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Chap
1
2
3
4
5
Important
- Population vs sample (parameter vs statistic)
- 2 branches of statistics
- qualitative vs quantitative
- observational study vs experiment
- sampling approaches
Distributions: frequency, relative frequency, cumulative
Graphical displays: histograms, stem-and-leaf, dotplots
(when to use which)
- measures of center: mean, median mode (how to
calculate [formulas!], when to use)
Distribution shapes: uniform, skewed, symmetric …
- measures of variation: range, variance (population and
sample), std dev (pop and sample); how to calculate
(formulas!))
- Empirical Rule
- quartiles (how to calculate and interpret)
- percentiles (how to interpret)
- 5 number summary and box-and-whisker
- IQR (how to calculate and interpret)
- z-score/standard score (how to calculate and interpret)
!!
- Probability – Fundamental Counting Principle, trees
- Classical, Empirical, Subjective probability
- Probability distribution: what is it, properties, mean
- Complements
- Independent vs dependent events
- Mutually exclusive events
- Addition Rule for independent events
- Venn diagram
- Combination & Permutation (when to use, how to
calculate)
- Random variable: discrete vs continuous
- Probability distribution (what is it, properties, calculate
mean, expected value)
- Binomial distribution: requirements, how to calculate
(formula!) probabilities, calculate mean, calculate std
dev (and interpret), shapes
- Names of other discrete probability distributions
- Normal distribution (continuous!) – properties, how to
draw curves with different means/std devs
- How to use Standard Normal Table (inside and out)
- How to find Probabilities using table
- Get z-score given area and/or percentile
- Find x-value corresponding to z-score
- Find x-value corresponding to a given probability
Not So Important …
- Levels of measurement
(nominal, ordinal …)
- Use of random # table
- replication, placebo,
confounding variable
- identifying bias sampling/survey
- class limits, class boundaries
- frequency polygon, ogive, pie
chart, tine series
- weighted mean
- Chebychev’s Theorem
- std dev of grouped data
- Law of large numbers
- Conditional probability formula
- Distinguishable Permutations
- how to calculate variance or std
dev of a discrete random variable
- details of poisson, geometric
distributions
- Normal approx. to binomial
6
7
8
9
- sampling distribution of sample mean
- Central Limit Theorem
- Finding probability for x and mean of x
- point estimate vs confidence interval for population
mean
- margin of error
- use of t Table
- how to calculate (formula) confidence interval
(expression) using t or z; when to use which
- how to interpret (inferential!)
- Hypothesis test for population mean – what is it all
about?
- What is α?
- What is P-value?
- Type I error?
- Steps to reach decision
- P-Value approach (when to use)
- test statistic (z or t?)
- Rejection region (critical value) approach (when to use)
- Interpretation of decision
- Hypothesis test for difference between two population
means
- Independent vs dependent/paired samples (what test
statistic to use when) – *do not* need to know formulas
for test statistics (but *do* need to know how to use
them)
- *do* need to know (formulas) how to find df for tests of
two populations
- x/y, independent/dependent, explanatory/response
variables
- meaning, interpretation, and properties of “r”; *do not*
need to know formula for r
- inferential test for significance of r (“rho”): using table
11 ~and~ using hypothesis testing approach (t-test; do
*not* need to know formula for test statistic, but know
how to use it)
- correlation versus causation
- regression line: what is it? when do we find it? how do
you use it?. *do not* need to know formulas for “m” and
“b” (open book portion). Understand worksheet done in
class
- residual: what is it? how to find it?
-coefficient of determination: *do* need to know
formula, and interpret
- multiple regression: how to use it to predict “y” (what
variable has greatest effect)
- minimum sample size needed
for confidence interval
- confidence interval for
population proportion
- confidence interval for variance
- confidence interval for standard
deviation
- Type II error
- Hypothesis test for population
proportions
- Hypothesis test for population
variance
- Hypothesis test for population
std dev
- Hypothesis test for difference
between two population
proportions
- standard error of estimate for a
regression line
- prediction interval for y
- constructing a multiple
regression equation
Stay tuned!
10
- There *will* be a chi-square test (open book)
- There *will* be a test of equal variances; *do need* to
know how to construct F statistic and df’s (formula)
- ANOVA