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IQR: Q3Q1, 1.5IQR: possible outliers
Standard deviation how close points are to the mean, measure of spread around the center of the
data
1.
subtract meaneach #, 2. square #s, add all and divide by n1, 3. take square root
standardize by finding z score: y y(avg)/ SD
residual:
observed ypredicted y (datamodel); 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.
REDUCE BIAS, by using a random sample. REDUCE variability by using larger sample and
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This test prep was uploaded on 03/30/2008 for the course PAM 2100 taught by Professor Abdus,s. during the Spring '08 term at Cornell University (Engineering School).
 Spring '08
 ABDUS,S.

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