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pam 210 final

# pam 210 final - IQR Q3-Q1 1.5IQR possible outliers Standard...

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IQR: Q3-Q1, 1.5IQR: possible outliers Standard deviation- how close points are to the mean, measure of spread around the center of the data 1. subtract mean-each #, 2. square #s, add all and divide by n-1, 3. take square root standardize by finding z score: y- y(avg)/ SD residual: observed y-predicted y (data-model); mean of least sq residuals is always 0 lurking variables: not among explanatory and response, and may influence interpretation of relationships among those variables confounding variables: when their effects on a response variable cant be distinguished (could be explanatory or lurking) common response: changes in explanatory and response are cause by changes in lurking variables we can draw cause and effect conclusions in an experiment but not an observational study Chapter 3: Experimental design: 1.control: the effects of lurking variables on the response by comparing 2 or more treatments, 2. randomize: randomly assign experimental units to treatments, 3. repeat : do again to reduce chance of variation Something is statistically significant if the observed effect is so large that it would rarely occur by chance. Double blind - neither the subject nor experimenter know which treatment the subject receives Blocks are formed when using experimental units that are similar in some way that is important to the response. Randomization is then carried out separately within each block Matched pair designs- common form of blockin for comparing just 2 treatments Population - entire group of individuals that we want info about Sample- part of the population that we actually examine in order to gather info SRS- simple random sample of size n consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected Probability sample - sample chosen by chance. We must know what samples are possible and what chance or probability each sample has Parameter- describes the population. It is a fixed number, but in practice we don’t know its value Statistic- # that describes the sample. The value of this is known when we have taken a sample, but can change from sample to sample. We use statistic to estimate unknown parameter Sampling variability- value of statistic varies in repeated random sampling Sampling distribution - is the distribution of all possible means computed from all possible samples of a given size from a given population Bias- concerns the center of the sampling distribution. A statistic used to estimate a parameter is unbiased if the mean of its sampling distribution is equal to the true value of the parameter being estimated. (statistic close to mean has low bias) Variabiltity - described by the spread of its sampling distribution. The spread is determined by the sampling design and sample size n. when larger probability samples, have smaller spreads.

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