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Statistical Sciences

Statistical Sciences 2037A/B

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Spring

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uwLecture 1what is statisticsdata and other shit like thatpredict analyze patterns interpreta collection of procedures for gaining and analyzing information in order to help people make decisions when faced with uncertainty pg 4Uncertainty in statsreason we get a large sample size and get a lot of different individuals is to combat uncertaintywant to understand the causation of the data what is the info a result ofthere is variability based on natural different ppl and whatnot or can be measurement error key is to look for the trends in the data stats as a problem solving tool5 steps1 problemowhat is the problem2 plan not just the numbers but how the numbers represent what youre trying to analyze or discoverowhat are you going to measure and how3 data collect it and organize itocollect and organize it4 analysis patterns and hypothesisolook for patterns forma hypothesis5 conclusionointerpretationsthis is a cycle so sometimes we reject the hypothesis and generate new ones and start the cycle againDataa collection of numbers or other pieces of information to which meaning has been attachedexample the numbers and shitis it data does it have any meaningwe dont know theres no meaning attached to themright now its just a list of colours and numbers but until you assign what they represent it has no meaningon their own these numbers are not dataseven critical componentscomponent 1whos funding it do they gain by doing the studycomponent 2how does the researcher know the participants if they know eachother then maybe the participant is inclined to open up morecomponent 3how did they select the participants did they take a fair sample of what they were trying to prove in order to get conclusive evidence or was it fucked up biased shit to try and get a certain end resultwas the study voluntary for participants or were they recruitedcomponent 4testing a basketball player on a soccer field maybe judged poorly in terms of fitness on the field but good on the courtwordingone may favour a certain question 1st question is more general while 2nd is more specific 2nd may seem more justifyable because you know where the buget is going the 1st question had a higher percentage of ppl because both are for increased government but ppl may not want the budget to be allocated to the fields that the 2nd question devotes them tored bull study did not state any questions askedcomponent 5was the study taken by phone mail internet etc all may have different outcomescomponent 6factor of intrest in redbull study was whether they mix caffeine with alcohol or notexample maybe all of the mix alcohol group are first year students who go out and do fucked up shit compared to the older more mature group who dont mix were people who dont drink considered non mixers we dont knowcomponent 7exampleif something is 16 times more likely to happen when taking a drug it may be 16 x 0000000000008 which is still very insignificant dont have dicrete numbers of participants in red bull article so you cant tell if the numbers tell the whole story Red bull studycomponent 1 was that alcohol abouse institute funded the research2study tesam which did not interact with students3random invitiation by year4 completely random questions werethis shit isnt really importatn just examples of how if we have the info we can come to a greater interpretation of what the data meansdont always believe what you readLecture 2 dumbass group shitLecture 3Dataqualitative also known as categorical dataheight is a quantiitative measurehair colour is qualitative measurethe difference in qualitativa and quantitative is the values collected 7you can summarize qualitative data numerically but the distinction is what you are measuring numerical vs non numericalcounted and measureddiscrete you can easily countcontinuous there is an infinite number of possible values example heightyour height can range anywhere from 0cm to200cm but any individual can be within that spectrum with infinite possibilities example 1652433274cm number can continue forevercontinous means with whatever range of numbers exists there are inifinate possible numberslevels of Measurementqualitative data is data that can be obesrved but not measured numericallyie hair colour is qualitativecant measure hair colour numerically but you can obeserve how many people have a hair colourquantitative data is data that can be measured numerically ie age iq height weighthow variables are categorizedcan the values be ranked logically can you rank the data on how meaningful the data is example hair colourone colour is not more meaningful than the other But you could rank them from lightest to darkest logicallyif no you have nominal datanominal datadata that isnt ranked in a meaningful way no way to rank hair colourare the meaningful differences betweeen ranks example disagree vs agree you could say strongly disagree vs somewhat disagree you can argue strongly disagree is more meaningful than somewhat disagree and is ranked higherordinalno differences between meaningful ranks strong disagree is no more meaningful than somewhat disagreenominal oand ordinal are more specific ways of claddifying qualitative datais there a meaningful zero valuezero has to be the absence of what youre measuring 0cm means no height 0degrees celcius is the freezing point of water and 100 is the boiling point in this case 0 doesnt mean the absense of heat and is not a meaningful zero0 kelvin is the is 100 degrees twice as hot as 50 degrees nocant be expressed as a ratiowhen dealing with values of data that dont have a meaningful zero and cant be expressed as a ratio it is called intervalwhen dealing with values of data that does have a meaningful zero and can be expressed as a ratio it is called a ratio105 is 2 10 is twice as tall as 5 which means it can be expressed as a ratio while tempurature does not follow thisratio and interval green are both quantitative measurementsexamples gendernominal neither gender is betterjob statusordinal employed vs unemployed or you could view it as lower level vs managment so you can rank them and they become ordinal

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