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The Mathematics Vision Project Scott Hendrickson, Joleigh Honey, Barbara Kuehl, Travis Lemon, Janet Sutorius © 2016 Mathematics Vision Project Original work © 2013 in partnership with the Utah State Of f ice of Education This work is licensed under the Creative Commons Attribution CC BY 4.0 MODULE 9 Modeling Data SECONDARY MATH ONE An Integrated Approach

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Page 1: M1 Mod9 SE 7 2016nopage · 9.8 Rocking the Residuals – A Develop Understanding Task Use residual plots to analyze the strength of a linear model for data (S.ID.6) READY, SET, GO

The Mathematics Vision Project Scott Hendrickson, Joleigh Honey, Barbara Kuehl, Travis Lemon, Janet Sutorius

© 2016 Mathematics Vision Project Original work © 2013 in partnership with the Utah State Off ice of Education

This work is licensed under the Creative Commons Attribution CC BY 4.0

MODULE 9

Modeling Data

SECONDARY

MATH ONE

An Integrated Approach

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SECONDARY MATH 1 // MODULE 9

MODELING DATA

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

MODULE 9 - TABLE OF CONTENTS

MODELING DATA

9.1 Texting by the Numbers – A Solidify Understanding Task

Use context to describe data distribution and compare statistical representations (S.ID.1, S.ID.3)

READY, SET, GO Homework: Modeling Data 9.1

9.2 Data Distribution – A Solidify/Practice Understanding Task

Describe data distributions and compare two or more data sets (S.ID.1, S.ID.3)

READY, SET, GO Homework: Modeling Data 9.2

9.3 After School Activity – A Solidify Understanding Task

Interpret two way frequency tables (S.ID.5)

READY, SET, GO Homework: Modeling Data 9.3

9.4 Relative Frequency – A Solidify/Practice Understanding Task

Use context to interpret and write conditional statements using relative frequency tables (S.ID.5)

READY, SET, GO Homework: Modeling Data 9.4

9.5 Connect the Dots – A Develop Understanding Task

Develop an understanding of the value of the correlation co-efficient (S.ID.8)

READY, SET, GO Homework: Modeling Data 9.5

9.6 Making More $ – A Solidify Understanding Task

Estimate correlation and lines of best fit. Compare to the calculated results of linear regressions and the

correlation co-efficient (S.ID.7, S.ID.8)

READY, SET, GO Homework: Modeling Data 9.6

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SECONDARY MATH 1 // MODULE 9

MODELING DATA

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

9.7 Getting Schooled – A Solidify Understanding Task

Use linear models of data and interpret the slope and intercept of regression lines with various units

(S.ID.6, S.ID.7, S.ID.8)

READY, SET, GO Homework: Modeling Data 9.7

9.8 Rocking the Residuals – A Develop Understanding Task

Use residual plots to analyze the strength of a linear model for data (S.ID.6)

READY, SET, GO Homework: Modeling Data 9.8

9.9 Lies and Statistics – A Practice Understanding Task

Use definitions and examples to explain understanding of correlation coefficients, residuals, and linear

regressions (S.ID.6, S.ID.7, S.ID.8)

READY, SET, GO Homework: Modeling Data 9.9

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SECONDARY MATH I // MODULE 9

MODELING DATA – 9.1

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

9.1 Texting by the Numbers A Solidify Understanding Task

Technologychangesquicklyandyethasalargeimpactonour

lives.Recently,Rachelwasbusychattingwithherfriendsviatext

messagewhenhermomwastryingtoalsohaveaconversationwithher.Afterward,theyhada

discussionaboutwhatisanappropriatenumberoftextstosendeachday.Sincetheycouldnot

agree,theydecidedtocollectdataonthenumberoftextspeoplesendonanygivenday.Theyeach

asked24oftheirfriendsthefollowingquestion:“WhatistheaveragenumberoftextsyouSEND

eachday?”Thedataandhistogramrepresentingall48responses:

{0,2,3,3,5,5,5,5,5,5.5,6,6,6,10,12,13,15,15,16,20,25,35,36,70,80,85,110,130,137,138,

138,140,142,143,145,150,150,150,150,150,150,150,155,162,164,165,175,275}

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SECONDARY MATH I // MODULE 9

MODELING DATA – 9.1

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

PartI:

1. Whatinformationcanyouconcludebasedonthehistogramabove?

2. Representthesamedatabycreatingaboxplotabovethehistogram.

3. Whatstorydoestheboxplottell?Describetheprosandconsofeachrepresentation(histogramandboxplot).Inotherwords,whatinformationdoeseachrepresentationhighlight?Whatinformationdoeseachrepresentationhideorobscure?

PartII:Priortotalkingaboutthedatawithhermom,Rachelhadcreatedaboxplotusingherown

datashecollectedanditlookedquitedifferentthanwhentheycombinedtheirdata.

Averagenumberoftextssenteachday

4. DescribethedataRachelcollectedfromherfriends.Whatdoesthisinformationtellyou?

5. Comparethetwoboxplots(Rachel’sdatavsalldata).

6. Rachelwantstocontinuesendinghernormalnumberoftexts(averageof100perday)andhermomwouldlikehertodecreasethisbyhalf.Presentanargumentforeachside,usingmathematicstojustifyeachperson’srequest.

2

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.1

Mathematics Vision Project

Licensed under the Creative Commons Attribution CC BY 4.0

mathematicsvisionproject.org

9.1

READY Topic:MeasuresofcentraltendencySam’stestscoresforthetermwere60,89,83,99,95,and60.

1.SupposethatSam’steacherdecidedtobasethetermgradeonthemean.

a.WhatgradewouldSamreceive?

b.Doyouthinkthisisafairgrade?Explainyourreasoning.

2.SupposethatSam’steacherdecidedtobasethetermgradeonhismedianscore.

a.WhatgradewouldSamreceive?

b.Doyouthinkthisisafairgrade?Explainyourreasoning.

3.SupposethatSam’steacherdecidedtobasethetermgradeonthemodescore.

a.WhatgradewouldSamreceive?

b.Doyouthinkthisisafairgrade?Explainyourreasoning.

4.Aiden’stestscoresforthesametermwere30,70,90,90,91,and99.WhichmeasureofcentraltendencywouldAidenwanthisteachertobasehisgradeon?Justifyyourthinking.

5.Mostteachersbasegradesonthemean.Doyouthinkthisisafairwaytoassigngrades?Whyorwhynot?

READY, SET, GO! Name PeriodDate

3

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.1

Mathematics Vision Project

Licensed under the Creative Commons Attribution CC BY 4.0

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9.1

SET Topic:Examiningdatadistributionsinabox-and-whiskerplot.

6.Makeabox-and-whiskerplotforthefollowingtestscores.

60,64,68,68,72,76,76,80,80,80,84,84,84,84,88,88,88,92,92,96,96,96,96,96,96,96,100,100

7a.Howmuchofthedataisrepresentedbythebox?

b.Howmuchisrepresentedbyeachwhisker?

