# MGMC30H3 Lecture Notes - Lecture 1: Dont, Box Plot, The Big Issue

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Principles of Statistics (ST 201)

Summer 2020

Instructor: Erin Howardf

howarder@oregonstate.edu

TA: Tingyu Zu

zhuti@oregonstate.edu

TA: Emma Grossman

grossmen@oregonstate.edu

Lecture Notes

Week 1

Lesson 1: What is Statistics?

●“A branch of mathematics dealing with the collection, analysis, interpretation, and presentation

of masses of numerical data”

○‘The science of data’

●How do we use statistics:

○Explore data:

■Graphical displays → pie chart, histagrap, box chart, bars

■Summary statistics → average, mean, median

○Collecting data:

■Sampling

■Surveys

■Experiments: impose some sort of treatment on individuals or units to see

how that might actually change some response

○Inferring data:

■Taking a small sample and inferring it to a larger population using:

●Estimation

●Hypothesis Testing

●Who uses stat?

○Ecology, marketing, public health, public policy, etc

●Why learn stat?

○Better job candidate

○The ability to describe data

○Better decision maker : use observable data

○Informed consumer

○Save the world!

Lesson 2: Types of Variables

●The first step in dealing with data is to organize your thinking about the data

○Individual: an object described by data

○Variable: characteristic of the individual

■EX: a person, animal, or thing

■Two types of variables: categorical variable: places individuals into one

of several groups or categories or types. Quantitative variables: takes

numerical values for which arithmetic operations make sense

●To make decisions on what type of estimation, calculations, or

hypothesis tests we should use based upon what type of variable

we have

■EXAMPLE:

Individ

ual

Exam

Score

GPA

Gender

Year

1

79

3.3

M

Jr

2

88

2.9

M

Sr

3

92

3.8

F

Sop

h

4

87

3.7

M

Jr

5

88

3.4

F

Jr

●There are 4 diff variables: Individual, ExamScore, GPA, Gender

and Yr

○All real data will have some variability: meaning outcomes from each

individual can be different (variations)

■Natural variations

●EX: different exam score, along with GPA, gender and yr

●EX: CATEGORICAL variables: gender and yr (these c/n be

described using some sort of arithmetic / number)

●EX: QUANTITATIVE: exam score and GPA (have numbers and

can take averages of these values. Are numerical values that it

makes sense to use arithmetic for)

Lesson 3: Picturing Distributions with Graphs

●To examine a single variable, we want to graphically display its distribution

●Categorical and quantitative

●Distributions: describes what values a variable takes and how often it takes these values

○The distribution can be displayed using a variety of graphical tools

■EX; CATEGORICAL VARIABLES: pie chart or bar graph

■Pie chart:

●Each category represented by a slice of the pie

●Each slice is sized according to the proportion of the pie each

category represents

●Graph should include category

(piece) labels, count AND

percentages

●Category counts must add to total or percent must add to 100%

●All charts should include a descriptive title

●Bar Chart

●Each category represented by a bar

●Bars height represent the count or percent out of the total

●Graph should include category (bar) labels, counts AND percent

●Category counts must add to total or percent must add to 100%

●All charts should include a descriptive title

●Bar charts go w categorical variables