- 142 Views
- Uploaded on
- Presentation posted in: General

Statistics!

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Statistics!

- Check in
- How is that proposal coming along…?

- Finish up material from Tuesday
- 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?

- Statistics are numbers that describe a sample
- Parameters are numbers that describe a population

- 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

- 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

- “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

- 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

- 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 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

“heat”

Less than 0

Even more less than 0

“0”

1

2

3

4

- 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

- Univariate—referring to a single variable
- Bivariate—two variables
- Multivariate—more than two variables
- Proportion—a percent