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# Stats Final

Term | Definition |
---|---|

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 |

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