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    Analysis of Musical Preferences and OrchestralProgramming

    Max Candocia

    January 5, 2014

    Orchestral preferences analysis MC 1

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    Motivation

    Whenever I go to an orchestra concert, I notice a few things:1 The audience is mostly people with gray hair.2 The repetoire almost always includes Baroque, and almost never

    includes anything written after 1950.3 The musicians are very talented.

    This was especially the case after I watched a CSO performance at myschool, after which the students were promised a meet & greetwith the

    musicians. Instead, the students talked to each other in a tent withrefreshments, while the musicians talked to the donors inside theperforming arts center.

    Orchestral preferences analysis MC 2

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    Motivation (cont.)

    After this small disappointment, I began wondering about theprogramming more. Here, one of the best orchestras in the world cameto one of the best concert halls in the world and decided to play Vivaldi,

    Mozart, and Beethoven. Was this a good choice, or could they havechosen a better selection to appeal to a wider audience? My intuitiontold me that, as a premier orchestra, theyve probably done marketresearch on this before and their choice of music appeals to their donors.

    The main question of this study then, is, What makes people go to or avoid an orchestra concert?

    Orchestral preferences analysis MC 3

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    Description of Survey

    Last April, I decided to make a survey to ask questions about peoplesmusical preferences and what types of changes to programming thatwould make them more likely to go to a concert. Additionally, I addedsome marketing questions, such as how much people would pay for aticket and how they hear about concerts they go to.

    I posted the survey on Facebook and Reddit, and I also sent it in a chainemail to family and friends. I collected 1,654 responses total, which is

    enough to run tests on various hypotheses. Here is a link to the originalsurvey. The survey may still be taken, but I will not be focusing onanalyzing any new data in the near future.

    Orchestral preferences analysis MC 4

    https://docs.google.com/forms/d/1LoeOSpyZxdQqiHZuH7vqomOjW_lCJnARmjTaUlBT5H4/viewformhttps://docs.google.com/forms/d/1LoeOSpyZxdQqiHZuH7vqomOjW_lCJnARmjTaUlBT5H4/viewform
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    Sources of Bias

    Now, before I get hundreds of messages along the lines of ...but did youtake X into account?, I should admit that the data is not 100%bias-free. The main source of bias is that most of my data was collectedon Reddit, a website with young Internet-users who are not an accurate

    representation of the general population, or even their own age group.Additionally, the chain email spanned a relatively small, but olderdemographic. There were many who are members of the NorthshoreConcert Band, which is somewhat problematic for statistical purposes.My solution to this is to use where I posted the survey as a controlvariable, so that effects which are unique to specic sources are not aspronounced. Additionally, I also controlled for region and stratiedethnicity and source in the main part of the report.

    Orchestral preferences analysis MC 5

    O i D hi Q i P i C G M k i T i i l E d

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    Disclaimers

    I should note that I am in no way affilitated with any of the externallinks posted in this report, and all the video links in the composerssection are meant to give people a good representation of the variouscomposers listed.

    Additionally, the testimonials near the end do not necessarily representmy opinions. They are there to represent a pattern of complaints andexperiences I saw as well as to provide food for thought for the readers.

    Lastly, I do not intend to denigrate the CSO with the example I gaveearlier this section. It was simply the event that piqued my interest inthis topic.

    Orchestral preferences analysis MC 6

    O i D hi Q ti P i C G M k ti T ti i l E d

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    Demographics

    As you can see in Figure 1, the agedistribution is heavily peaked around20, with females being slightly olderon average. In Figure 2 and 3 on

    the next page, you can see theregions and ethnicities representedby survey. It primarily consists of people from the United States andof Caucasian descent. Thepercentages of males and femalesoverall are 76.7% and 23.3%,respectively.

    0.00

    0.02

    0.04

    0.06

    0.08

    20 40 60 80Age

    d e n s

    i t y Sex

    FemaleMale

    Age Distribution of Survey respondents

    Figure 1: Age density plot by gender.

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    Demographics (cont.)

    67.2%

    18.6%

    8.7% Region

    United States

    EuropeOther North America

    Australia

    Asia

    Africa

    South America

    Other

    Responses by Region

    Figure 2: Regional breakdown of survey respondents

    80.6%

    5.4 %

    5.4%5.7%

    Ethnicity

    Ca ucasia n (nonHispanic)

    Black (nonHispanic)

    Hispanic/Latino

    Native American/Alaskan

    Other/Mixed

    Asian/Pacific Islander

    Prefer not to say

    Responses by Ethnicity

    Figure 3: Ethnic breakdown of survey respondents

    Orchestral preferences analysis MC 8

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    Demographics (cont.)

    Here is the breakdown of the surveyby different sources. While /r/Musicis the largest source, it provides alarge amount of data for a broaderaudience than many of the othersources. The Generalcategoryrefers to the copy of the survey sentout via email, which I had lessoversight over.

    6.8%5.1%

    11.1%

    53.8%

    5.3%

    source

    r/SampleSize

    Facebook

    GeneralNot Categorized

    r/ClassicalMusic

    r/GameMusic

    r/Music

    r/MusicGeeks

    r/Orchestra

    r/RepublicOfMusic

    r/ShamelessPlug

    r/WeAreTheMusicMakers

    Responses by source

    Figure 4: Breakdown of survey sources

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    General Modications

    Each of these was rated on a scale from 1 to5, where 1 indicates a person would most likelyavoid a concert due to the change, and 5means they would most likely attend theconcert.

    Adding preconcert lectures to describe

    the music to be played.Changing ensemble to a wind orchestra,which is mostly brass, woodwind, andpercussion instruments.Playing popular music.Allowing food in the performance area.Allowing informal wear. Technicallythere isnt a strict rule against this inmost places, but oftentimes its expectedto dress nicely.

    Playing shorter pieces, rather thanlonger ones.Going to a concert with friends. This ismore of a decision that a person makesthan a programming change.Playing more modern classical music.Playing music with more percussion.Advertising towards a younger audience.Advertising the mood of the music to beplayed.