8.Whatdoesthegraphtellyouaboutstudentsuccessonthetest?

GO Topic:Creatinghistograms.

UsethedatafromtheSETsectiontoanswerthefollowingquestions.

9.Makeafrequencytablewithintervals.Useanintervalof5.10.Makeahistogramofthedatausingyourintervalsof5.

11.Whatinformationishighlightedinthehistogram?12.Whatinformationishighlightedinthebox-and-whiskerplot?

50 55 60 65 70 75 80 85 90 95 100

4

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SECONDARY MATH I // MODULE 3 MODELING DATA—9.2

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

9.2 Data Distribution A Practice Understanding Task Alotofinformationcanbeobtainedfromlookingatdataplotsandtheirdistributions.Itisimportantwhendescribingdatathatweusecontexttocommunicatetheshape,center,andspread.Shapeandspread:

• Modes:uniform(evenlyspread-noobviousmode),unimodal(onemainpeak),bimodal(twomainpeaks),ormultimodal(multiplelocationswherethedataisrelativelyhigherthanothers).

• Skeweddistribution:whenmostdataistoonesideleavingtheotherwitha‘tail’.Dataisskewedtosideoftail.(iftailisonleftsideofdata,thenitisskewedleft).

• Normaldistributionandstandarddeviation:curveisunimodalandsymmetric.Datathathasanormaldistributioncanalsodescribethedatabyhowfaritisfromthemeanusingstandarddeviation.

• Outliers:valuesthatstandawayfromthebodyofthedistribution.Forabox-and-whiskeroutliersdeterminediftheyaremorethan1.5timestheinterquartilerange(lengthofbox)beyondquartiles1and3.Alsoconsideredanoutlinerifdataismorethantwostandarddeviationsfromthecenterofanormaldistribution.

• Variability:valuesthatareclosetogetherhavelowvariability;valuesthatarespreadaparthavehighvariability.

Center:• Analyzethedataandseeifonevaluecanbeusedtodescribethedataset.Normal

distributionsmakethiseasy.Ifnotanormaldistribution,determineifthereisa‘center’valuethatbestdescribesthedata.Bimodalormultimodaldatamaynothaveacenterthatwouldprovideusefuldata.

Therearerepresentationsoftestscoresfromsixdifferentclassesfoundbelow,foreach:

1. Describethedatadistribution.2. ComparedatadistributionsbetweenAndersonandWilliams.3. ComparedatadistributionsbetweenWilliamsandLemon.4. ComparedatadistributionsbetweenCroftandHurlea.5. ComparedatadistributionsbetweenJones,Spencer,andAnderson.6. ComparedatadistributionsbetweenSpencerandtheotherhistograms.7. Whichdistributionsaremostsimilar?Different?Explainyouranswer.

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SECONDARY MATH I // MODULE 3 MODELING DATA—9.2

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

DatasetI:Williams’sclass DatasetII:Lemon’sclass

DatasetIII:Croft’sClass DatasetIV:Anderson’sClass

DatasetV:Hurlea’sclass DatasetVI:Jones’class

6

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SECONDARY MATH I // MODULE 3 MODELING DATA—9.2

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

DatasetVII:Spencer’sclass

DatasetVIII:OverallAchievementTestScores

7

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SECONDARY MATH I // MODULE 9

MODELING DATA – 9.2

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Licensed under the Creative Commons Attribution CC BY 4.0

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9.2

READY Topic:Drawingconclusionsfromdata.Inproblems1–4youaretoselectthebestanswerbasedonthegivendata.Belowyourchosenanswerisaconfidencescale.Circlethestatementthatbestdescribesyourconfidenceinthecorrectnessoftheansweryouchose.Thegoalistogainawarenessofhowitseemseasiertodrawconclusionsinsomecasesthaninothers. 1.Data:1,2,4,8,16,32, Thenextnumberinthelistwillbe:________

a.largerthan32 b.positive c.exactly64 d.lessthan32

IamcertainIamcorrect. Iamalittleunsure. IhadnoideasoIguessed.

Whataboutthedatamadeyoufeelthewayyoudidabouttheansweryoumarked?

2.Data:47,-13,-8,9,-23,14, Thenextnumberinthelistwillbe:________

a.positive b.negative c.lessthan100 d.lessthan-100

IamcertainIamcorrect. Iamalittleunsure. IhadnoideasoIguessed.

Whataboutthedatamadeyoufeelthewayyoudidabouttheansweryoumarked?

3.Data:-10,¾,38,-10,½,-81,-10,¼,93,-10, Thenextnumberinthelistwillbe:______

a.morethan93 b.negative c.afraction d.awholenumber

IamcertainIamcorrect. Iamalittleunsure. IhadnoideasoIguessed.

4.Data:50,-43,36,-29,22,-15 Thenextnumberinthelistwillbe:______

a.odd b.lessthan9 c.two-digits d.greaterthan-15

IamcertainIamcorrect. Iamalittleunsure. IhadnoideasoIguessed.

Whataboutthedatamadeyoufeelthewayyoudidabouttheansweryoumarked?

READY, SET, GO! Name PeriodDate

8

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SECONDARY MATH I // MODULE 9

MODELING DATA – 9.2

Mathematics Vision Project

Licensed under the Creative Commons Attribution CC BY 4.0

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9.2

SET Topic:Creatinghistograms.

Mr.Austingaveaten-pointquiztohis9thgrademathclasses.Atotalof50studentstookthequiz.Mr.Austinscoredthequizzesandlistedthescoresalphabeticallyasfollows.

1stPeriodMath 2ndPeriodMath 3rdPeriodMath

6,4,5,7,5,

9,5,4,6,6,

8,5,7,5,8,

1,8,7,10,9

4,5,8,6,8,

9,5,8,5,1,

5,5,7,5,7

9,8,10,5,9,

7,8,9,8,5,

8,10,8,8,5

5.UseALLofthequizdatatomakeafrequencytablewithintervals.Useanintervalof2.

Score Frequency

0-1

2–3

4–5

6–7

8–9

10-11

6.Useyourfrequencytabletomakeahistogramforthedata

7.Describethedatadistributionofthehistogramyoucreated.Includewordssuchas:mode,skewed,outlier,normal,symmetric,center,andspread,iftheyapply.(Hint:Don’tforgetstandarddeviation.)

9

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SECONDARY MATH I // MODULE 9

MODELING DATA – 9.2

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9.2

8.Createagraphofyourchoice(histogram,boxplot,dotplot)for1stand3rdperiod.

9.Whichclassperformedbetter? Justifyyouranswerbycomparingtheshape,center,andspreadofthetwoclasses.(Hint:Don’tforgetstandarddeviation.)

GO

Topic:Figuringpercentages

10.Whatpercentof97is11? 11.Whatpercentof88is132?

12.Whatpercentof84is9? 13.Whatpercentof88.6is70?

