1. Statistics
Chapter 1
Sections

2. Definitions:
Population – the complete collection of all elements to be studied
Sample – a subcollection of elements taken from a population

3. Example – A population could be every student at Riverview High School while a sample would be only the students in my 5th hour class or only the Seniors or only the male students. Each sample is a subgroup taken from the original population.

4. Parameter – a numerical measurement describing some characteristic of a population.
Example – 32% of the students at RCHS plan to attend the sock hop after the football game.
Hint – Population and parameter both start with “p”.

5. Statistic – a numerical measurement describing some characteristic of a sample
Example – Based on a sample of 100 students at RCHS, 45% prefer pizza over french fries.
Hint – Sample and statistic both start with “s”.

6. Quantitative data – numbers that represent counts or measurements.
Examples – incomes of college graduates, scores on Chapter 1 test, amount of time spent studying each evening, GPAs

7. Qualitative data – nonnumeric data
Examples – favorite color, favorite food, gender

8. Discrete data – the number must be a finite number (has an ending), typically this is data that must be a whole number
Examples – number of students, number of desks, number of books, number of puppies

9. Continuous data – the data value corresponds to a continuous scale that covers a range of values (i.e. the number can be taken out to many decimal places and recorded more and more accurately…basically a decimal number)
Examples – any type of measurement, height, weight, amounts of liquid, etc.

10. Four types of measurement:
Nominal level of measurement – data that consists of names, labels, or categories. Data cannot be arranged in order.
Examples – names of pets, colors, foods, television shows, 5th hour classes

11. Ordinal level of measurement – data that can be arranged in some order but differences are meaningless
Examples – grade in a class, A is better than B but B-A is meaningless
rankings – poor, good, better, best

12. Interval level of measurement – numerical data with no natural starting point (i.e. zero has a meaning) Can have negatives.
Example – The best example of this is temperature. Zero degrees does not mean a lack of temperature. It just means that it is REALLY COLD! Another example is years…1970, 2008, etc. Time did not begin in the year 0!

13. Ratio level of measurement – numerical data that has zero as a starting point. There is nothing below zero.
Examples – weight, cost, income, distance