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Elem Probability & Statistics

by: Mrs. Lizeth Bailey

Elem Probability & Statistics MATH 1530

Marketplace > Tennessee Tech University > Mathematics (M) > MATH 1530 > Elem Probability Statistics
Mrs. Lizeth Bailey
GPA 3.97

David Young

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David Young
Class Notes
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This 2 page Class Notes was uploaded by Mrs. Lizeth Bailey on Wednesday October 21, 2015. The Class Notes belongs to MATH 1530 at Tennessee Tech University taught by David Young in Fall. Since its upload, it has received 9 views. For similar materials see /class/225714/math-1530-tennessee-tech-university in Mathematics (M) at Tennessee Tech University.

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Popular in Mathematics (M)


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Date Created: 10/21/15
Ch 1 Data observations Statistics collections of methods for planning studies and experiments obtaining data and then organizing summarizing presenting population the complete collection of all elements scores people measurements and so on to be studied the collection is complete census collection of data from every member of a population sample subcollection of members collected from a population sample data must be collected in an appropriate way such as through a process of random selection otherwise inaccurateuseless Types of Data parameter measurement describing some characteristic of a population statistic numerical measurement describing some characteristic of a sample quantitative data numbers representing counts of measurements weight height qualitative data nonnumeric characteristic gender Quantitative data discrete and continuous discrete countablegtnumbersgtfinite Levels of Measurement Nominal names labels categories only CANNOT be ordered yes no undecided Ordinal data can be arranged but difference is meaninglessundeterminable ABCDF lnterval difference matter no natural zero starting point Years 2000 1776 and 1492 Ratio natural zero starting point zerono quantity present prices of books Ch 13 Critical Thinking Requires more common sense than math expertise Misuses voluntary response sample respondents decide to or not participate sample that are too small inaccurate probability graphs misleading format if read incorrectly pictographs exaggerate by increasing dimensions in proportion percentages 110 effort Other examples include oaded questions order ofquestions refusals correlations and causality selfinterest study precise numbers partial pictures deliberate distortions Ch 14 Design of Experiments Observational study wo attempting to modify subjects Experiment apply some treatment to subjects and observe Cross Sectional data are observes measured and collected at same time Retrospective case control data collected from past by going back in time Prospective longitudinal dara collected in future from groups sharing common factors Confounding experimenter is unable to distinguish between effects of different factors Blinding subject does not know he or she is receiving a treatment of placebo Blocks groups of subjects w similar characteristics Completely randomized experimental design random selection Rigorously controlled decision very carefully chosen Replication repetition of experiment Sample size large enough based on appropriate method Random sample


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