    Playing video game music.

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    Composer Programming

    Each person was asked how thepresence of a piece by a certaincomposer would affect their likelihoodto go to a concert. There were only 3

    options, being less likely, no change,and more likely.VivaldiMozartBeethovenGustav MahlerJohannes Brahms

    Pyotr TchaikovskyGeorge Gershwin

    Sergei RachmaninovIgor StravinskyLeonard BernsteinPhilip GlassSteve ReichJohn Williams

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    g p Q g g p g

    Other Questions

    Favorite genre of music and second most favorite genre of music.Whether or not the respondent listens to classical music.How often the respondent goes to an orchestra concert.The respondents favorite section of an orchestra.

    Whether or not the person recognizes at least 10 of the composers for whichpreferences were asked.The maximum price the respondent would pay for an orchestra ticket.How the respondent normally hears about concerts they go to.Whether or not the respondent played a musical instrument in a high schoolband/orchestra.

    Word-association questions with music the respondent listens to and what they think of classical music. I will go into more detail near the end of this report.A free response for any bad experiences the respondent has had with classical music.

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    g p g g p g

    Overview of Analysis Methods

    The absolute simplest method would be to simply analyze theaverages for each group. While straightforward, this methodsuffers when you want to take a large number of categoriesinto account, because it requires you to have a large numberof responses in each possible combination of categories.

    The next simplest approach one uses to nd relationships indata is linear regression . Essentially, you try to t a linebased on the data you have to the outcome you want topredict. For example, if I want to predict how much money aperson spends based on their income, I might t a line to agraph, as you can see in Figure 5. In this case, the realformula for spending is 2 + 0 . 8 income , which is prettyclose to the value estimated by linear regression.

    0 20 40 60 80 100

    0

    2 0

    4 0

    6 0

    8 0

    1 0 0

    Spending vs. Income

    Income in thousands of dollars

    S p e n

    d i n g

    i n t h o u s a n

    d s o

    f d o

    l l a r s

    Slope: 0.79

    Intercept: 2.49

    Figure 5: A line of best t superimposed on randomly generated data, along with the estimated slope (how steep the line is) and intercept (the value of the line whenincome is zero).

    Orchestral preferences analysis MC 13

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    Overview of Analysis Methods (2)Looking at Figure 6, it is obvious that this data is hard tointerpret. Literally, these results imply that someone at age20 would mark an average preference of 1. 462 20 0. 047 = 0 . 522 (or someone at age 0 has apreference of 1.462, but that makes no sense consideringteenagers are the youngest people under consideration), andfor each year older a person is, their predicted preference is0.047 of a category lower.

    One other option for analyzing this data is using multinomiallogistic regression 1, which measures the probability someoneis in a category given their responses, which does not takeinto account the ordered nature of the responses (e.g.,dislike < no opinion < like ). While some information is lost,it is somewhat simpler in its interpretation, as its easier tomake sense out of the results.

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    Video Game Music Response vs. Age

    Age

    R e s p o n s e ,

    j i t t e r e

    d

    Slope: 0.047

    Intercept: 1.462

    Figure 6: Attempt at linear regression of discrete data. The points are jittered so that you can see the density of points more easily, but they only take the values of -2,-1, 0, 1 and 2 on the y-axis. Note that this data is shifted down by 3 from the original values of 1, 2, 3, 4 and 5.

    1 To further improve these models, I use stepwise selection, adding and removing terms from the model, until a model with the optimal AIC (forprogramming changes) or BIC (for composer results) is found. AIC and BIC are information criteria which reward models for having better ts andpenalizes them for using more terms. AIC is less restrictive, and the exact penalty term is just 2 p , where p is the number of predictors in the model.Note that for non-numeric variables, p increases by how many different values a variable can have minus 1. e.g., I keep track of 7 ethnic categories, so

    adding ethnicity to the model would increase p by 6.

    Orchestral preferences analysis MC 14

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    Analysis Methods - Technical Details

    A few details important to the model are how I controlled for various sources of bias, such asethnicity, source, and region of respondents. For source and ethnicity, I used 500 stratiedsamples to construct the probability data. This means that in addition to the variables you seeon the following pages, I assigned a probability of 69.1%, 12.6%, 16.3%, 5%,0.9%, and 7.9%to Caucasian, Black, Hispanic, Asian/Pacic Islander, Native American, and other/mixedgroups, respectively, to control for any effects from ethnicity, and probabilities of 30%, 35%,25%, and 10% from /r/SampleSize, /r/Music, General, and Facebook, respectively. Theregion was xed to the United States for all of the probabilities. The values for the ethnicitiesare based on results from the 2010 US census . The values for the sources were chosensomewhat randomly so that the average of the sources would more closely resemble the USpopulation. This process was repeated to a total of 200 times so I could average theprobabilities you see on the following pages.

    The original models I start out with include sex, source, age group, ethnicity, orchestraconcert frequency, and interaction effects between those variables. The model is then shrunkas terms are eliminated because they do not add signicantly to the model.

    Orchestral preferences analysis MC 15

    http://www.census.gov/prod/cen2010/briefs/c2010br-02.pdfhttp://www.census.gov/prod/cen2010/briefs/c2010br-02.pdfhttp://www.census.gov/prod/cen2010/briefs/c2010br-02.pdf
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    Pre-concert lecturesPre-concert lectures arelectures usually given by

    the conductor of anorchestra before a concertbegins about the music tobe played. The graph onthe right suggests that thepre-concert lectures aremore enjoyed by men andmore enjoyed by peoplewho attend more orchestra

    concerts to begin with.They are also less popularwith the youngest agegroups, with the exceptionof those who already attendmany concerts a year.

    Male Female

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    Probabilities of effects of having a preconcert lectures by sex and orchestra concert attendance

    Figure 8: This graph shows the probability of changes to an individuals attendance of anorchestra concert due to the inclusion of pre-concert lectures.

    Orchestral preferences analysis MC 17

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    Pop orchestraSometimes orchestras playpopular music, ranging

    from soundtrack music topopular hip-hop and rocksongs. This is one of themore controversial changesto orchestral programming,as many people who go toorchestra concerts enjoythem for the type of musicthey play as well as the

    sounds of the instruments.

    To the right the graphsuggests many things.Generally, younger peoplewill have a warmerresponse to popular music,and women seem to bemore receptive to it.

    However, the 30-34 femaleage group appears to be astrong exception to thattrend, whereas the 45-59female age group seems tohave a much more positiveresponse to them thantheir male counterpartsregardless of concertattendance frequency.

    Male Female

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    Probabilities of effects of playing pop music by age group, sex, and orchestra concert attendance

    Figure 9: This graph shows the probability of changes to an individuals attendance of anorchestra concert due to popular music being played.

    Orchestral preferences analysis MC 18

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    Allowing foodAllowing food is anothercontroversial change that has

    been suggested. Whileconsumption of food duringconcerts is more common incabaret-style settings, it is oftenconsidered informal and rude toeat food during a concert.

    Looking at the graph to the right,it appears as if food being allowedhas the most positive effects onpeople who attend concerts lessfrequently. It also seems to havea negative effect on the older agegroups. This is especially so forthose ages 60+ who attendconcerts 7+ times per year.

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    Probabilities of effects of allowing food by age group and orchestra concert attendance

    Figure 10: This graph shows the probability of changes to an individuals attendance of anorchestra concert due to the allowance of food in the concert hall or other place of

    performance. The graph is faceted by how often one attends orchestra concerts.Orchestral preferences analysis MC 19

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    Suggesting informal attireWhile almost every concert halldoesnt have a dress code more strict

    than that of a family restaurant,many often feel pressure to dress upin more formal wear at concerts. Theway the original question was worded(using required instead of suggested) may skew some of theseresults.

    Looking at the graph to the right, itappears that sex and orchestraconcert attendance are the primaryexplanatory variables. Generally,males slightly prefer this and femalesare more indifferent. Those whoattend concerts more frequently aremore indifferent to this, and thereappears to be no strong presence of anegative effect anywhere.

    Male Female

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    Effect is...

    Most likely

    More likelyUnchanged

    Less likely

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    Probabilities of effects of suggesting informal attire by age group, attendance and sex

    Figure 11: This graph shows the probability of changes to an individuals attendance of an orchestra concert due to less formal wear being required/suggested. The graph is faceted by sex and how often one attends orchestra concerts. There is no change among age groups.

    Orchestral preferences analysis MC 20

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    Playing shorter piecesAnother common complaint is thatorchestra pieces are too long. In my

    experience, this is usually in regardsto a piece of music where there aremultiple movements, i.e., sections of the piece which can last anywherefrom a few minutes to over 20minutes.

    The effects of shortening piecesseems pretty straightforward tointerpret from the probabilities on theright. Most people are indifferent,although the second most commonresponse is positive for those whoattend concerts 6 times or less peryear and slightly negtive for thosewho attend concerts more often.Women are also slightly morereceptive to shorter pieces, althoughthis is a relatively small effect.

    Male Female

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    Effect is...

    Most likely

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    Probabilities of effects of playing shorter pieces by age group, sex and attendance

    Figure 12: This graph shows the probability of changes to an individuals attendance of an orchestra concert due to shorter pieces being performed. The graph is faceted by sex and how often one attends orchestra concerts, but there is no change among age groups.

    Orchestral preferences analysis MC 21

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    Attending a concert with friendsWhile not a programming change, Ithought it would be interesting to seehow bringing a friend might affectones likelihood to attend anorchestra concert.

    On average, it seems like age groupis the primary determining factor,with positive effects all around,especially for those outside the 35-59age group. Males tend to have aslightly higher overall preference, butthere are very few differencesbetween men and women.

    Male Female

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    Probabilities of effects of going to concert with friends by age group and sex

    Figure 13: This graph shows the probability of changes to an individuals attendance of a concert because that persons friend(s) will also be attending. The values vary across age group and gender.

    Orchestral preferences analysis MC 22

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    Adding modern classical music to the programMost concerts Ive been to play music writtenin the rst half of the 20th century or earlier.Many people wish that orchestras would play

    music more recently written, while otherscounter that much of that music is evenmore esoteric to the average person, andwould only make them want to attend less.

    Overall, there is a tendency for a slightpositive effect in the groups that attendconcerts less frequently and a much strongerpositive preference in those who attendconcerts more frequently. As a side note, Iam curious to know what people think of when they think of modern classical music.Ive attended such concerts where even myclassical-music-loving friends said theydnever ever go to it because the music isreally difficult to listen to since its atonal.Example: Schoenberg, 6 Little Pieces . Othertimes people think of modern pieces writtenby composers like John Adams, whose piecesare not usually as harsh on the ear as theSchoenburg piece from the example I linked

    to above. Example:Short Ride in a Fast Machine .

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    Probabilities of effects of playing modern classical music by age group and attendance

    Figure 14: This graph shows the probability of changes to an individuals attendance of a concert due to modern classical music being played.

    Orchestral preferences analysis MC 23

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    https://www.youtube.com/watch?v=YWQAw7XSkDYhttps://www.youtube.com/watch?v=YWQAw7XSkDYhttps://www.youtube.com/watch?v=YWQAw7XSkDYhttps://www.youtube.com/watch?v=Pi4A9bPDvTchttps://www.youtube.com/watch?v=Pi4A9bPDvTchttps://www.youtube.com/watch?v=YWQAw7XSkDYhttps://www.youtube.com/watch?v=Pi4A9bPDvTchttps://www.youtube.com/watch?v=YWQAw7XSkDY
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    Adding more percussion-focused music to programmingWhile this is somewhat closelyrelated to modern classical music due

    to how pieces are orchestrated withmore percussion as of late, the lackof percussion instruments in commonorchestral repetoire often leavespeople wanting.

    About 24% nd this changefavorable, whereas about 11% nd itunfavorable. While this change

    doesnt seem to have much of aneffect on most people, it couldcertainly draw in more people thanbefore, although there doesnt appearto be any strong relationshipsbetween adding percussion and sex orfrequency of concert attendance.

    0.00

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    Probabilities of effects of using more percussion on attendance of an orchestra concert

    Figure 15: This graph shows the probability of changes to an individuals attendance of a concert due to the addition of more percussion-focused music. There does not appear to be any relationship between this and sex, age group, or frequency of concert attendance.

    Orchestral preferences analysis MC 24

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    Advertising towards younger audiencesOne idea to bring in youngeraudiences is to advertise towards

    them. One aw with this question isthat advertising towards a particulardemographic isnt always explicit, andthat can have an effect on howpeople are actually affected by it.

    The probabilities to the rightsuggests that advertising (perhapsexplicit advertising, at least) towards

    a younger demographic will have anet negative effect on people. I doquestion the validity of this based onhow I asked the question, but it maybe a useful inference.

    0.00

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    Probabilities of effects of advertising towards a younger audience

    Figure 16: This graph shows the probability of changes to an individuals attendance of a concert due to the advertising of the concert targeting a younger audience.Surprisingly, the effect does not change across sex, age group, or frequency of orchestraconcert attendance.

    Orchestral preferences analysis MC 25

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    Advertising the mood of a concertAnother idea I came up with isadvertising the mood of a concert.

    Classical music contains a widevariety of music with every possiblemood you can imagine, and Itheorized that it might get peoplewho are unfamiliar with the music togo to the concert. One problem withthis is that asking people directlyabout advertising might not result intheir answer being the same asreality. Also, Im sure that the moodof the music would denitelyinuence whether or not these groupswould attend a concert, so moreresearch would have to be done inthis area to get any denitive results.

    Looking at the plot on the right, itappears as if there is an overallpositive effect, especially for femalesand especially for people who dontgo to concerts very frequently.

    Male Female

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    Probabilities of effects of advertising the mood of a concert versus sex and attendance

    Figure 17: This graph shows the probability of changes to an individuals attendance of a concert due to the mood being advertised. The graph is faceted by sex and frequency of orchestra concert attendance, but there is no change between age groups.

    Orchestral preferences analysis MC 26

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    Playing video game musicOne last non-composer change toorchestral programming I looked into

    was the playing of video game music.This is something that has beenbecoming more popular recently, andthere are many arguments for andagainst it that are similar to thearguments for and against orchestrasplaying popular music.

    Surprisingly (at least for me), there isno signicant correlation betweenhow often one attends orchestraconcerts and how much they wouldlike to see video game music played.Only sex and age group aresignicant. Males tended to have amore positive response towards videogame music, although the older agegroups have highly negativeresponses to the music.

    Male Female

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    Probabilities of effects of playing video game music at a concert versus sex and age group

    Figure 18: This graph shows the probability of changes to an individuals attendance of a concert due to video game music being played. The graph is faceted based on sex,and there is a strong negative reaction for older age groups.

    Orchestral preferences analysis MC 27

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    Composers and going to orchestra concerts

    One thing I thought would be worthwhile to look into was how people felt about composersand their willingness to go to concerts featuring the composers. I selected several differentcomposers from different eras and measured the responses towards them on a scale of 1 to 3(in retrospect, 1 to 5 would have been much better).

    One thing in particular which is difficult with measuring the meaningfulness of peoplesresponses with this model is taking into account whether or not people are familiar with thecomposers or not. In this case, it is meaningful to also look at that value, as well. While itcan be demonstrated that more regular concert attendence is correlated with the familiarity of composers, its still useful to treat it as its own group, particularly for the sake of people whomay be familiar with the music but do not go to concerts very often.

    Because of the added variable, I will change the age group divisions to allow for a moreaccurate analysis of those groups. 1 Additionally, I use a more restrictive model selection

    criteria so that more complicated models are highly unfavorable.2

    1Specically, I need to increase the size of the population of each group to allow for dividing those groups by sex, concertattendance, and composer familiarity without having too many small groups biasing results.

    2BIC, or Bayesian Information Criterion, penalizes a model based on both the sample size of the data and the number of explanatory variables in the model. Specically, the penalty term is p log(n), where n is the sample size and p is the numberof predictor variables. Note that including orchestra concert frequency in the model increases p by 5 because there are 6

    levels (possible values) for that variable.Orchestral preferences analysis MC 28

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    Sample sizes of subgroups

    Figure 19: This mosaic plot represents all of the different subgroups by four different variables. The size of each rectangle corresponds to the sample size of the subgroups. The colors, though not necessary for understanding the following models,indicate that there is at least one correlation between different groups (e.g., young people who go to concerts are less likely to be familiar with composers). Dots indicate empty groups. Generally, the results for groups that are represented by a larger sample size are more reliable.

    Orchestral preferences analysis MC 29

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    Composer - Bach & How to read the plotsFor the composer probabilities, I always tooknote of how composer familiarity (that is,whether or not an individual was familiar

    with at least 10 out of the 15 composerslisted here) affected preferences. Thesecharts are similar to the previous ones,except there are only 3 levels for the response(which was a mistake on my part), and asemi-transparent barplot indicates that theindividuals are less familiar with composers.Note that the estimates for older people whoare unfamiliar with the composers in generalare highly innaccurate (particularly for higherconcert attendance) because there were veryfew such people sampled.For music by J.S. Bach, a German Baroque 1era composer, the responses are generallypositive. Apart from older age groups, whoserepresentative sample is much smaller, thereisnt too strong of an effect.

    Example of his music:Brandenburg Concerto No. 3

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    Probabilities of effects of adding Bachto a concert program

    Figure 20: This graph shows the probability of changes to an individuals attendance of a concert due to music by J.S. Bach being programmed.

    1 The Baroque era of music took place from roughly 1600-1750. It is characterized by smaller orchestras and old instruments, such as the harpsichord.

    Orchestral preferences analysis MC 30

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    http://hhttps//www.youtube.com/watch?v=Xq2WTXtKurkhttp://hhttps//www.youtube.com/watch?v=Xq2WTXtKurk
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    Composer - VivaldiFor music by AntonioVivaldi, an ItalianBaroque-era composer, theresponses are generallypositive. There arent anystrong relationships betweensexes or age groups.

    Example of his music:Four Seasons - Spring

    0.00

    0.25

    0.50

    0.75

    1.00

    Familiar UnfamiliarComposer Familiarity

    p r o b

    a b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Vivaldito a concert program

    Figure 21: This graph shows the probability of changes to an individuals attendance of a concert due to music by Vivaldi being programmed, based on the individuals familiarity with at least 10 composers out of 15. There is no signicant difference between age groups.

    Orchestral preferences analysis MC 31

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    https://www.youtube.com/watch?v=TKthRw4KjEghttps://www.youtube.com/watch?v=TKthRw4KjEghttps://www.youtube.com/watch?v=TKthRw4KjEg
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    Composer - MozartFor music by WolfgangAmadeus Mozart, anAustrian composer of theClassical1 era, responses aregenerally very favorable,moreso than Vivaldi. Thereare no signicantcorrelations between

    responses and age group,sex, or frequency of orchestra concertattendance.

    Example of his music:The Magic Flute - Overture

    0.00

    0.25

    0.50

    0.75

    1.00

    Familiar UnfamiliarComposer Familiarity

    p r o

    b a

    b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Mozartto a concert program

    Figure 22: This graph shows the probability of changes to an individuals attendance of a concert due to music by Mozart being programmed.

    1 The Classical era roughly took place from 1750-1820. The music from this era is characterized by larger ensembles and less complex music than that of the Baroque era. Additionally,

    the harpsichord became less popular as the piano became more prominent.Orchestral preferences analysis MC 32

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    https://www.youtube.com/watch?v=h018rMnA0pMhttps://www.youtube.com/watch?v=h018rMnA0pM
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    Composer - Beethoven

    For music by Ludwig van

    Beethoven, a Germancomposer of the Classicalera, responses are generallyvery favorable, moreso thanVivaldi. There are nosignicant correlationsbetween responses and age

    group, sex, or frequency of orchestra concertattendance.

    Example of his music:Symphony No. 5 - Mvt. I

    Male Female

    0.000.250.500.75

    1.00

    0.0 00.250.500.751.00

    0.000.250.500.751.0 0

    0.000.250.500.751.00

    0.000.250.500.751.00

    0.00

    0.250.500.751.00

    N ev er

    S el d om

    On c e / y r

    2 6 / y r

    7 1 2 / y r

    1 3 + / y r

    < 1 8

    1 8

    2 0

    2 1

    2 4

    2 5

    2 9

    3 0

    4 4

    4 5 +

    < 1 8

    1 8

    2 0

    2 1

    2 4

    2 5

    2 9

    3 0

    4 4

    4 5 +

    Age Group

    p r o b a b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Beethovento a concert program

    Figure 23: This graph shows the probability of changes to an individuals attendance of a concert due to music by Beethoven being programmed.

    Orchestral preferences analysis MC 33

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    https://www.youtube.com/watch?feature=player_detailpage&v=N6K_IuBsRM4#t=20https://www.youtube.com/watch?feature=player_detailpage&v=N6K_IuBsRM4#t=20https://www.youtube.com/watch?feature=player_detailpage&v=N6K_IuBsRM4#t=20
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    Composer - MahlerFor music by GustavMahler, an Austriancomposer of the Romantic 1era, there is an overall trendthat more regularconcertgoers have astronger preference for hismusic. There is no apparent

    difference between agegroups or sexes.

    Example of his music:Finale of Symphony No. 1 , Titan

    0.000.250.500.751.00

    0.000.250.500.751.00

    0.000.250.500.751.0 0

    0.000.250.500.751.00

    0.000.250.500.751.00

    0.000.25

    0.500.751.00

    N ev er

    S el d om

    On c e / y r

    2 6 / y r

    7 1 2 / y r

    1 3 +

    / y r

    Familiar UnfamiliarComposer Familiarity

    p r o

    b

    a b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Mahlerto a concert program

    Figure 24: This graph shows the probability of changes to an individuals attendance of a concert due to music by Mahler being programmed.

    1 The Romantic era, roughly 1810-1920, was characterized by music focusing on nature, spirituality, nationalism, and a rejection of formulaic music composition.

    Orchestral preferences analysis MC 34

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    https://www.youtube.com/watch?v=IIykYnoKKt8https://www.youtube.com/watch?v=IIykYnoKKt8https://www.youtube.com/watch?v=IIykYnoKKt8https://www.youtube.com/watch?v=IIykYnoKKt8
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    Composer - Brahms

    For music by Johannes

    Brahms, a Germancomposer of the Romanticera. Overall reception of hismusic seems to improvewith frequency of orchestraconcert visits. For somereason, females in the

    Never and Seldomclasses for orchestra concertattendance seem to haveunusually high probabilitiesfor being less likely to go,provided they are familiarwith composers in general.

    Example of his music:Hungarian Dance No. 5

    Male Female

    0.000.250.500.75

    1.00

    0.0 00.250.500.751.00

    0.000.250.500.751.0 0

    0.000.250.500.751.00

    0.000.250.500.751.00

    0.00

    0.250.500.751.00

    N ev er

    S el d om

    On c e / y r

    2 6 / y r

    7 1 2 / y r

    1 3 + / y r

    < 1 8

    1 8

    2 0

    2 1

    2 4

    2 5

    2 9

    3 0

    4 4

    4 5 +

    < 1 8

    1 8

    2 0

    2 1

    2 4

    2 5

    2 9

    3 0

    4 4

    4 5 +

    Age Group

    p r o b a b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Brahmsto a concert program

    Figure 25: This graph shows the probability of changes to an individuals attendance of a concert due to music by Brahms being programmed.

    Orchestral preferences analysis MC 35

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    h k k

    https://www.youtube.com/watch?v=3X9LvC9WkkQhttps://www.youtube.com/watch?v=3X9LvC9WkkQhttps://www.youtube.com/watch?v=3X9LvC9WkkQhttps://www.youtube.com/watch?v=3X9LvC9WkkQhttps://www.youtube.com/watch?v=3X9LvC9WkkQ
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    Composer - Tchaikovsky

    For music by Pyotr

    Tchaikovsky, a Russiancomposer of the Romanticera. With those familiarwith composers in general,the overall opinion seemsvery high. Even for thosewho dont know many

    composers, there seems tobe a good opinion of hisname.

    Example of his music:Piano Concerto No. 1

    0.00

    0.25

    0.50

    0.75

    1.00

    Familiar UnfamiliarComposer Familiarity

    p r o

    b a

    b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Tchaikovskyto a concert program

    Figure 26: This graph shows the probability of changes to an individuals attendance of a concert due to music by Tchaikovsky being programmed.

    Orchestral preferences analysis MC 36

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    C S i k

    https://www.youtube.com/watch?feature=player_detailpage&v=0uoR76XEVPY#t=61https://www.youtube.com/watch?feature=player_detailpage&v=0uoR76XEVPY#t=61
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    Composer - StravinskyFor music by IgorStravinsky, a Russiancomposer of the Modern 1era. While there appears tobe a generally decentresponse to his inclusion inconcert programs, it is lowerthan that of Tchaikovsky.

    Example of his music:Firebird - Infernal Dance

    0.00

    0.25

    0.50

    0.75

    1.00

    Familiar UnfamiliarComposer Familiarity

    p r o

    b a

    b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Stravinskyto a concert program

    Figure 27: This graph shows the probability of changes to an individuals attendance of a concert due to music by Stravinsky being programmed.

    1 The Modern era of music can be roughly dened as 1890-1930. During this time drastic changes to music included deviations from common rhythms in music and atonality.

    Incidentally, Stravinsky became more of a neoclassical composer for a few decades, composing music that resembled that of the Classical era.Orchestral preferences analysis MC 37

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    C R h i

    https://www.youtube.com/watch?v=6Vj8ow8iC4shttps://www.youtube.com/watch?v=6Vj8ow8iC4s
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    Composer - Rachmnaninov

    For music by Sergei

    Rachmaninov, anotherRussian composer of theModern era. The overallresponse is somewhatneutral, although thereappears to be a noticeableincrease in his dislike in the

    younger females.

    Example of his music:Piano Concerto No. 2

    Male Female

    0.000.250.500.75

    1.00

    0.0 00.250.500.751.00

    0.000.250.500.751.0 0

    0.000.250.500.751.00

    0.000.250.500.751.00

    0.00

    0.250.500.751.00

    N ev er

    S el d om

    On c e / y r

    2 6 / y r

    7 1 2 / y r

    1 3 + / y r

    < 1 8

    1 8

    2 0

    2 1

    2 4

    2 5

    2 9

    3 0

    4 4

    4 5 +

    < 1 8

    1 8

    2 0

    2 1

    2 4

    2 5

    2 9

    3 0

    4 4

    4 5 +

    Age Group

    p r o b a b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Rachmaninovto a concert program

    Figure 28: This graph shows the probability of changes to an individuals attendance of a concert due to music by Rachmaninov being programmed.

    Orchestral preferences analysis MC 38

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    C Sh t k i h

    https://www.youtube.com/watch?v=KgPXOW5bpZkhttps://www.youtube.com/watch?v=KgPXOW5bpZkhttps://www.youtube.com/watch?v=KgPXOW5bpZk
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    Composer - Shostakovich

    For music by Dmitri

    Shostakovich, anotherRussian composer of theModern era. The overallresponse also more neutral,although the popularityseems high for females whoare regular concertgoers

    (7+ concerts per year).

    Example of his music:Symphony No. 5, Mvt. IV

    Male Female

    0.000.250.500.75

    1.00

    0.0 00.250.500.751.00

    0.000.250.500.751.0 0

    0.000.250.500.751.00

    0.000.250.500.751.00

    0.00

    0.250.500.751.00

    N ev er

    S el d om

    On c e / y r

    2 6 / y r

    7 1 2 / y r

    1 3 + / y r

    < 1 8

    1 8

    2 0

    2 1

    2 4

    2 5

    2 9

    3 0

    4 4

    4 5 +

    < 1 8

    1 8

    2 0

    2 1

    2 4

    2 5

    2 9

    3 0

    4 4

    4 5 +

    Age Group

    p r o b a b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Shostakovichto a concert program

    Figure 29: This graph shows the probability of changes to an individuals attendance of a concert due to music by Shostakovich being programmed.

    Orchestral preferences analysis MC 39

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    C G h i

    https://www.youtube.com/watch?v=YarFI7r2shYhttps://www.youtube.com/watch?v=YarFI7r2shYhttps://www.youtube.com/watch?v=YarFI7r2shY
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    Composer - Gershwin

    For music by George

    Gershwin, an Americancomposer who wrote musicwith elements of both jazzand classical music, theoverall response seems fairlypositive. However, for the30-44 male group, there

    seems to be a slightly morenegative response toGershwin.

    Example of his music:Rhapsody in Blue

    Male Female

    0.000.250.500.751.00

    0.0 00.250.500.751.00

    0.000.250.500.751.0 0

    0.000.250.500.751.00

    0.000.250.500.751.00

    0.00

    0.250.500.751.00

    N ev er

    S el d om

    On c e / y r

    2 6 / y r

    7 1 2 / y r

    1 3 + / y r

    < 1 8

    1 8

    2 0

    2 1

    2 4

    2 5

    2 9

    3 0

    4 4

    4 5 +

    < 1 8

    1 8

    2 0

    2 1

    2 4

    2 5

    2 9

    3 0

    4 4

    4 5 +

    Age Group

    p r o b

    a b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Gershwinto a concert program

    Figure 30: This graph shows the probability of changes to an individuals attendance of a concert due to music by Gershwin being programmed.

    Orchestral preferences analysis MC 40

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    Composer Bernstein

    https://www.youtube.com/watch?feature=player_detailpage&v=uj158c_4e0M#t=40https://www.youtube.com/watch?feature=player_detailpage&v=uj158c_4e0M#t=40https://www.youtube.com/watch?feature=player_detailpage&v=uj158c_4e0M#t=40
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    Composer - Bernstein

    For music by LeonardBernstein, an Americancomposer who wrote musicfor a wide variety of works,including West Side Story ,there seems to be anincreasing level of popularity with orchestral

    concert attendance. For theolder age groups in themale category (30+), thereseems to be more of anegative effect for theregular concertgoers (7+times/year).

    Example of his music:Mambo!

    Male Female

    0.000.250.500.751.00

    0.0 00.250.500.751.00

    0.000.250.500.751.0 0

    0.000.250.500.751.00

    0.000.250.500.751.00

    0.00

    0.250.500.751.00

    N ev er

    S el d om

    On c e / y r

    2 6 / y r

    7 1 2 / y r

    1 3 + / y r

    < 1 8

    1 8

    2 0

    2 1

    2 4

    2 5

    2 9

    3 0

    4 4

    4 5 +

    < 1 8

    1 8

    2 0

    2 1

    2 4

    2 5

    2 9

    3 0

    4 4

    4 5 +

    Age Group

    p r o b

    a b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Bernsteinto a concert program

    Figure 31: This graph shows the probability of changes to an individuals attendance of a concert due to music by Bernstein being programmed.

    Orchestral preferences analysis MC 41

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    Composer Reich

    https://www.youtube.com/watch?feature=player_detailpage&v=NEs8yqhavtI#t=10https://www.youtube.com/watch?feature=player_detailpage&v=NEs8yqhavtI#t=10
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    Composer - ReichFor music by Steve Reich, anAmerican composer who is well

    known for his minimalist music, theredoes not seem to be a very goodresponse to his music. While thegraph does suggest that there is aslight bias in favor of Reichs music,my experience with his music wouldlend me to believe that among thepeople who are less likely to go to aconcert with his music, there are

    quite a few people who wouldabsolutely avoid it, because the factthat he has composed pieces thathave gained notoriety due to theirhighly minimalistic nature.

    Example of a notorious piece of his:Four Organs

    Another example of his music:Vermont Counterpoint

    0.00

    0.25

    0.50

    0.75

    1.00

    Familiar UnfamiliarComposer Familiarity

    p r o b

    a b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Reichto a concert program

    Figure 32: This graph shows the probability of changes to an individuals attendance of a concert due to music by Reich being programmed.

    Orchestral preferences analysis MC 42

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    Composer Glass

    https://www.youtube.com/watch?v=TYqs3NHCrlEhttps://www.youtube.com/watch?v=2SEHaB_ZITQhttps://www.youtube.com/watch?v=2SEHaB_ZITQhttps://www.youtube.com/watch?v=TYqs3NHCrlE
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    Composer - Glass

    For music by Philip Glass,another American composerwho is well known for hisminimalist music and musicin soundtracks, responsesseem to be more positivethan Reichs, although itstill suggests that the

    minimalistic nature of hismusic is offputting tocertain audiences.

    Example of his music:Symphony No. 8, Mvt. I

    0.00

    0.25

    0.50

    0.75

    1.00

    Familiar UnfamiliarComposer Familiarity

    p r o

    b a

    b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Glassto a concert program

    Figure 33: This graph shows the probability of changes to an individuals attendance of a concert due to music by Glass being programmed.

    Orchestral preferences analysis MC 43

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    Composer Williams

    https://www.youtube.com/watch?v=HjQA6rMwDlEhttps://www.youtube.com/watch?v=HjQA6rMwDlE
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    Composer - Williams

    For music by John Williams,an American composer verywell-known for his music insoundtracks of movies suchas Harry Potter and Star Wars , responses aregenerally positive. There isa signicant probability for

    a negative response forthose who are familiar withcomposers, which is similarto the responses of Reichand Glass in that regard.

    Example of his music:Duel of the Fates 0.00

    0.25

    0.50

    0.75

    1.00

    Familiar UnfamiliarComposer Familiarity

    p r o

    b a

    b i l i t y

    Effect is...

    More likely

    No effect

    Less likely

    Fam.w/10+ composers?

    Familiar

    Unfamiliar

    Probabilities of effects of adding Williamsto a concert program

    Figure 34: This graph shows the probability of changes to an individuals attendance of a concert due to music by Williams being programmed.

    Orchestral preferences analysis MC 44

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    Genre Analysis - Overview

    https://www.youtube.com/watch?v=J1gH_cjdb60https://www.youtube.com/watch?v=J1gH_cjdb60
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    Genre Analysis - Overview

    The heavy part of the analysis is nished, and now I will presentsome more visual results. These are focused around peoples favorite(and second-most favorite) genres, as well as other statistics, includingsome word-association data that was collected.

    Note: This section is not intended to be representative of any givenpopulation other than the sample its from. Unlike the previoussections, I did not control for source, ethnicity, etc.

    Orchestral preferences analysis MC 45

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    Favorite Genres - Correlations

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    Favorite Genres Correlations

    You can see the correlation matrix 1 between favoritegenre and second-most favorite genre in the gure onthe right. Correlation indicates how frequently twovalues are associated with each other, and ranges from-1 to 1. In the case of data like this, the diagonals (i.e.when the favorite genre is the same as second-mostfavorite genre) do not mean anything, and the mostinteresting part is the higher correlation between peoplewho list classical music as their favorite genre and jazzas their second. Hip-hop and rap as well as rock andmetal also share a somewhat positive correlation. Noneof these values is particularly high, although thecorrelation between classical (favorite) and jazz(second-most favorite) is particularly notable.

    0 + + + + + + +

    0 + + + + + + + +

    + 0 + + + +

    + 0 + + + + + +

    + + 0 + +

    + 0 + + + + +

    + + + 0 + +

    + + + 0 + +

    + + + 0 + +

    + + + + 0 +

    + + + + 0 +

    + + + + 0 + + + + + + + + 0

    Classical

    Alternative

    Country

    Electronic

    Folk

    Hiphop

    Indie

    Jazz

    Metal

    Pop

    Punk

    Rap

    Rock

    C l a s s

    i c a

    l

    A l t e r n a

    t i v e

    C o u n

    t r y

    E l e c

    t r o n

    i c

    F o

    l k

    H i p

    h o p

    I n d i e

    J a z z

    M e

    t a l

    P o p

    P u n

    k

    R a p

    R o c

    k

    Favorite Genre

    S e c o n d

    m o s t

    F a v o r i

    t e G e n r e

    0.0

    0.1

    0.2

    Correlation

    Correlations between first and secondmost favorite genre

    Figure 35: The correlation matrix between second-most favorite genres and favorite genres among the sample population. Orange indicates higher positive correlations, dark blue indicates more negative. The signs of each entry are plotted in the cells.

    1 I used Pearsons correlation to generate this pot. The reasoning is that a) it is unbiased and b) it is easier to show to a general audience. Additionally, I set the diagonals to 0 so thatthe greatest negative correlations could be seen, since they would otherwise be on the diagonal.

    Orchestral preferences analysis MC 46

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    Favorite Genre & Classical Listening

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    Favorite Genre & Classical ListeningLooking at what peoples favorite genres are, youcan see that there are differences among variousgenres in terms of whether or not people who

    identify it as their favorite also listen to classicalmusic. There was one response for classical that wasNo, and I didnt want to exclude the possibilitythat someone who doesnt listen to a lot of musicmarked that answer down. Below is a table of theexact values for the responses.

    Yes NoAlternative 76 133

    Classical 243 1Country 5 10

    Electronic 52 114Folk 18 21

    Hip-hop 18 45Indie 63 132Jazz 43 25

    Metal 56 94Other 38 35

    Pop 23 36Punk 19 36

    Rap 8 8Rock 116 186

    Table 1: Total number of responses ineach category for favorite genre and classical listening.

    0.00

    0.25

    0.50

    0.75

    1.00

    H i p

    h o p

    E l e c t r o n i c

    I n d i e

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    P u n k

    A l t e r n a t i v e

    M e t a l

    R o c k

    P o p

    F o l k

    R a p

    O t h e r

    J a z z

    C l a s s i c a l

    Favorite Genre

    p r o p o r t i o n

    Classical Listener

    YesNo

    Classical listening by favorite genre

    Figure 36: Stacked barplot indicating how often people with a particular favorite genre tend to listen to classical music.

    Orchestral preferences analysis MC 47

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    Word Association - Clustering

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    Word Association ClusteringAfter asking people whatwords they associated withboth classical music and

    music they listen to, Iclustered each of theresponses by how often theywere associated with eachother 1 . On the right are twosets of dendrograms (tree-likediagrams), each of whichshow how close differentwords are to each other.Labels preceded by Classicalrefer to what adjectivespeople associated classicalmusic with, and labelspreceded by Listen refer toadjectives people used todescribe music they listen to.

    To read one of thedendrograms, look at howlong the closest junctionbetween two adjacent

    adjectives are. Thoseadjectives are the two mostcommonly associated witheach other. They form acluster with 2 members, andthen you can compare thatcluster with other clustersusing the same logic.

    Clustering of words Classical listeners

    List e n Loud

    Listen Boring

    Listen Complicated

    Listen Cool

    Listen Emotional

    Listen Epic

    Listen Ethereal

    Listen Exciting

    Listen Fast Listen Happy

    Listen New Listen Old

    Listen Repetitive

    Listen Sad Listen Simple

    Listen Soft Listen Slow

    Listen Wild

    Classical Loud

    Classical Boring

    Classical Complicat

    Classical Cool

    Classical Emotional Classical Epic

    Classical Ethereal

    Classical Exciting

    Classical Fast

    Classical Happy

    Classical New

    Classical Old

    Classical Repetitive

    Classical Sad

    Classical Simple

    Classical Soft

    Classical Slow

    Classical Wild

    Figure 37: Clustering of words for classical listeners.

    Clustering of words Nonclassicallisteners

    Listen Loud

    Listen Boring

    Listen Complicated

    Listen Cool

    Listen Emotional

    Listen Epic

    Listen Ethereal

    Listen Exciting

    Listen Fast

    Listen Happy

    Listen New Listen Old

    Listen Repetitive

    Listen Sad

    Listen Simple

    Listen Soft Listen Slow

    Listen Wild

    Classical Loud

    Classical Boring

    Classical Complicat

    Classical Cool

    Classical Emotional

    Classical Epic

    Classical Ethereal Classical Exciting

    Clas sic al Fast Classical Happy

    Classical New

    Classical Old

    Classical Repetitive

    Classical Sad

    Classical Simple

    Classical Soft Classical Slow

    Classical Wild

    Figure 38: Clustering of words for non-classical-listeners.

    1 Specically, I used Jaccard dissimilarity for the distance function and Wards method as the clustering method.

    Orchestral preferences analysis MC 48

    Overview Demographics Questions Programming Composers Genres Marketing Testimonials End

    High School Musical Experience

  • 8/13/2019 Analysis of Musical Preferences and Orchestral Music

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    High School Musical Experience

    Out of all the responses, 942 (57%) of respondents stated that they played

    a musical instrument in their high school band or orchestra, and 712 (43%)stated they did not. On this page I will show a few different sets of statistics contrasting these two groups. The two tables below show thepercentage of responses from each group towards their favorite genre of music.

    0%

    5%

    10%

    15%

    20%

    A l t e r n a t i v e

    C l a s s i c a l

    C o u n t r y

    E l e c t r o n i c

    F o l k

    H i p

    h o p

    I n d i e

    J a z z

    M e t a l

    O t h e r

    P o p

    P u n k

    R a p

    R o c k

    P e r c e n t a g e

    Participation

    Yes

    No

    Favorite Genre by HS Band/Orchestra Participation

    Figure 39: Favorite genres by participation in a high school band or orchestra.

    Looking at the statistics for favorite instrument section, there is a notable

    difference between the two groups. In particu


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