14.Whatis270%of60? 15.Whatis84%of25?

18

16

14

12

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8

6

4

2

1 2 3 4 5 6 7 8 9 10

18

16

14

12

10

8

6

4

2

1 2 3 4 5 6 7 8 9 10

10

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SECONDARY MATH I // MODULE 9 MODELING DATA—9.3

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

9.3 After School Activity A Develop Understanding Task PartI

Rashidisinchargeofdeterminingtheupcomingafterschoolactivity.Todeterminethetypeof

activity,Rashidaskedseveralstudentswhethertheyprefertohaveadanceorplayagameof

soccer.AsRashidcollectedpreferences,heorganizedthedatainthefollowingtwo-wayfrequency

table:

Girls Boys Total

Soccer 14 40 54

Dance 46 6 52

Total 60 46 106

Rashidisfeelingunsureoftheactivityheshouldchoosebasedonthedatahehascollectedandis

askingforhelp.Tobetterunderstandhowthedataisdisplayed,itisusefultoknowthattheouter

numbers,locatedinthemarginsofthetable,representthetotalfrequencyforeachroworcolumn

ofcorrespondingvaluesandarecalledmarginalfrequencies.Valuesthatarepartofthe‘inner’bodyofthetablearecreatedbytheintersectionofinformationfromthecolumnandtherowandthey

arecalledthejointfrequencies.

1. Usingthedatainthetable,constructaviableargumentandexplaintoRashidwhichafter

schooleventheshouldchoose.

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SECONDARY MATH I // MODULE 9 MODELING DATA—9.3

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

PartII:Twowayfrequencytablesallowustoorganizecategoricaldatainordertodraw

conclusions.Foreachsetofdatabelow,createafrequencytable.Wheneachfrequencytableis

complete,writethreesentencesaboutobservationsofthedata,includinganytrendsor

associationsinthedata.

2. Dataset:Thereare45totalstudentswholiketoreadbooks.Ofthosestudents,12ofthem

likenon-fictionandtherestlikefiction.Fourgirlslikenon-fiction.Twentyboyslikefiction.

Fiction Nonfiction Total

Boys

Girls

Total

Observation1:

Observation2:

Observation3:

3. Dataset:35seventhgradersand41eighthgraderscompletedasurveyabouttheamountoftimetheyspendonhomeworkeachnight.50studentssaidtheyspentmorethananhour.

12eighthgraderssaidtheyspendlessthananhoureachnight.

Total

Morethanonehour

Lessthanonehour

Total

Observation1:

Observation2:

Observation3:

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SECONDARY MATH I // MODULE 9

MODELING DATA – 9.3

Mathematics Vision Project

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9.3

READY

Topic:Interpretingdatafromascatterplot

1.Thescatterplotcomparesshoesizeandheightinadultmales.Basedonthegraph,doyouthinkthereisarelationshipbetweenaman’sshoesizeandhisheight?

Explainyouranswer.

2.Thescatterplotcomparesleft-handednesstobirthweight.Basedonthegraph,doyouthinkbeingleft-handedisrelatedtoaperson’sbirthweight?

Explainyouranswer.

READY, SET, GO! Name PeriodDate

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SECONDARY MATH I // MODULE 9

MODELING DATA – 9.3

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9.3

SET Topic:Two-wayfrequencytablesHereisthedatafromMr.Austin’s10-pointquiz.Studentsneededtoscore6orbettertopassthequiz.

1stPeriodMath 2ndPeriodMath 3rdPeriodMath

6,4,3,7,5,

9,5,4,6,6,

8,5,7,3,6,

2,8,7,10,9

3,3,8,6,6,

9,5,8,5,3,

5,5,7,5,7

9,8,10,5,9,

7,8,9,8,3,

8,10,8,7,5

3.Makeatwo-wayfrequencytableshowinghowmanystudentspassedthequizandhow

manystudentsfailedthequizineachclass. 1stperiod 2ndperiod 3rdperiod TotalPassed Failed Total

Useacoloredpenciltolightlyshadethecellscontainingthejointfrequencynumbersinthetable.Theun-shadednumbersarethemarginalfrequencies.(Usethesetermstoanswerthefollowingquestions.)4.IfMr.Austinwantedtoseehowmanystudentsinall3classescombinedpassedthequiz,

wherewouldhelook?

5.IfMr.Austinwantedtowritearatioofthenumberofpassingstudentscomparedtothenumberoffailingstudentsforeachclass,wherewouldhefindthenumbershewouldneedtodothis?

6.Makeatwo-wayfrequencytablethatgivestherelativefrequenciesofthequizscoresforeachclass.

1stPeriod 2ndPeriod 3rdPeriod Total

Passed

Failed

Total

14

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SECONDARY MATH I // MODULE 9

MODELING DATA – 9.3

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9.3

GO Topic:Organizingdata.7.Sophiesurveyedallofthe6thgradestudentsatReaganElementarySchooltofindoutwhichTVNetworkwastheirfavorite.Shethoughtthatitwouldbeimportanttoknowwhethertherespondentwasaboyoragirlsosherecordedherinformationthefollowingway.AnimalPlanet CartoonNetwork Disney Nickelodeon

GGBBBBBGBBBGBBBGGBBBBBBBB

BBBBBBBBBGGGBBBGBGBGGGBGG

GGGGGGBBBBBBGBGBGGBBBGGBGGGGGBBBGGGGGB

BBBBGGGGGGGGGGGGGGGBBGGGGBGGGGGGGGGBBBBBGGGGGGGG

Sophieplannedtouseherdatatoanswerthefollowingquestions:

I.Aretheremoregirlsorboysinthe6thgrade? II.Whichnetworkwastheboys’favorite? III.Wasthereanetworkthatwasfavoredbymorethan50%ofonegender?Butwhenshelookedatherchart,sherealizedthatthedatawasn’ttellingherwhatshewantedtoknow.Herteachersuggestedthatherdatawouldbeeasiertoanalyzeifshecouldorganizeitintoatwo-wayfrequencychart.HelpSophieoutbyputtingthefrequenciesintothecorrectcells.

FavoriteTVNetworks Girls Boys Totals

AnimalPlanet

CartoonNetwork

Disney

Nickelodeon

Totals

NowthatSophiehasherdataorganized,usethetwo-wayfrequencycharttoanswerher3questions.

a.Aretheremoregirlsorboysinthe6thgrade?

b.Whichnetworkwastheboys’favorite?

c.Wasthereanetworkthatwasfavoredbymorethan50%ofonegender?

15

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SECONDARY MATH I // MODULE 9 MODELING DATA—9.4

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9.4 Relative Frequency A Solidify/Practice Understanding Task Rachelisthinkingaboutthedatasheandhermomcollectedfortheaveragenumberoftextsapersonsendseachdayandstartedthinkingthatperhapsatwo-waytableofthedatatheycollectedwouldhelpconvincehermomthatshedoesnotsendanexcessiveamountoftextsforateenager.Thetableseparateseachdatapointbyage(teenagerandadult)andbytheaveragenumberoftextssent(morethan100perdayorlessthan100perday).

1. Writetwoobservationstatementsofthistwowaytable.

Tofurtherprovideevidence,Racheldecidedtodosomeresearch.Shefoundthatonly43%ofpeoplewithphonessendover100textsperday.Shewasdisappointedthatthedatadidnotsupporthercaseandconfusedbecauseitdidnotseemtomatchwhatshefoundinhersurvey.

2. Whatquestionsdothesestatisticsraiseforyou?WhatdatashouldRachellookfortosupporthercase?

Afterlookingmorecloselyatthedata,Rachelfoundotherpercentageswithinthesamedatathatseemedmoreaccuratewiththedatashecollectedfromherteenagefriends.

Averageismorethan100textssentperday

Averageislessthan100textssentperday

Total

Teenager 20 4 24

Adult 2 22 24

Total 22 26 48

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SECONDARY MATH I // MODULE 9 MODELING DATA—9.4

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3. HowmightRachelusethedatainthetwowaytabletofindpercentagesthatwouldbeusefulforhercase?

PartII:OnceRachelrealizedtherearealotofwaystolookatasetofdatainatwowaytable,shewasmotivatedtolearnaboutrelativefrequencytablesandconditionalfrequencies.Whenthedataiswrittenasapercent,thisiscalledarelativefrequencytable.Inthissituation,the‘inner’valuesrepresentapercentandarecalledconditionalfrequencies.Theconditionalvaluesinarelativefrequencytablecanbecalculatedaspercentagesofoneofthefollowing:

• thewholetable(relativefrequencyoftable)• therows(relativefrequencyofrows)• thecolumns(relativefrequencyofcolumn)

SinceRachelwantstoemphasizethataperson’sagemakesadifferenceinthenumberoftextssent,thefirstthingshedecidedtodoisfocusontheROWofvaluessoshecouldwriteconditionalstatementsaboutthenumberoftextsapersonislikelytosendbasedontheirage.Thisiscalledarelativefrequencyofrowtable.

4. Fillinthepercentageofteenagersforeachoftheconditionalfrequenciesinthehighlightedrowbelow:

SincethePERCENTAGEScreatedfocusonROWvalues,allconditionalobservationsarespecifictotheinformationintherow.Completethefollowingsentencefortherelativefrequencyofrow:

5. Ofallteenagersinthesurvey,_______%averagemorethan100textsperday.

6. Writeanotherstatementbasedontherelativefrequencyofrow:

Average is more than 100 texts sent

per day

Average is less than 100 texts sent per day

Total

Teenager % of

teenagers

20

__ %

4

__%

24

100%

% of Adults

2 8%

22 92%

24 100%

% of People

22 46%

26 54%

48 100%

Row

17

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SECONDARY MATH I // MODULE 9 MODELING DATA—9.4

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

Belowistherelativefrequencyofcolumnusingthesamedata.Thistime,allofthepercentagesarecalculatedusingthedatainthecolumn.

7. Writetwoconditionalstatementsusingtherelativefrequencyofcolumn.

Thisdatarepresentstherelativefrequencyofwholetable:

8. Createtwoconditionaldistributionstatementsfortherelativefrequencyofwholetable.

9. Whatinformationishighlightedwhendataisinterpretedfromrelativefrequencytables?

Averageismorethan100textssentperday

Averageislessthan100textssentperday

Total

%ofTeenagers 2042%

48%

2450%

%ofAdults 24%

2246%

2450%

%ofTotal 2246%

2654%

48100%

Average is more than 100 texts sent per day

Average is less than 100 texts sent per day

Total

Teenagers 20 91%

4 15%

24 50%

Adults 2 9%

22 85%

24 50%

Total 22 100%

26 100%

48 100%

18

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.4

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9.4

READY

Topic:WritingexplicitfunctionrulesforlinearrelationshipsWritetheexplicitlinearfunctionforthegiveninformationbelow.

1.(3,7)(5,13)

2.Mikeearns$11.50anhour

3.(-5,-2)(1,10)

4.(-2,12)(6,8)

5.

6.

READY, SET, GO! Name PeriodDate

19

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.4

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9.4

SET Topic:RelativeFrequencytables

Foreachtwo-waytablebelow,createtheindicatedrelativefrequencytableandalsoprovidetwoobservationswithregardtothedata.7.Thistablerepresentssurveyresultsfromasampleofstudentsregardingmodeoftransportationtoandfromschool.

Createtherelativefrequencyofcolumntable.Thenprovidetwoobservationstatements.8.Thetwo-waytablecontainssurveydataregardingfamilysizeandpetownership.

Createtherelativefrequencyofrowtable.Thenprovidetwoobservationstatements.

Walk Bike CarPool Bus Total

Boys 37 47 27 122 233

Girls 38 22 53 79 192

Total 75 69 80 201 425

Walk Bike CarPool Bus Total

Boys

Girls

Total 100% 100% 100% 100% 100%

NoPets OwnonePetMorethanonepet

Total

Familiesof4orless 35 52 85 172Familiesof5ormore 15 18 10 43

Total 50 70 95 215

NoPets OwnonePetMorethanonepet

Total

Familiesof4orless 100%Familiesof5ormore 100%Total 100%

20

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.4

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9.4

9.Thetwo-waytablebelowcontainssurveydataaboutboysandgirlsshoes.

Createtherelativefrequencyofwholetable.Thenprovidetwoobservationstatements.

GO Topic:Onevariablestatisticalmeasuresandcomparisons

Foreachsetofdatadeterminethemean,median,mode,range,andstandarddeviation.Thencreateeitherabox-and-whiskerplotorahistogram.

10.23,24,25,20,25,29,24,25,30 11.20,24,10,35,25,29,24,25,33

12.Howdothedatasetsinproblems10and11comparetooneanother?

13.2,3,4,5,3,4,7,4,4 14.1,1,3,5,5,10,5,1,14

15.Howdothedatasetsinproblems13and14comparetooneanother?

Athleticshoes Boots DressShoe Total

Girls 21 35 60 116

Boys 50 16 10 76

Total 71 51 70 192

Athleticshoes Boots DressShoe Total

Girls

Boys

Total 100%

21

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SECONDARY MATH I // MODULE 9

MODELING DATA – 9.5

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9.5 Connect the Dots

A Develop Understanding Task

Foreachsetofdata:• Graphonascatterplot.• Usetechnology(graphingcalculatororcomputer)tocalculatethecorrelationcoefficient.

SetA2 2.3 3.3 3.7 4.2 4.6 4.5 5 5.5 5.7 6.1 6.41 1.5 2.5 1.9 2.8 3.2 4.5 3.7 1.7 4.8 2.7 2.3SetB2 2.3 3.3 3.7 4.2 4.6 4.5 5 5.5 5.7 6.1 6.41 1.5 2.5 1.9 2.8 3.2 4.5 3.7 4 4.8 5 4.6SetC2 2.3 3.3 3.7 4.2 4.6 4.5 5 5.5 5.7 6.1 6.44.7 4.9 4.2 3.9 3.5 3.2 3.1 2.6 3.2 2.1 1.3 0.8SetD2 2.3 3.3 3.7 4.2 4.6 4.5 5 5.5 5.7 6.1 6.44.7 4.9 3.6 3.9 2.1 4.5 3.1 1.7 3.7 2.1 1.3 1.8SetE2 2.3 3.3 3.7 4.2 4.6 4.5 5 5.5 5.7 6.1 6.44.7 4 4.2 3.9 2.8 3.2 4.5 3.7 3.2 4.8 5 4.4SetF2 2.3 3.3 3.7 4.2 4.6 4.5 51.8 2.22 3.62 4.18 4.88 5.44 5.3 6SetG2 2.3 3.3 3.7 4.2 4.6 4.5 54.4 4.01 2.71 2.19 1.54 1.02 1.15 0.5

1. Putthescatterplotsinorderbaseduponthecorrelationcoefficients.

2. Compareeachscatterplotwithitscorrelationcoefficient.Whatpatternsdoyousee?

CCBY

https://flic.kr/p/jAZ

BNr

22

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SECONDARY MATH I // MODULE 9

MODELING DATA – 9.5

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3. UsethedatainSetAasastartingpoint.Keepingthesamex-values,modifythey-valuestoobtainacorrelationcoefficientascloseto0.75asyoucan.Recordyourdatahere:

2 2.3 3.3 3.7 4.2 4.6 4.5 5 5.5 5.7 6.1 6.4

Whatdidyouhavetodowiththedatatogetagreatercorrelationcoefficient?

4. Thistime,againstartwiththedatainSetA.Keepthesamex-values,butthistime,modifytheyvaluestoobtainacorrelationcoefficientascloseto0.25asyoucan.Recordyourdatahere:

2 2.3 3.3 3.7 4.2 4.6 4.5 5 5.5 5.7 6.1 6.4

Whatdidyouhavetodowiththedatatogetacorrelationcoefficientthatiscloserto0?

5. Onemoretime:startwiththedatainSetA.Keepthesamex-values,modifythey-valuestoobtainacorrelationcoefficientascloseto-0.5asyoucan.Recordyourdatahere:

2 2.3 3.3 3.7 4.2 4.6 4.5 5 5.5 5.7 6.1 6.4

Whatdidyouhavetodowiththedatatogetacorrelationcoefficientthatisnegative?

6. Whataspectsofthedatadoesthecorrelationcoefficientappeartodescribe?

7. Onthenightbeforethelastmathtest,Shaniquaheldastudygroupatherhouse.Itwasa

greatnight;theyatealotofpizza,didmath,andlaughedalot.Shaniquascoredbetteron

hertestthanusualandthoughtitmightberelatedtopizza.Shecollectedthefollowingdata

fromherfriendsinthestudygroup:

23

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SECONDARY MATH I // MODULE 9

MODELING DATA – 9.5

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

Shaniqua David Susana Ruby Deion Oscar

Numberof

Pizza

Slices

Eaten

2 6 1 4 3 5

Increase

inTest

Score

5 9 4 7 6 8

Createascatterplotofthisdataandcalculatethecorrelationcoefficient.Basedonthesedata,wouldyourecommendeatingpizzaonthenightbeforeatestto

increasescores?Whyorwhynot?

8. Describeasituationwithtwovariablesthatmayhaveahighcorrelation,butnotbe

causallyrelated.

9. Whataresomereasonsthattwovariablesmaybehighlycorrelatedbutnothaveacausal

relationship?

24

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.5

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9.5

READY

Topic:EstimatingthelineofbestfitExaminethescatterplotbelow.Imaginethatyoudrewastraightlinethroughthegeneralpatternofthepoints,keepingascloseaspossibletoallpointswithasmanypointsabovethelineasbelow.

1.Predictapossibley-interceptandslopeforthelineyouimagined.

a.y-intercept:____________________b.slope:___________________________

2.Sketchthelinethatyouimaginedforquestion#1andwriteanequationforthatline.

SET Topic:Estimatingthecorrelationcoefficient Matchthefollowingscatterplotswiththecorrectcorrelationcoefficient.Possiblecorrelationcoefficients:a.0.05 b.0.97 c.-0.94 d.-0.49 e.0.68 f.-0.25

READY, SET, GO! Name PeriodDate

© 2012 http://en.wikipedia.org/wiki/File:Scatter_diagram_for_quality_characteristic_XXX.svg

25

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MODELING DATA – RSG 9.5

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9.5

3.

4.

5.

6.

7.

8.

26

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MODELING DATA – RSG 9.5

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9.5

GO Topic:Visuallycomparingslopesoflines.

Followtheprompttosketchthegraphofalineonthesamegridwiththegivencharacteristics.

8.Agreaterslope

9.Alesserslope

10.Alargery–interceptandalesserslope

11.Slopeistheoppositereciprocal.

27

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.6

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9.6 Making More $

A Solidify Understanding Task

EachyeartheU.S.CensusBureauprovidesincomestatisticsfortheUnited

States.Intheyearsfrom1990to2005,theyprovidedthedatainthe

tablesbelow.(Alldollaramountshavebeenadjustedfortherateof

inflationsothattheyarecomparablefromyear-to-year.)

1. Createascatterplotofthedataformen,setting1991asyear1.

Whatisyourestimateofthecorrelationcoefficientforthesedata?

Year

Median Income for All Women

2005 23970 2004 23989 2003 24065 2002 23710 2001 23564 2000 23551 1999 22977 1998 22403 1997 21759 1996 20957 1995 20253 1994 19158 1993 18751 1992 18725 1991 18649

Year

Median Income for All Men

2005 41196 2004 41464 2003 40987 2002 40595 2001 41280 2000 41996 1999 42580 1998 42240 1997 40406 1996 38894 1995 38607 1994 38215 1993 37712 1992 37528 1991 38145

CCBY40

1KCa

lculator.org

https://flic.kr/p/aFD

grH

28

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.6

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2. Onaseparategraph,createascatterplotofthedataforwomen,setting1991asyear1.Whatisyourestimateofthecorrelationcoefficientforthesedata?

3. Estimateanddrawlinesthatmodeleachsetofdata.

4. Describehowyouestimatedthelineformen.Ifyouchosetorunthelinedirectlythroughanyparticularpoints,describewhyyouselectedthem.

5. Describehowyouestimatedthelineforwomen.Ifyouchosetorunthelinedirectly

throughanyparticularpoints,describewhyyouselectedthem.

6. Writetheequationforeachofthetwolinesinslopeinterceptform.

a. Equationformen:

b. Equationforwomen:

7.Usetechnologytofindtheactualcorrelationcoefficientformen.

Whatdoesittellyouabouttherelationshipbetweenincomeandyearsformen?

8. Whatistheactualcorrelationcoefficientforwomen?

a. Whatdoesittellyouabouttherelationshipbetweenincomeandyearsforwomen?

b. Whatdothecorrelationcoefficientsformenandwomentellusabouthowthedatacompares?

29

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.6

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

9.Usetechnologytocalculatealinearregressionforeachsetofdata.Addtheregressionlinestoyourscatterplots.

c. Linearregressionequationformen:

d. Linearregressionequationforwomen:

10. Compareyourmodeltotheregressionlineformen.Whatdoestheslopemeanineachcase?(Includeunitsinyouranswer.)

11.Compareyourmodeltotheregressionlineforwomen.Whatdoesthey-interceptmeanineachcase?(Includeunitsinyouranswer.)

12.Comparetheregressionlinesformenandwomen.Whatdothelinestellusabouttheincomeofmenvswomenintheyearsfrom1991-2005?

13.Whatdoyouestimatewillbethemedianincomeformenandwomenin2015?

30

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.6

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14.TheCensusBureauprovidedthefollowingstatisticsfortheyearsfrom2006-2011.

Withtheadditionofthesedata,whatwouldyounowestimatethemedianincomeofmenin2015tobe?Why?

15.Howappropriateisalinearmodelformen’sandwomen’sincomefrom1991-2011?Justifyyouranswer.

Year

Median Income for All Men

2011 37653 2010 38014 2009 38588 2008 39134 2007 41033 2006 41103

Year

Median Income for All Women

2011 23395 2010 23657 2009 24284 2008 23967 2007 25005 2006 24429

31

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.6

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9.6

READY

Topic:FindingdistanceandaveragesUsethenumberlinebelowtoanswerthequestions.

1.FindthedistancebetweenpointAandeachofthepointsonthenumberline.

AF=______ AC=______ AG=______ AB=_______ AD=______ AE=______

2.WhatisthetotalofallthedistancesfrompointAthatyoufoundinexercisenumberone?3.Findtheaverageofthedistancesthatyoufoundinexercise1.4.WhichpointorpointsonthenumberlineislocatedtheaveragedistanceawayfrompointA?5.CirclethelocationorlocationsonthenumberlinethatistheaveragedistanceawayfromA.6.FindthedistancebetweenpointDandeachofthepointsonthenumberline.

DF=______ DC=______ DG=______ DA=_______ DB=______ DE=______

7.WhatisthetotalofallthedistancesfrompointDthatyoufoundinexercisenumbersix?8.Findtheaverageofthedistancesthatyoufoundinexercise6.9.IsthereapointonthenumberlinelocatedtheaveragedistanceawayfrompointD?10.LabelalocationonthenumberlinethatistheaveragedistanceawayfrompointD,labelitY.

READY, SET, GO! Name PeriodDate

32

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MODELING DATA – RSG 9.6

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9.6

SET Topic:Scatterplotsandlinesofbestfitortrendlines 11.Createascatterplotforthedatainthetable.

12.DotheEnglishandhistoryscoreshave

apositiveornegativecorrelation?13.DotheEnglishandhistoryscoreshaveastrongorweakcorrelation?14.Whichgraphbelowshowsthebestmodelforthedataandwillcreatethebestprediction?

Explainwhyyourchoiceisthebestmodelforthedata.

a.

b.

c.

English Score History Score

60 65

53 59

44 57

61 61

70 67

33

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MODELING DATA – RSG 9.6

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9.6

15.Whichgraphbelowshowsthebestmodelforthedataandwillcreatethebestprediction?Explainwhyyourchoiceisthebestmodelforthedata.

a.

b. c.

16.Whichgraphbelowshowsthebestmodelforthedataandwillcreatethebestprediction?

Explainwhyyourchoiceisthebestmodelforthedata.

a.

b. c.

GO Topic:Creatingexplicitfunctionrulesforarithmeticandgeometricsequences.Usethegiveninformationbelowtocreateanexplicitfunctionruleforeachsequence.17.! 2 = 7;commondifference=3 18.! 1 = 8;commonratio=2

19.ℎ 6 = 3;commonratio=-3 20.! 5 = −3;commondifference=7

21.! 7 = 1;commondifference=-9 22.! 1 = 5;commonratio=!!

34

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MODELING WITH DATA – 9.7

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9.7 Getting Schooled

A Solidify Understanding Task

InGettingMore$,LeoandAracelinoticeda

differenceinmen’sandwomen’ssalaries.Araceli

thoughtthatitwasunfairthatwomenwerepaidless

thanmen.Leothoughtthattheremustbesome

goodreasonforthediscrepancy,sotheydecidedtodigdeeperintotheCensusBureau’sincome

datatoseeiftheycouldunderstandmoreaboutthesedifferences.

First,theydecidedtocomparetheincomeofmenandwomenthatgraduatedfromhighschool(or

equivalent),butdidnotpursuefurtherschooling.Theycreatedthescatterplotbelow,withthex

valueofapointrepresentingtheaveragewoman’ssalaryforsomeyearandtheyvalue

representingtheaverageman’ssalaryforthesameyear.Forinstance,theyear2011isrepresented

onthegraphbythepoint(17887,30616).Youcanfindthispointonthegraphinthebottomleft

corner.

1. Baseduponthegraph,estimatethecorrelationcoefficient.

Women’sincome($)

Men’sincome($)

CCBYSteven

Isaacson

https://flic.kr/p/2M3fF

35

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MODELING WITH DATA – 9.7

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2. Estimatetheaverageincomeformeninthistimeperiod.Describehowyouusedthegraph

tofindit.

3. Whatistheaverageincomeforwomeninthistimeperiod?Describehowyouusedthe

graphtofindit.

4. LeoandAracelicalculatedthelinearregressionforthesedatatobe! = 2.189! − 6731.8.Whatdoestheslopeofthisregressionlinemeanabouttheincomeofmencomparedto

women?Usepreciseunitsandlanguage.

“Hmmmm,”saidAraceli,“It’sjustasIsuspected.Thewholesystemisunfairtowomen.”“No,wait,”

saidLeo,“Let’slookatincomesformenandwomenwithbachelor’sdegreesormore.Maybeithas

somethingtodowithlevelsofeducation.”

5. LeoandAracelistartedwiththedataformenwithbachelor’sdegreesormore.Theyfound

thecorrelationcoefficientfortheaveragesalaryvsyearfrom2000-2011wasr=-.894.

Predictwhatthegraphmightlooklikeanddrawithere.Besuretoscaleandlabeltheaxes

andput12pointsonyourgraph.

36

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.7

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

Theactualscatterplotforsalariesformenwithbachelor’sdegreesfrom2000-2011isbelow.Howdidyoudo?

6. BothLeoandAraceliweresurprisedatthisgraph.Theycalculatedtheregressionlineand

got ! = −588.46! + 69978.Whatdoesthisequationsayabouttheincomeofmenwithbachelor’sdegreesfrom2000-2011?Useboththeslopeandthey-interceptofthelineof

regressioninyouranswer.

Next,theyturnedtheirattentiontothedataforwomenwithbachelor’sdegreesormorefrom

2000-2011.Here’sthedata:

Year 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000IncomeforWomen($)

41338 42409 42746 42620 44161 44007 42690 42539 42954 42871 42992 43293

37

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.7

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

7. Analyzethedataforwomenwithbachelor’sdegreesbycreatingascatterplot,interpreting

thecorrelationcoefficientandtheregressionline.Forconsistencywiththemen’sgraphabove,use

0fortheyear2000,1fortheyear2001,etc.Drawthegraphandreporttheresultsofyouranalysis

below:

8. Nowthatyouhaveanalyzedtheresultsforwomen,comparetheresultsformenand

womenwithbachelor’sdegreesandmoreovertheperiodfrom2000-2011.

38

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.7

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

9. Leobelievesthatthedifferenceinincomebetweenmenandwomenmaybeexplainedby

differencesineducation,butAracelibelievestheremustbeotherfactorssuchasdiscrimination.

BasedonthedatainthistaskandGettingMore$,makeaconvincingcasetosupporteitherLeoor

Araceli.

10. Whatotherdatawouldbeusefulinmakingyourcase?Explainwhattolookforandwhy.

39

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MODELING DATA – RSG 9.7

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9.7

READY Topic:FindingdistancesandaveragesThegraphbelowshowsseveralpointsandtheline! = !.Usethegraphtoanswereachquestion.

1.TheverticaldistancebetweenpointNandtheline! = !onthegraphis3.Findalloftheverticaldistancesbetweenthepointsandtheline! = !.

B:

D:

E:

G:

I:

L:

N:

X:

2.Calculatethesumofallthedistancesyoufoundinexerciseone.

3.Whatistheaverageverticaldistanceofthepointsfromtheline! = !?

4.Isthelineshownonthegraphthelineofbestfit?Explainwhyorwhynot.Ifitisnotthebestline,drawonethatisbetterfittothedata.

5.Estimatethecorrelationcoefficientforthissetofdatapoints.Ifyouhaveawaytocalculateitexactly,checkyourestimate.(Youcoulduseagraphingcalculatorordatasoftware.)

READY, SET, GO! Name PeriodDate

40

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.7

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9.7

SET Topic:CreatingandanalyzingscatterplotsDeterminewhetheralinearoranexponentialmodelwouldbebestforthegivenscatterplot.Thensketchamodelonthegraphthatcouldbeusedtomakepredictions.6.

7.

8.a)Usethedatainthetablebelowtomakeascatterplot.

b)Isthecorrelationofthegraphpositiveornegative?Why?

c)Whatwouldyouestimatethecorrelationcoefficienttobe?Why?

d)Createaregressionlineandwritetheregressionequation.

e)Whatdoestheslopeoftheregressionequationmeanintermsofthevariables?

f)Mostschoolyearsare36weeks.Iftherateofspendingiskeptthesame,howmuchmoremoneyneedstobesavedduringthesummerinorderfortheretobemoneytolastall36weeks?

20

200

Money

Weeks

41

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.7

Mathematics Vision Project

Licensed under the Creative Commons Attribution CC BY 4.0

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9.7

GO Topic:Determiningwhentouseatwo-waytableandwhenuseascatterplot9.Inwhichsituationsdoesitmakethemostsensetouseatwo-waytableandlookattherelativefrequencies.

10.Inwhichsituationsdoesitmakethemostsensetouseascatterplotandalinearorexponentialmodeltoanalyzeandmakedecisionsordrawconclusions?

Labeleachrepresentationbelowasafunctionornotafunction.Ifitisafunction,labelitaslinear,exponential,orneither.Ifisdoesnotrepresentafunction,explainwhy.11.

! !0 121 122 12

3 12

4 12

12.! !

1 152 303 152 201 25

13.! !

-6 -2-5 -3-4 -4-3 -5-2 -6

14.! + 12! = 4

15.! = 3 ∙ 4 !!!

16.Theamountofmedicineinthebloodstreamofacatastimepasses.Theinitialdoseofmedicineis80mmandthemedicinebreaksdownat35%eachhour.

17.

Time 0 1 2 3 4

Moneyinbank $250 $337.50 $455.63 $615.09 $830.38

42

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.8

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

9.8 Rockin’ the Residuals

A Solidify Understanding Task

Thecorrelationcoefficientisnottheonlytoolthat

statisticiansusetoanalyzewhetherornotalineisagood

modelforthedata.Theyalsoconsidertheresiduals,

whichistolookatthedifferencebetweentheobserved

value(thedata)andthepredictedvalue(they-valueon

theregressionline).Thissoundsalittlecomplicated,but

it’snotreally.Theresidualsarejustawayofthinking

abouthowfarawaytheactualdataisfromtheregressionline.

Startwithsomedata:

x 1 2 3 4 5 6y 10 13 7 22 28 19Createascatterplotandgraphtheregressionline.In,thiscasethelineis! = 3! + 6.

CCBYJamieAdkins

https://flic.kr/p/aRzLKP

43

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.8

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

Drawalinefromeachpointtotheregressionline,likethesegmentsdrawnfromeachpointbelow.

1. Theresidualsarethelengthsofthesegments.Howcanyoucalculatethelengthofeach

segmenttogettheresiduals?

2. Generally,ifthedatapointisabovetheregressionlinetheresidualispositive,ifthedata

pointisbelowtheline,theresidualisnegative.Knowingthis,useyourplanfrom#1to

createatableofresidualvaluesusingeachdatapoint.

44

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.8

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

3. Statisticiansliketolookatgraphsoftheresidualstojudgetheirregressionlines.So,you

getyourchancetodoit.Graphtheresidualshere.

Now,thatyouhaveconstructedaresidualplot,thinkaboutwhattheresidualsmeanandanswer

thefollowingquestions.

4. Ifaresidualislargeandnegative,whatdoesitmean?

5. Whatdoesitmeanifaresidualisequalto0?

45

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.8

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

6. Ifsomeonetoldyouthattheyestimatedalineofbestfitforasetofdatapointsandallofthe

residualswerepositive,whatwouldyousay?

7. Ifthecorrelationcoefficientforadatasetisequalto1,whatwilltheresidualplotlooklike?

Statisticiansuseresidualplotstoseeiftherearepatternsinthedatathatarenotpredictedbytheir

model.Whatpatternscanyouidentifyinthefollowingresidualplotsthatmightindicatethatthe

regressionlineisnotagoodmodelforthedata?Basedontheresidualplotarethereanypoints

thatmaybeconsideredoutliers?

8.

46

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.8

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

9.

10.

11.

