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 (L1, L2) exponential model (L1, log y) power model (log x, log y) 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