STAT*2060: Statistics for Business Decisions
Week 1 Lectures
1 What is Statistics?
1: A characteristic of a sample (we will come back to this shortly!)
2: The science of collecting, analyzing, interpreting, presenting, and making conclusions
Statistics is a science that allows us to answer questions such as:
•Does there exist a relationship between the amount of money spent on an advertising campaign,
and the resulting sales?
•Does one chemotherapy drug result in higher proportions of colon cancer survivors than another
•Can a grocery store chain use population demographics (population size, household income, house-
hold demographics (kids, no kids, pets, etc.)) to predict average monthly revenue?
To answer these (and other!) questions, we must:
2 Data Collection: Terminology and Techniques
Example: Why the hate-on for Pokemon Go? It’s making people healthy
http://www.cbc.ca/news/business/pokemon-go-exercise-1.3727123, Aug. 21, 2016.
An article on CBC.ca discusses the Pokemon Go craze, and brings into question the social media
backlash against Pokemon hunters when there appears to be a positive health beneﬁt associated with
playing the game. In particular, the article makes the statement “Sixty per cent of American millennials
surveyed in a new Manulife poll said playing the game has upped their activity levels”, and links to the
Let’s use this example to deﬁne some important terms:
In statistics, we can use information from our sample data, and our sample statistics, to make state-
ments about the corresponding population parameters. This is a branch of statistics known as inferential
statistics, which we will come back to in a few weeks.
The question then arises: “How do we obtain a sample, or collect sample data?”
2.1 Sampling Methods
There are various sampling methods that can be used to obtain a sample from a population. Which
method we choose can depend on a variety of factors (resources, what we are trying to sample, etc.).
However, in general the preferred sampling methods are those that incorporate some degree of ran-
domization, as this helps to limit any potential sampling bias that may exist.
Simple Random Sample (SRS):