1
Chapter 2 Descriptive statistics: Numerical
We are still trying to understand the shape of our data and any relationships
between variables in our data. For example, is the distribution of income
symmetric, or not? Is # of years of schooling positively

1
Introduction
Bring calculator for DEMO.
Bring textbook.
Hand out survey.
Ch. 1- Definitons and Descriptive Graphs
Start with a Question:
Suppose we wish to know how many people in Ontario age 18 and older plan to
vote for the Liberal party in the next f

1
Chapter 3
Basic Ideas:
Ex: Roll a die. Dont know if 1,2,3,4,5, or 6 will occur.
Ex: Dont know how many patients will show up in a hospital emergency
room in any given day.
Ex: Dont know how many units of product will be ordered on any given
day.
Ex: Pos

Page |1
Ch.6 notes Sampling Distributions and Inferential Statistics
In Ch. 6, we learn how to make inferences about unknown population parameters
using sample statistics.
The sample statistic is called an estimator of the true unknown population
paramete

Page |1
2) Sample Proportion,
^
P
^
P
= the estimator for true Population proportion, P
X = # successes is a Bernoulli r.v.: with only 2 possible outcomes
success = 1
failure=0
P = percentage of successes in underlying population
^
P = x/n is the proporti

Page |1
Chapter 5 Continuous random variables
Two types of random variables:
Discrete random variable (Ch. 4)
has a countable # of outcomes all of which are uncertain. A discrete r.v. can take the
values 1.5, 2.75, etc, but it can only take a finite numbe

Page |1
Ch. 7 Notes Confidence intervals
Review of Ch. 6 - introduced point estimators and their sampling distributions
1. Concept of an estimator and its sampling distribution
^
1. Estimator ( of a population parameter () is a random variable (with
its o