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Fall Final Topics
by “Notecard”
Sampling
simple random sample, cluster
random sample, stratified random
sample, systematic random sample,
multistage
 voluntary response, convenience
 population, sample, census
 non-response, undercoverage,
response bias, wording

Experimental Design
(vocabulary)

Principals of good experiment

control, randomization, replication
experiment vs observation study
 treatment, factors, experimental units,
level
 placebo, placebo effect
 blind, double blind
 lurking variable, confounding variable

Experimental Design
completely randomized design
 randomized block design
 matched pair


always provide explanation of random
allocation, describe treatments,
compare in context
Simulations
Number assignment
 Description of a trial
 Stopping rule
 Summary of results


Be sure to clearly mark on number
line so reader can follow your
procedure
Center
mean
 median
 (mode)


resistance to outliers
Shape
symmetrical, bell shaped
 skewed right (mean>median)
 skewed left (mean<median)
 bi-modal, multi-modal
 uniform

Spread (variability)
minimum, maximum
 range
 interquartile range
 quartiles
 variance
 standard deviation: a measure of the
typical or average distance each point
is located from the mean
 formula sheet!

Unusual features
gaps, clusters – best seen by
histogram or dotplot
 outliers – best identified by boxplot

Q3 + 1.5 IQR
 Q1 – 1.5 IQR

Binomial distribution
binomial setting…fixed # of trials
 binomial formula – formula sheet
 pdf versus cdf….n,p,k
 mean of binomial – formula sheet
 standard deviation of binomial –
formula sheet
 calculator tricks when P(x>#)

Linear Regression vocab
explanatory, response (predicted)
 formulas for regression line, r, slope,
y-intercept
 regression line is always in context
 computer output
 centroid
 residual plot

Linear Regression vocab II
influential point
 extrapolation
 associations


causation, common response,
confounding
Linear Regression
interpretations
slope
 correlation coefficient
 coefficient of determination
 y-intercept
 residual plot

Nonlinear regression

linear model
ŷ  a  bx

(L1, L2)
exponential model (L1, log y)
ˆy  10a 10bx

power model
(log x, log y)
ˆy  10a x b

interpretations with “log” or “ln”
Probability rules
sample space, tree diagram
 multiplication rule
 verifying probability: 0<P<1, add to1
 complement rule
 general addition rule – formula sheet
 general multiplication rule
 disjoint/mutually exclusive

Conditional/independence

conditional probability
given on formula sheet
 no formula if given tables
 if 1 regular/2 conditional, use tree
diagram


proving independence

based on P(A|B) = P(A)
Random variables basics
discrete versus continuous random variable
 expected value (mean), variance –
formula sheet
 adding/subtracting constants
add/subtract the mean
 variance is unchanged


multiplying (or dividing) constants
mutiply/divide the mean
 mutiply/divide constant2 with variance

Combining Random
variables
Add or subtract the means
 Always add the variances
 Cannot add standard deviations –
must always convert!

Geometric
geometric setting … until 1st success
 no formulas provided
 no, no, no, no …yes

Normal distribution
Empirical rule: 68-95-99.7
 z-scores
 assessing normality

histogram/stemplot : bell shaped,
symmetrical, no unusual features
 boxplot: symmetrical, no outliers
 normal probability plot: linear, no
significant gaps

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