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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


Ordinal1
Ordinal

3

2

1


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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