible values
probability distribution function Px (x) = {1/8, 3/8, 3/8, 1/8}
z = (x - mu) / sigma
P(at least 1...) {x>=1} = P(1) + P(2) + P(3)...
mu = x - (z * sigma)
the mean value of a discrete (represented by an integer, whole #) randome variable x, denoted by the sum of x*p(x) ie) (1+.1) + (2* .5) + ...
the variance of a discrete random variable x, denoted by sigma^2, is computed by using the formula: sigma squared of x = the sum of x^2 * p(x) - mu^2
Created by: 563217868