Statistics!

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# Statistics! - PowerPoint PPT Presentation

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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### 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?
• What are some basic statistics?
What are they
• Statistics are numbers that describe a sample
• Parameters are numbers that describe a population
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
• 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
• Nominal
• Ordinal
• Interval
• Ratio
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
Nominal

green

blue

red

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
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 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
Interval

“heat”

Less than 0

Even more less than 0

“0”

1

2

3

4

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

“Weight”

0

1

2

3

4

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