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MTH 125 FYS Chapter 1 Notes

by: Ronni Peters

MTH 125 FYS Chapter 1 Notes MTH 125

Marketplace > Converse College > Mathmatics > MTH 125 > MTH 125 FYS Chapter 1 Notes
Ronni Peters

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About this Document

These notes cover Chapter 1- Section 1.1-1.4
FYS: This, Thats and Stats
Dr. Hoeberling
Class Notes
Statistics, Math, Stats




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This 4 page Class Notes was uploaded by Ronni Peters on Monday September 12, 2016. The Class Notes belongs to MTH 125 at Converse College taught by Dr. Hoeberling in Fall 2016. Since its upload, it has received 7 views. For similar materials see FYS: This, Thats and Stats in Mathmatics at Converse College.


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Date Created: 09/12/16
Chapter 1 Notes  Section 1.1­ Sampling  ● Statistics  ○ Collecting data, displaying data and drawing conclusions  ● Population:  ○ The entire collection (ex: Clemson University)  ● Sample:  ○ Subset of the population (ex: Athletes at Clemson)  ● Sample Types  ○ Simple Random Sample (SRS):​ everything has an equal chance of being selected  (ex: lottery)  ○ Stratified Sample:​ population divided into groups that are similar then from the  groups a SRS is taken  ○ Cluster Sample:​ grouped into categories then pick one whole group to sample  ○ Systematic Sample:​ items are ordered then decided how frequently to pick items  (ex: every 5th item)  ○ Voluntary Sample:​ asking people to give you an opinion; not reliable  ● Statistics and Parameters  ○ S​tatistic: describes a ​s​ample  ○ P​arameter: describes a ​p​opulation  Section 1.2­ Types of Data  ● Information is collected on ​individuals​ (ex: people, places, animals, etc)  ● The characteristic of the data collected is considered the ​variable​ (ex: hair color, race,  political views, gender, etc)  ● Values of the variables are the ​data  ● Quantitative and Qualitative Variables  ○ Quantitative Variables:​ numeric values (ex: height, goals scored, etc)  ■ Discrete:​ can be listed (ex: number of fouls in a game)  ■ Continuous:​ have to be given in an interval (ex: height)  ○ Qualitative Variables:​ categorical variables (ex: colors, race, etc)  ■ Ordinal: ​ have a natural ordering (ex: A, B, C)  ■ Nominal:​ no natural ordering (ex: blue, red, green)  Section 1.3­ Design of Experiments  ● Steps  1. Identify the variables and population  2. Develop a plan for collecting the data  3. Collect data  4. Describe the data using descriptive statistic techniques  5. Interpret the data and make decisions using inferential statistics  6. Identify possible errors  ● Designs of Statistical Studies  ○ 1. Observational Study:​ researcher or the person conducting the study doesn’t  apply treatment (just watching them)  ○ 2. Randomized Experiment:​ applies a treatment; treatment group and control  group  ● Experiments  ○ Double­Blind:​ neither the researcher or subjects know which treatments have  been assigned to what group  ○ Block Experiment:​ you “block” them into categories then divide those categories  into a control and treatment group   ● Confounders  ○ Related to both the treatment and the outcome  ● Observational Studies  ○ Cohort Studies  ■ Prospective:​ observe subjects over time; subjects are followed over time  ■ Cross Sectional:​ subjects are measured one time  ■ Retrospective:​ subjects are sampled after outcome has occurred  ○ Case­Control Study  ■ Two samples are drawn. One has the disease of interest and one does not  ■ Always retrospective  Section 1.4­ Bias in Studies  ● Sources of Bias  ○ Voluntary  ○ Self Interest  ○ Social Acceptability  ○ Leading Question  ○ Nonresponse Bias  ○ Sampling Bias 


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