Cumulative Stat Lab Notes
* Sort data by divorce rate before running analysis *
4/18/13
o Submit paper, dataset, do-file
o Dummy variables
Equal to either 1 or zero
Binary variable
Frosh = 1 if freshman
= 0 else
o Regression
Standardize comparisons

Chapter 8: Estimation: Additional Topics
Introduction
Comparisons
Same general form:
o Best point estimate ME
8.1 Confidence Interval Estimation of the Difference between Two Normal Population Means:
Dependent Samples
Compare population means
o Sample

Estimation: Single Population; Normal Distributions (7.1; 7.2)
Chapter 7 Introduction
Situations that require an estimate of some population parameter
Two estimation procedures
o Point estimate
o Confidence interval
Takes into account a measure of vari

Chapter 6: Sampling and Sampling Distributions
Introduction
Using a sample to predict general outcomes
Requires obtaining a proper sample from a population of items of interest that have
measured characteristics
o Sample observations can be shown to be

Continuous Random Variables: Uniform and Normal Distributions (Chapter 5)
Introduction
o Continuous random variables
Measures of time, distance, temperature, weight
Sales, investment, consumption, costs, revenues
Probability statements over ranges
E.

Binomial; Joint Distributions and Discrete Random Variables (4.4; 4.7; 4.5; 4.6)
4.4 Binomial Distribution
Bernoulli distribution
o Random exp gives rise to either success of failure
P(0) = 1 P
P(1) = P
P = probability of success in a single trial
o F

Symbols
N population
n sample
- population mean
2 population variance
population standard deviation
P population proportion
sample mean
2 sample variance
sample standard deviation
sample proportion
- the sum of
g or g geometric mean
g geometr

Sampling; Measures of Central Tendency and Variance (6.1; 2.1; 2.2)
6.1 Sampling from a Population
A population is generated by a process that can be modeled as a series of random
experiments
o The process of determining the sampling distribution uses ob

Probability Basics; Conditional Probabilities; Independence (3.1-3.3)
3.1 Random Experiment, Outcomes and Events
Random experiment a process leading to two or more possible outcomes, without
knowing exactly which outcome will occur
Basic outcomes the po

Joint and Marginal Probabilities; Bayes Rule (3.4-3.5)
3.4 Bivariate Probabilities
Two distinct sets of events, which we label A1, A2,AH and B1, B2,BK
o The events Ai and Bj are mutually exclusive and collectively exhaustive within
their sets
o Intersect

Introductions; Summation Notation; histograms (1.1; 1.5; 1.6)
1.1 Decision Making in an Uncertain Environment
Intro
o Define problem
o Determine what data are needed
o Collect the data
o Use statistics to summarize the data and make inferences and decisi

Expected Values and Variances of Discrete R.V. and Linear Function of Discrete R.V. (4.1-4.3)
4.1 Random Variables
Random variable a variable that takes on numerical values realized by the outcomes in
the sample space generated by a random experiment
o X

Covariance and Correlation (2.3; 2.4)
2.3 Weighted Mean and Measures of Grouped Data
Weighted mean - =
o wi = weight of the ith observation
o n = wi
Used for Weighted GPA (e.g. by credit hours)
o Credit hours = wi
o Value = xi
o Credit Hours * Value = w

Chapter 11: Simple Regression
11.1 Overview of Linear Models
Y = 0 + 1X
o Y = dependent/endogenous variable
o X = independent/exogenous variable
o 0 = Y-intercept
o 1 = slope
Least squares regression
o
Slope
o
Y-intercept
o
2
s=
Cov (x, y) = sxy =

Chapter 10: Hypothesis Testing: Additional Topics (10.1-10.3)
10.1 Tests of the Difference between Two Normal Population Means: Dependent Samples
Two Means, Matched Pairs
o=
o (Xn, yn)
o Pair means smaller variance
o Test against tn-1,
10.2 Tests of the

Chapter 9: Hypothesis Testing: Single Population
Introduction
Test validity of some claim by using sample data
9.1 Concepts of Hypothesis Testing
Define 2 alternatives
o counter-factual testing
Similarity to criminal jury trial
Null hypothesis - a hyp