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

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Published on 10 Sep 2020
School
Department
Course
Professor
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!
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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
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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
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