47

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.8

Mathematics Vision Project

Licensed under the Creative Commons Attribution CC BY 4.0

mathematicsvisionproject.org

9.8

READYTopic:DescribingspreadDescribethespreadofthedatasetshownineachboxplotshownbelow.Includethemedian,therange,andtheinterquartilerange.1.

2.

3.Iftheboxplotsaboverepresenttheresultsoftwodifferentclassesonthesameassessment,whichclassdidbetter?Justifyyouranswer.

4.ThetwoboxplotsbelowshowthelowtemperaturesfortwocitiesintheUnitedStates.CityDistheboxplotontopandCityEonthebottom.

a.Whichcitywouldbeconsideredthecoldest,CityDorCityE? Why?

b.Dothesecitieseverexperiencethesametemperature?Howdoyouknow?

c.Isthereawaytoknowtheexacttemperatureforanygivendayfromtheboxplots?

d.Whatadvantage,ifany,couldahistogramoftemperaturedatahaveoveraboxplot?

READY, SET, GO! Name PeriodDate

C

i

C

i

48

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.8

Mathematics Vision Project

Licensed under the Creative Commons Attribution CC BY 4.0

mathematicsvisionproject.org

9.8

SETTopic:Residuals,residualplotsandcorrelationcoefficientsThedatasheetsinexercise5andexercise6arescatterplotsthathavetheregressionlineandtheresidualsindicated.Foreachexercise,a)Markonthegraphwhere !,! wouldbelocated.b)Usethegivendatasheettocreatearesidualplot.c)Predictthecorrelationcoefficient.5.Datasheet1a)mark !,!

b)residualplot1

C)Correlationcoefficient?

6.Datasheet2a)mark !,!

B)residualplot2

C)Correlationcoefficient?

30

25

20

15

10

5

5 10 15 20

15

10

5

–5

–10

–15

5 10 15 200

54

52

50

48

46

44

42

40

38

36

34

32

302 4 6 8 10

12

10

8

6

4

2

–2

–4

–6

–8

–10

–12

5 10

49

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.8

Mathematics Vision Project

Licensed under the Creative Commons Attribution CC BY 4.0

mathematicsvisionproject.org

9.8

Thefollowinggraphsareresidualplots.Analyzetheresidualplotstodeterminehowwellthepredictionline(lineofbestfit)describesthedata.7.Plot1 analysis

8.Plot2 analysis

50

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.8

Mathematics Vision Project

Licensed under the Creative Commons Attribution CC BY 4.0

mathematicsvisionproject.org

9.8

GOTopic:Geometricconstructions9.Constructanisoscelestrianglewithacompassandastraightedge.10.Constructasquareusingacompassandastraightedge.11.Useacompassandastraightedgetoconstructahexagoninscribedinacircle.

51

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.9

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

9.9 Lies and Statistics

A Practice Understanding Task

Decidewhethereachstatementis:

• Sometimestrue• Alwaystrue• Nevertrue

Giveareasonforyouranswer.

1. Theslopeofthelinearregressionlinecanbecalculatedusinganytwopointsinthedata.

_________________________________________________________________________________________________________

2. Ifthecorrelationcoefficientforasetofdatais0,thenthelineofbestfitishorizontal.

_________________________________________________________________________________________________________

3. Thesumoftheresidualsforthelineofbestfitis0.

_________________________________________________________________________________________________________

4. Ifthecorrelationcoefficientisverylarge,thentheremustbeanoutlierinthedata.

_________________________________________________________________________________________________________

5. Anegativecorrelationcoefficientmeansthatthedatapointsareveryrandomanddon’treallyfitalinearmodel._________________________________________________________________________________________________________

6. Anegativeresidualmeansthattheregressionlineisveryfarfromtheactualdatapoint.

_________________________________________________________________________________________________________

7. Ifthecorrelationcoefficientispositive,thentheslopeofthelineofbestfitwillprobablybepositive._________________________________________________________________________________________________________

CCBYU.S.D

ept.ofAgriculture

https://flic.kr/p/jPo

bb4

52

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SECONDARY MATH 1 // MODULE 9

MODELING WITH DATA – 9.9

Mathematics Vision Project Licensed under the Creative Commons Attribution CC BY 4.0 mathematicsvisionproject.org

8. Ifthereisaperfectcorrelationbetweenvariablesinthedata,thenthecorrelationcoefficientis1.

_________________________________________________________________________________________________________

9. Tofindthevalueofaresidualforapoint(a,b)givenalineofbestfit,f(x):a. Find!(!)b. Find! − !(!)c. Iftheanswerispositive,thenthepointisabovetheline.d. Iftheanswerisnegative,thenthepointisbelowtheline.

_________________________________________________________________________________________________________

10. Thelargertheresidualforagivenpoint,thefurtherawaythepointisfromthelineofbestfit.

_________________________________________________________________________________________________________

11. Ifthereisaperfectcorrelationbetweentwovariablesaandb,theneitheracausedborbcauseda._________________________________________________________________________________________________________

53

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.9

Mathematics Vision Project

Licensed under the Creative Commons Attribution CC BY 4.0

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9.9

READY

Topic:IdentifyingtypesoffunctionsandwritingtheexplicitequationsForeachrepresentationofafunction,decideifthefunctionislinear,exponential,orneither.

Justifyyouranswer.

1.! ! !

1 1176492 168073 24014 3435 49

2.Thefeeforataxirideis$7forgettingintothetaxiplus$2permile.

3.

4.

6.

! ! !

1 1

4 2

9 3

16 4

25 5

7.! 1 = 7; ! ! = 5 ∙ ! ! − 1

8.

ℎ ! = 3 ! − 1 + 2

9.! ! = 3!! − ! − 3!! + 1

READY, SET, GO! Name PeriodDate

54

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SECONDARY MATH I // MODULE 9

MODELING DATA – RSG 9.9

Mathematics Vision Project

Licensed under the Creative Commons Attribution CC BY 4.0

mathematicsvisionproject.org

9.9

SET Topic:ReviewingkeytopicsinstatisticsDecidewhethereachstatementissometimestrue,alwaystrue,ornevertrue.Ifthestatementissometimestruegiveoneexampleofwhenitistrueandanexampleofwhenitisnot.10.Thelinearregressionlinepassesthroughtheaverageofthexvaluesandtheaverageofthey

values. 11.Apositivecorrelationcoefficientmeansthatthepointsinthescatterplotareveryclosetogether.12.Anegativeresidualmeansyourpredictedvalueistoolow.13.Acorrelationcoefficientcloseto1meansthatalinearmodelismostappropriateforthedata. GO Topic:SolvingliteralequationsSolveeachequationforx.

14. !" = ! 15.! + !" = ! 16.!" + !" = !

Solveeachequationfory.

17.4! + ! = 3 18.2! = 6! + 9 19.5! − 2! = 10

Solveeachequationfortheindicatedvariable.

20.! = !!!; Solve for !. 21.! = !"!! ; Solve for ℎ. 22.! = !"! !

!" ;SolveforV.

55