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by: Misha Damsky

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# Stats 111 Lecture & Reading Notes Week 1 Statistc 111

Marketplace > University of Massachusetts > Statistics > Statistc 111 > Stats 111 Lecture Reading Notes Week 1
Misha Damsky
UMass
GPA 3.83
Elementary Statistics
Brian Burrell

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Tuesday & Thursday's lectures, plus notes on the first chapter of reading (pages 3-10).
COURSE
Elementary Statistics
PROF.
Brian Burrell
TYPE
Class Notes
PAGES
2
WORDS
CONCEPTS
Statistics, Stats, stat, stats 111, statistics 111, stat 111, brian burrell
KARMA
25 ?

## 3

1 review
"Same time next week teach? Can't wait for next weeks notes!"
Ms. Kenyatta Pfeffer

## Popular in Statistics

This 2 page Class Notes was uploaded by Misha Damsky on Friday January 23, 2015. The Class Notes belongs to Statistc 111 at University of Massachusetts taught by Brian Burrell in Spring2015. Since its upload, it has received 159 views. For similar materials see Elementary Statistics in Statistics at University of Massachusetts.

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## Reviews for Stats 111 Lecture & Reading Notes Week 1

Same time next week teach? Can't wait for next weeks notes!

-Ms. Kenyatta Pfeffer

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Date Created: 01/23/15
Lecture Pquot Reading Notes Week 1 Jawduly232 15 quot1 12015 Tuesday Lecture 1 Overview of aspirin heart attack study Ch1 good example of a randomized experiment Table 11 Heart attacks No heart attacks Total Rate 1000 Aspirin 104 10933 11037 942 Placebo 189 10845 11034 1713 BPAamp cancerstudy Obama approval ratings Keyterms in the future margin of error level of confidence no definitions given this lecture Salkexperiment Spongebob in the news quotIt39s the Sugar Fol ksquot diabetes is independent of obesity amp is more closely linked with fructosesugar intake Statistics study of variability within populations making comparisons and finding correlations Topics to cover rates amp risks percent increase controllingforvariables Bradford Hil criteria KEY DEFINITIONS Population the set ofall members ofa clearly specified group eg all Toyota Camrys currently registered inthe world Sample a subset drawn from a population used to draw conclusions aboutthe whole Parameter a numbercharacterizinga population Statistica numbercharacterizingasample COURSE STRUCTURE Part Design of Experiments Part II Descriptive Statistics Partlll Probability Part IV Inferential Statistics 12015 READING pages 310 Statistics a collection of procedures and principles forgaining amp analyzing information in orderto help people make decisions when faced with uncertainty Population all members ofagroup Samplesubsetofa populationgroupthatis actually studied Observational study sample is simply observed eg pulse rate Randomized experiment sample39s members are randomly assigned to 10f 2 or more groups eg aspirin heart attack study 12215 Thursday Lecture 2 Examples of data that fluctuate overtime stocks presidential approval ratings Anytime you have a huge sample you will find statistical significance TYPES OF STUDIES stats Page 1 Randomized experiment a sample is collected and randomly divided into groups which receive different treatments Differences are measured Observational study does not involve experimenter intervention I issue otherfactors you have not controlled for cannotestablish a cause ampeffect relationship SurveyZpoll a set ofquestions posed to a sample Looks at how a population breaks down Census a set ofquestions posed to an entire population Metaanalysis gathering all published data on a topicamp analyzing it all together BradfordHill criteria a set of criteria used to establish cause ampeffect relationships based on observational studies Critical components ofa statistical study the sample population gtsamplingframe gtsamplesampling units measurements I daj a collection of numbers orinformation to which meaning has been attached I What was measured How was it measured What isthe explanatory variable What is the response variable What were the researchers expecting What did they observe variables I categorical variables the measurements are qualitative notquantitativenumerical D ordinal categorical variables have a natural order eg poor fair good excellent El nominal categorical variables have no natural order eg ice cream flavors I measurementvariablesquantitativenotcategorical D discrete measurement variables any set of numbers whose values can be listed ora set that has a limit on the numberof possible values D continuous measurement variables any set of numbers whose values could fall anywhere on acontinuum eg heightorweight stats Page 2

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