# Statistics! - PowerPoint PPT Presentation

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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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## 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

• 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

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

3

2

1

### 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

“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