Stats Definition Notes
Stats Definition Notes k300
Popular in Elementary Statistics
Popular in Stats
This 2 page Class Notes was uploaded by Kaley Manspeaker on Wednesday January 27, 2016. The Class Notes belongs to k300 at Indiana University Purdue University - Indianapolis taught by Curtis Ramsey in Spring 2016. Since its upload, it has received 73 views. For similar materials see Elementary Statistics in Stats at Indiana University Purdue University - Indianapolis.
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Date Created: 01/27/16
Elementary Statistics Lecture 1 Definitions: Statistics-‐ the sciences of conducting studies to collect, organize, analyze, and draw conclusions from data • Variable-‐ A characteristic or attribute that can take on different values • Population-‐ All subjects to be studies • Sample-‐ A group of objects selected from the population • Descriptive-‐ Describe • Qualitative-‐ Distinct categories (good vs. bad) • Quantitative-‐ those that can be counted or measured • Discrete variables-‐ Assume values that can be counted (50-‐70 year olds) • Continuous variables-‐ Infinite number of values between any two specific values (height, weight, decimals, fractions) Boundaries-‐ Decrease the last number and add 5. Example: 5.55= 5.545 and 5.555 Keep the same number and add 5. • Nominal Level of Measurement-‐ Classifies data into mutually exclusive categories, in which no order or rankings may be assigned. (Zip code, eye color, nationality) • Ordinal Level of Measurement-‐ Data may be ranked, but precise differences between ranks do not exist (grades, 1 , 2 , 3 place) st nd rd • Interval Level of Measurement-‐ Ranks data and precise differences between measures exists, however there are no true zeros (IQ, temp, SAT scores) • Ratio Level of Measurement-‐ Just like interval but a true zero exists (height, weight, salary, age) • Random Sampling-‐ A sample in which all members of the population are equally likely to be chosen • Systematic Sampling-‐ A sample obtained by selecting every kth member of the population • Stratified Sampling-‐ Divide population into subgroups or strata, select subjects from each subject • Cluster Sample-‐ Divide population into sections of clusters and then use all members of that cluster in the sample • Convenience Sampling-‐ Ask who is convenient • Sampling error-‐ Difference between sample measures and the population measures • Non-‐sampling error-‐ Errors don’t have to do with your sample
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