Statistics: art of collecting, classifying, interpreting, and reporting numerical information related to a particular subject.

Statistic: a numerical fact of interest in some context.

Population: the total set of objects or measurements that are of interest to a decision maker

2 SETS OF TECHNIQUES IN STATS:

---DESCRIPTIVE: focused on summarizing and presenting information. Data set: the set of all observations for a given project or purpose

---INFERENTIAL: designed for making estimates or inferences about the characteristics of a population based on the info found in one or more SAMPLE: representative subsets of the population INFINITE POPULATION: not realistically possible to count every single member of population ( cars in the world)

FINITE POPULATION: possible and realistic to count every member (weight/ gender of member)

CENSUS: a set of observations taken from every member of population

PARAMETER: a value for summarizing the measurements of a quantifiable feature of the population

SAMPLE STATISTIC: if the corresponding feature is measured in the sample

ESTIMATE= INFER

WHY USE SAMPLES?

1) Time constraints ( decision deadlines, trends, seasonality)

2) Cost constraints

3) Unknown population size

4) Destructive tests

5) Greater accuracy of some samples

REPRESENTATIVE SAMPLE: sample should be representative of population ( half balls blue, half balls green , even though the size of the balls are different in the big box, the small box has similarity to size and color)

DATA: sets of numeric or nonnumeric facts the represent records of observations … single observation is called DATUM…set of all observations for a given project or purpose is called DATA SET

CONSTANT: an observed characteristic whose value does not change over time or in a different experiment (like the circumference to its diameter)

VARIABLE: observed characteristic that can change from observation to observation (number of strikeouts in a game) RANDOM VARIABLE: something that happens by chance or is unplanned (cannot be known in advance) QUALITATIVE RANDOM VARIABLE: not a result of a numerical value, one observes a trait or characteristic that can be classified into a number of categories (NDP, GREEN PARTY, LIBERALS, NONE) QUANTITATIVE RANDOM VARAIBLE: random variable that takes values that varies in magnitude (discrete or continuous) DISCRETE RANDOM VARIABLE: restraints on values (cars can’t be 3.75 either 3 or 4) CONTINUOUS RANDOM VARIABLE: any value over a particular range of values (height)

DATA

VARIABLES CONSTANTS

RANDOM V NONrandom V

Qualitative RV Quantitative RV: Discrete & Continuous

LEVELS OF MEASUREMENT: (highest level to lowest level, contain more info to contain less info)

Quantitative: RATIO, INTERVAL Qualitative: ORDINAL, NOMINAL

NOMINAL: Ford, Chrysler, GM

ORDINAL: very bad, neutral bad, good

INTERVAL: 20 ,30, 40 only

RATIO: 5,10,15,20,25 etc

CHAPTER 2:

Scientific Approach: 1) Determine the objectives of the research 2) Determine the sources of the data 3) Design the data gathering instruments 4) Develop a sampling plan 5) Collect and analyze the data 6) Report and follow up on the findings

TYPES OF RESEARCH:

1) EXPLORATORY RESEARCH: conducted when very little is known as yet about the problem being studied ( pilot studies, focus groups)

2) CONCLUSIVE RESEARCH: conducted when research objectives are clear and the problems are unambiguous

Descriptive Research: used to describe the characteristics of a population, collected from sample or population ( business applications, marketing studies, forecasting)

Casual Research: exploring possible cause and effect relationships among the observed factors. (sciences)

TYPES OF DATA SOURCES:

1) PRIMARY: