Day 01: STA205 - Introduction to Statistical Methods

Some thoughts for the course:

• There are three kinds of lies: lies, damned lies, and statistics

  (from the autobiography of Mark Twain)

• Attitude: your best friend, or worst enemy

1. Name
2. Year (Freshman, etc.)
3. Major
4. Where are you from?
5. Something special about yourself.

• Syllabus
• Tentative Schedule
• Homework exercises
• Intro to StatCrunch
• Course Objectives
• Important formulas

• What do we mean by "Statistics"? What is/are Statistics?
  • Statistics are numbers ("descriptive statistics")

    Definition 1: Statistics are numbers calculated from a collection of data

    "Data": items of information, either numerical or non-numerical (quantitative versus qualitative)

    Examples of statistics:

    • baseball "averages"
    • percent, proportion of NKU students that graduated from the same high school as one of their parents
    • average height of NKU students
    • percentages of students preferring each type of soft drinks offered on NKU campus

  • Statistics is science ("inferential statistics")

    Definition 2: Statistics is a science that deals with the collection and summary of information that is then used to make interpretations, decisions, estimates, predictions, etc.

    Examples:

    • Polio Vaccine example from text
    • Calling elections before some voters have even voted (and certainly before most votes have been counted)
      • What makes this easy?
      • Why did the Senatorial campaign in Minnesota take so long to call?
    • Heights of NKU students

• Chapter 2: Using graphs to describe data
  • Section 2.1: Two types of data
    1. Qualitative data -- categorical
       • Yes, No, Maybe
       • Pepsi, Coke, RC, 7-Up -- the uncola
    2. Quantitative data -- numerical data that "measures" some quantity
       • ages of students as freshmen
       • number of people in a line at the supermarket
       • time it takes to complete an exam
    3. Some variables are a little hazy: e.g. month of birth (ordinal, periodic)

  • Section 2.3: Histograms (2.2 is barcharts, for qualitative data)
    • Histograms communicate the relative importance of different intervals of the measured data
    • Construction:

      range of the data broken into convenient, non-overlapping "intervals" of equal width ("bins") count how many values are in each interval -- the "frequency" of an interval On a set of axes, rectangles are drawn above the intervals to a height equal to the frequency (although sometimes the heights are normalized in some way, e.g. to look like a probability -- so that if all the bins are added up, you get an area of 1).

    • Examples:
      • Cicada-Killer Wasps Right Wing Lengths
      • NKU student heights
      • Example from StatCrunch
    • Example of the interpretation of a histogram

• How do the experts do it? (The Gallup (polling) Organization)