sta 291 summer 2010
Download
Skip this Video
Download Presentation
STA 291 Summer 2010

Loading in 2 Seconds...

play fullscreen
1 / 23

STA 291 Summer 2010 - PowerPoint PPT Presentation


  • 64 Views
  • Uploaded on

STA 291 Summer 2010. Lecture 1 Dustin Lueker. Topics. Statistical terminology Descriptive statistics Probability and distribution functions Inferential statistics Estimation (confidence intervals) Hypothesis testing Simple linear regression and correlation. Why study Statistics?.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' STA 291 Summer 2010' - claus


An Image/Link below is provided (as is) to download presentation

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 - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
sta 291 summer 2010

STA 291Summer 2010

Lecture 1

Dustin Lueker

topics
Topics
  • Statistical terminology
  • Descriptive statistics
  • Probability and distribution functions
  • Inferential statistics
    • Estimation (confidence intervals)
    • Hypothesis testing
  • Simple linear regression and correlation

STA 291 Summer 2010 Lecture 1

why study statistics
Why study Statistics?
  • Research in all fields is becoming more quantitative
    • Research journals
    • Most graduates will need to be familiar with basic statistical methodology and terminology
  • Newspapers, advertising, surveys, etc.
    • Many statements contain statistical arguments
  • Computers make complex statistical methods easier to use

STA 291 Summer 2010 Lecture 1

lies damn lies and statistics
Lies, Damn Lies, and Statistics
  • Many times statistics are used in an incorrect and misleading manner
  • Purposely misused
    • Companies/people wanting to further their agenda
      • Cooking the data
        • Completely making up data
      • Massaging the numbers
        • Altering values to get desired result
  • Accidentally misused
    • Using inappropriate methods
      • Vital to understand a method before using it

STA 291 Summer 2010 Lecture 1

what is statistics
What is Statistics?
  • Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data
  • Applicable to a wide variety of academic disciplines
    • Physical sciences
    • Social sciences
    • Humanities
  • Statistics are used for making informed decisions
    • Business
    • Government

STA 291 Summer 2010 Lecture 1

general statistical methodology
General Statistical Methodology

STA 291 Summer 2010 Lecture 1

basic terminology
Basic Terminology
  • Population
    • Total set of all subjects of interest
      • Entire group of people, animals, products, etc. about which we want information
  • Elementary Unit
    • Any individual member of the population
  • Sample
    • Subset of the population from which the study actually collects information
    • Used to draw conclusions about the whole population

STA 291 Summer 2010 Lecture 1

basic terminology1
Basic Terminology
  • Variable
    • A characteristic of a unit that can vary among subjects in the population/sample
      • Ex: gender, nationality, age, income, hair color, height, disease status, state of residence, grade in STA 291
  • Parameter
    • Numerical characteristic of the population
      • Calculated using the whole population
  • Statistic
    • Numerical characteristic of the sample
      • Calculated using the sample

STA 291 Summer 2010 Lecture 1

data collection and sampling theory
Data Collection and Sampling Theory
  • Why take a sample? Why not take a census? Why not measure all of the units in the population?
    • Accuracy
      • May not be able to find every unit in the population
    • Time
      • Speed of response from units
    • Money
    • Infinite Population
    • Destructive Sampling or Testing

STA 291 Summer 2010 Lecture 1

example
Example
  • University Health Services at UK conducts a survey about alcohol abuse among students
    • 200 of the students are sampled and asked to complete a questionnaire
    • One question is “have you regretted something you did while drinking?”
      • What is the population?
      • What is the sample?

STA 291 Summer 2010 Lecture 1

flavors of statistics
‘Flavors’ of Statistics
  • Descriptive Statistics
    • Summarizing the information in a collection of data
  • Inferential Statistics
    • Using information from a sample to make conclusions/predictions about the population
      • Ex: using a sample statistic to estimate a population parameter

STA 291 Summer 2010 Lecture 1

example1
Example
  • The Current Population Survey of about 60,000 households in the United States in 2002 distinguishes three types of families: Married-couple (MC), Female householder and no husband (FH), Male householder and no wife (MH)
  • It indicated that 5.3% of “MC”, 26.5% of “FH”, and 12.1% of “MH” families have annual income below the poverty level
    • Are these numbers statistics or parameters?
  • The report says that the percentage of all “FH” families in the USA with income below the poverty level is at least 25.5% but no greater than 27.5%
    • Is this an example of descriptive or inferential statistics?

