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Statistics!. Today. Check in How is that proposal coming along…? Finish up material from Tuesday Statistics. Statistics. Purpose for today and Tuesday Familiarize you with statistical terms and concepts Help you get a general sense of statistics What are they? Why do we use them?

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Presentation Transcript
today
Today
  • Check in
    • How is that proposal coming along…?
  • Finish up material from Tuesday
  • Statistics
statistics1
Statistics
  • Purpose for today and Tuesday
    • Familiarize you with statistical terms and concepts
    • Help you get a general sense of statistics
      • What are they?
      • Why do we use them?
      • What are some basic statistics?
what are they
What are they
  • Statistics are numbers that describe a sample
  • Parameters are numbers that describe a population
what are statistics for
What are statistics for?
  • We use them to describe our variables
    • Descriptive statistics
  • We use them to make inferences from samples to populations
    • Inferential statistics
  • This is why sampling and bias are so very important
basic descriptive statistics frequencies
Basic descriptive statistics-frequencies
  • Frequencies
  • Remember: variables are divided into categories
  • Frequencies tell us how many are in each type of category
    • Frequencies can refer to the raw number, or the percent
types of variables
Types of variables
  • Nominal
  • Ordinal
  • Interval
  • Ratio
nominal
Nominal
  • “named” variables
  • Can be represented with numbers but have no numerical qualities
    • There is no rank order
  • E.g. Red, blue, green cars
  • Male/female gender
nominal1
Nominal

green

blue

red

ordinal
Ordinal
  • Variables that have “order”
  • We assign them a rank, and may use numbers
  • We don’t actually know how much the ranks differ
  • E.g. bad, worse, worst
  • Some of the time, most of the time, all of the time
ordinal2
Ordinal
  • We should not manipulate ordinal variables numerically
    • Add, subtract, multiply
  • Because we don’t know if the categories are exact
  • But in practice ordinal variables are numerically manipulated all the time
interval
Interval
  • Interval data is rank ordered
  • We know that the space from one to the next is “equal”
  • E.g. temperature
  • But interval data has “no true zero”
    • There can’t be a true absence of the thing being measured
  • Like temperature, zero is “arbitrary”
  • We decide what zero is
interval1
Interval

“heat”

Less than 0

Even more less than 0

“0”

1

2

3

4

ratio data
Ratio Data
  • Like interval data
  • It is ordered
  • We know that the space from one rating to the next is “equal”
  • It has a “true zero”
  • There CAN be an absence of it
  • E.g. length, weight
    • You can have “zero” weight
ratio
Ratio

“Weight”

0

1

2

3

4

u seful terms
Useful terms
  • Univariate—referring to a single variable
  • Bivariate—two variables
  • Multivariate—more than two variables
  • Proportion—a percent
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