Statistics 270 Lecture 1

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Statistics 270 Lecture 1
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Today

• Course outline
• Introductory to statistics
• Some Definitions
• Descriptive statistics

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Introduction

• What is statistics?
• Discipline which deals with the collection,
  organization and interpretation of data.
• Done to answer questions of interest.

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Example (Pain Reduction and Reiki)

• Is Reiki an effective pain management tool?
• Reiki treatment is touch therapy used as an
  alternative to pain medication.
• A pilot study involving 20 volunteers
  experiencing pain was conducted.
• All treatments were provided by a certified Reiki
  therapist.
• Pain was measured using before and after the
  Reiki treatment.
• If study was repeated, would we see the same
  results?

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Example (Saving for Retirement)

• What are the attitudes of low wage earners about
  saving for retirement?
• Americans earning 35,000 or less were asked how
  they are likely to accumulate enough money to
  retire.
• What are the data?

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Some Definitions

• Interested in something about a population.
• Population is a collection of individuals.
• Describe individuals with data.
• Data sets contain information/facts relating to
  individuals.
• A variables are attributes of an individual
  (e.g., hair color, pain severity, ...).
• Distribution of a variable gives the values the
  variable can take and how often it takes on each
  value

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Some Definitions

• Can measure individuals a single time (e.g.,
  weight) to get a univariate data set
• Can measure several variables per individual
  multi-variate data
• Would like to measure a sample of indivuduals to
  make inference about the population inferential
  statistics

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Types of Variables

• Two types of variable
• Quantitative Variables take on numeric values for
  which addition and averaging make sense (height,
  weight, income,).
• Qualitative Variables each individual falls into
  a category (ethnicity, machine works or does not,
  ).
• Hair color
• Color preference (red1, blue2, green3)
• Length of time slept

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• Will first focus on descriptive statistics
  (graphical and numeric).
• Will move on to inferential statistics (test
  hypotheses).
• In either case, statistical tools are used to
  describe data and help answer scientific
  questions.

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Descriptive Statistics

• Want to describe or summarize data in a clear and
  concise way.
• Two basic methods graphical and numerical.

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Graphical Descriptions of Data

• Often, pictures tells entire story of data.
• Have different plots for the different sorts of
  variables.
• For Qualitative variables, will use bar-plots and
  pie charts.

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Bar Charts

• Variable values are the category labels
  (typically placed along the x-axis)
• Heights of bar is the count (percentage) of
  values falling in that category.
• Note bars are the same width!

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Example(retirement savings)

• A USA Today (Jan. 4, 2000) poll asked Americans
  who earn 35,000 or less how they expected to
  accumulate a 500,000 retirement nest-egg.
• The results are summarized in the frequency table
  below

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Pie Charts

• Variable values are the category labels
• Each category must appear on the plot
• Percentage of area of pie covered by pie is
  relative frequency or percent) of values falling
  in that category.
• Can easily see percentage for each category
• Note Less flexible than bar chart

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