STA 291 Summer 2010 Lecture 1

scales of measurement
Scales of Measurement
  • Quantitative or Numerical
    • Variable with numerical values associated with them
  • Qualitative or Categorical
    • Variables without numerical values associated with them

STA 291 Summer 2010 Lecture 1

qualitative variables
Qualitative Variables
  • Ordinal
    • Disease status, company rating, grade in STA 291
      • Ordinal variables have a scale of ordered categories, they are often treated in a quantitative manner (A = 4.0, B = 3.0, etc.)
        • One unit can have more of a certain property than another unit
  • Nominal
    • Gender, nationality, hair color, state of residence
      • Nominal variables have a scale of unordered categories
        • It does not make sense to say, for example, that green hair is greater/higher/better than orange hair

STA 291 Summer 2010 Lecture 1

quantitative variables
Quantitative Variables
  • Quantitative
    • Age, income, height
      • Quantitative variables are measured numerically, that is, for each subject a number is observed
        • The scale for quantitative variables is called interval scale

STA 291 Summer 2010 Lecture 1

example2
Example
  • A study about oral hygiene and periodontal conditions among institutionalized elderly measured the following
    • Nominal (Qualitative): Requires assistance from staff?
      • Yes
      • No
    • Ordinal (Qualitative): Plaque score
      • No visible plaque
      • Small amounts of plaque
      • Moderate amounts of plaque
      • Abundant plaque
    • Interval (Quantitative): Number of teeth

STA 291 Summer 2010 Lecture 1

example3
Example
  • A birth registry database collects the following information on newborns
    • Birth weight: in grams
    • Infant’s Condition:
      • Excellent
      • Good
      • Fair
      • Poor
    • Number of prenatal visits
    • Ethnic background:
      • African-American
      • Caucasian
      • Hispanic
      • Native American
      • Other
  • What are the appropriate scales? Quantitative (Interval) Qualitative (Ordinal, Nominal)

STA 291 Summer 2010 Lecture 1

importance of different types of data
Importance of Different Types of Data
  • Statistical methods vary for quantitative and qualitative variables
  • Methods for quantitative data cannot be used to analyze qualitative data
  • Quantitative variables can be treated in a less quantitative manner
    • Height: measured in cm/in
        • Interval (Quantitative)
      • Can be treated at Qualitative
        • Ordinal:
          • Short
          • Average
          • Tall
        • Nominal:
          • <60in or >72in
          • 60in-72in

STA 291 Summer 2010 Lecture 1

other notes on variable types
Other Notes on Variable Types
  • Try to measure variables as detailed as possible
    • Quantitative
  • More detailed data can be analyzed in further depth
    • Caution: Sometimes ordinal variables are treated as quantitative (ex: GPA)

STA 291 Summer 2010 Lecture 1

discrete variables
Discrete Variables
  • A variable is discrete if it can take on a finite number of values
    • Gender
    • Nationality
    • Hair color
    • Disease status
    • Grade in STA 291
    • Favorite MLB team
      • Qualitative variables are discrete

STA 291 Summer 2010 Lecture 1

continuous variables
Continuous Variables
  • Continuous variables can take an infinite continuum of possible real number values
    • Time spent studying for STA 291 per day
      • 43 minutes
      • 2 minutes
      • 27.487 minutes
      • 27.48682 minutes
        • Can be subdivided into more accurate values
        • Therefore continuous

STA 291 Summer 2010 Lecture 1

examples
Examples
  • Number of children in a family
  • Distance a car travels on a tank of gas
  • % grade on an exam

STA 291 Summer 2010 Lecture 1

discrete or continuous
Discrete or Continuous
  • Quantitative variables can be discrete or continuous
  • Age, income, height?
    • Depends on the scale
      • Age is potentially continuous, but usually measured in years (discrete)

STA 291 Summer 2010 Lecture 1

ad