Jargon for Statistics I.
BEFORE MIDTERM
Descriptive versus inferential statistics,
Population, sample, data frame,
Statistic, parameter, Positive Skewness, Negative Skewness.
Time series data, cross-sectional data, non-stationary series
Interval data, ratio data, levels of measurement,
Nominal data, ordinal data, relative frequency,
Placebo Effect, Simpson's paradox, 
Requirments for a good graph (source, heading, axes labels, scale, legend etc)
( http://exploringdata.cqu.edu.au/sim_par.htm )
quantitative and qualitative data.
Frequency distribution, probability distribution,
Outlier, measures of dispersion  (variance, standard deviation,
Range, IQR, etc) Empirical Rule, Z-score, two-sigma Rule.
MAD (mean absolute deviation) 
Measures of central tendency  (mean, median, mode), 
Weighted Average and examples (Dow Jones Industrial, S&P 500, Consumer Price Index)
Percentile, coefficient of variation. Moving average. 

Stratified random sample.
Here a typical question will be:  
Population has 25 students of whom 15 are white and 10 black.  A stratified sample of size 10 should have how many whites / blacks?  
Answer: Let N=population size, N1=blacks=10, N2=whites =15, n=sample size=10.  Note that N1 /N  =(10/25)*10  or 4 blacks and 
How many whites in the sample? (N2/N)*n= (15/25)*10  or 6
Verify that 6+4=10.  We aim to have a representative sample

AFTER MIDTERM

FINAL exam Part I will be during the class on Monday, Dec. 10, 
as a webtest on the computer software

Part I of the Final Exam is set at 30% of your grade
will focus on the following lessons from the Hawkeslearning computer software

4.1, classical probability
4.2  Probability Rules
4.3  Counting Rules
Chapter 4 Review and test
5.1  Discrete Random Variable
5.2, Binomial
5.3, Poisson
5.4, hypergeometric

6.2  Reading the Normal curve Table
6.3, normal distribution word problems
6.4, find z
7.3, sampling dist means
7.4  Approximating the Binomial Dist using the Normal Distribution


Jargon Items for Part 2 of the Final Exam, set at around 9% of your grade
Sampling distribution  (size of the sample space when n items are chosen for a sample from a population of N items  
answer= N C n,  enumerate these if N and n are small)

Expected value of a random variable.  Sharpe Ratio
In what sense is it irrational to do Casino gambling?
  [Hint: check information in textbook on expected value, note that expected value is negative for gambling]

Statistical independence 
[Hint: check contingency tables discussion where it says that independence means unconditional probability equals the conditional probability]

Conditional probability,
Marginal probability, compound event, simple event,
Examples of discrete random variables and
   continuous random variables.
Uniform random variable.
Simple random sample.

Bayes Theorem Statement.  
Definitions: Posterior probability, Prior Probability, Likelihood  function 

Central Limit Theorem.
Standard Normal Variable (=z)
Sampling Frame.
FPC = Finite population correction = (N-n)/(N-1)
Correction for continuity  (Check textbook under continuity correction)