INTRODUCTION TO STATISTICS

CHAPTER 1:IMPORTANT TERMS & CONCEPTS

What is STATISTICS?Statistics is the science of

conducting studies to collect, organize, summarize, analyze and draw conclusions from data.

BRANCHES OF STATISTICS

STATISTICS

DESCRIPTIVE INFERENTIALStatistics that are collected , organized, etc. using data that already exists.

Generalizes from samples to populations. Makes predictions.

Identify each statistic as either descriptive or inferential.

1.Texting while driving can make you 23 times more likely to get in an accident.

2.In 2005, 50% of all paper products were recycled.

3.There are 50 to 70 confirmed shark attacks every year.

Variables and types of Data

Variables

Quantitative Qualitative

Discrete Continuous

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VARIABLES AND DATA

Variable – A characteristic or attribute that can assume different values.

Data – are the values or measures that the variable can assume.

Example: color – r, o, y, g, b, p Test - A, B, C, D, F

Variable Slide

QUANTITATIVE VARIABLES Variables that are numerical in

nature and can be ordered or ranked.

Examples: age, weight, height, body temperatures, etc.

Variable Slide

QUALITATIVE VARIABLES Variables that can be placed into

distinct categories.

Examples: colors, types of foods, seasons, brand names, etc.

QUANTITATIVE OR QUALITATIVE?

• The median age in PA is 40.1 years.

• Among the state's occupied housing units, 69.6% were owned, compared with 30.4% that were rented.

Variable Slide

DISCRETE VARIABLES Assume values that can be

counted. (usually whole numbers)

Examples: # of ______, shoe size, etc.

Variable Slide

CONTINUOUS VARIABLES Can assume all values between any two

specific values. They are usually obtained by measuring.

Examples: temperature, weight, length, etc.

DISCRETE OR CONTINUOUS?

• The median age in PA is 40.1 years.

• Among the state's occupied housing units, 69.6% were owned, compared with 30.4% that were rented.

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MEASUREMENT SCALES OF QUANTITATIVE VARIABLES

1. Nominal: No order or ranking ex: zip codes, locker #’s, phone #’s,

etc.

2. Ordinal: Data can be ranked; however precise differences between the ranks do not exist.

ex: 1st, 2nd, 3rd; letter grades, etc.

MEASUREMENTS CON’T

3. Interval: Ranks data; Precise differences between units of measure. No meaningful zero.

ex: temp., IQ, etc.

4. Ratio: Same as interval, but has a true zero

ex: weight, height, length, etc.

SAMPLING TECHNIQUES

1. Random: Samples selected by chance.

2. Systematic: Samples selected by numbering each subject and then selecting every kth number.

SAMPLING CON’T3. Stratified: Divide a population

into groups according to a common characteristic, then sample from each group.

4. Cluster: Samples taken from already intact groups (usually locative) within a population.

USES AND MISUSES