Month: July 2019

R Analysis 6: ANOVA & American Cartoons

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Hello everybody,

It’s Michael, and today’s post will be the 6th R analysis; the focus of this analysis, as you might have guessed from the title, will be ANOVA. The topic of this ANOVA analysis will be American cartoons.

Here’s the dataset-Cartoons.

Now, as always, let’s load our dataset into R and learn about each variable:

Screen Shot 2019-07-13 at 4.19.13 PM

As you can see, we’ve got 78 cartoons (the observations) listed along with 9 other variables that describe the cartoons (such as the name, rating, network, etc.). Here’s a variable-by-variable breakdown:

  • Name-The name of the cartoon
  • Debut.Year-The year the cartoon premiered
  • Seasons-The number of seasons the cartoon lasted or, if the show is still on the air, the amount of seasons that have aired as of July 1, 2019.
  • Episodes-The number of episodes the cartoon lasted, or, of the show is still on the air, the amount of episodes that have aired as of July 1, 2019.
  • Creator-The creator(s) of the cartoon.
  • Network-The network that currently airs new episodes of the cartoon. Some of the cartoons on this list, such as American Dad, switched networks (in that case, from FOX to TBS), so I list the network that currently airs new episodes.
  • Ended-Whether or not the show ended; this can either be a “Yes” or “No”.
  • Contract.Length-If Ended is a no, then this denotes the length of a show’s current contract. If Ended is a yes, then this field is blank.
  • Rating-The shows TV rating, which can be one of these five:
    • TV-Y
    • TV-Y7
    • TV-PG
    • TV-14
    • TV-MA

Now, let’s check for missing values (remember to install the Amelia package). Also remember to use the line missmap(name of file variable) to see the missmap:

Interestingly, the graph doesn’t list any of our variables as missing, even though the Contract.Length variable has several blank spots. However, those blank spots might’ve counted as a value.

Now, let’s do some one-way ANOVA:

In this model, I used Episodes as the dependent variable and Rating as the independent variable. I am trying to analyze the relationship (if there is any) between how many episodes a cartoon has aired and the cartoon’s TV rating (in other words, whether or not the cartoon is for kids). As you can see, Rating has 3 asterisks beside it, meaning that it is a very significant variable.

Now, let’s do a Tukey’s HSD Test to analyze pair-wise differences:

This Tukey’s HSD Test gives us the pair-wise differences among pairs of TV content ratings. Remember that a pair-wise difference with a p adj that is less than 0.01 is statistically significant. There are three statistically significant pair-wise differences, which include:

  • TV-PG; TV-14-this one is interesting because cartoons with TV-PG and TV-14 ratings often air on network TV. For example, FOX airs the TV-PG Simpsons and Bob’s Burgers along with the TV-14 Family Guy.
  • TV-PG; TV-MA-this one isn’t as interesting. Well, cartoons with both of these ratings air on cartoon network, though you’ll find plenty more cartoons with a TV-MA rating on Cartoon Network’s Adult Swim block (e.g. Robot Chicken, Aqua Teen Hunger Force)
  • TV-Y7; TV-PG-this one is quite interesting, since Cartoon Network has aired several shows with both of these ratings, such as the TV-Y7 Ed, Edd n Eddy and Camp Lazlo along with the TV-PG Regular Show and Adventure Time.

Now let’s create a two-way ANOVA model:

In this model, I once again used Episodes as a dependent variable and Rating as an independent variable. However, I also included Network (the network a show airs on) as another independent variable.

Just as with the previous model, Rating is a statistically significant variable, except this time it has slightly less significance, as there are two asterisks instead of three. Network, on the other hand, isn’t significant at all, as its p-value is much greater than 0.01.

Now let’s do a Tukey’s HSD test to analyze pair-wise differences. Since this is a two-way ANOVA, we will see pair-wise differences for :

Tukey multiple comparisons of means
99% family-wise confidence level

Fit: aov(formula = file$Episodes ~ file$Network + file$Rating)

$`file$Network`
diff lwr upr p adj
Comedy Central-Cartoon Network -18.6166667 -217.13053 179.89720 1.0000000
Crackle-Cartoon Network -79.3666667 -458.47404 299.74071 0.9997177
Disney Channel-Cartoon Network -39.8666667 -312.22545 232.49211 0.9999939
FOX-Cartoon Network 84.7583333 -63.63972 233.15638 0.5350384
FX-Cartoon Network -0.3666667 -379.47404 378.74071 1.0000000
G4-Cartoon Network 25.6333333 -353.47404 404.74071 1.0000000
Hulu-Cartoon Network -77.3666667 -456.47404 301.74071 0.9997826
Netflix-Cartoon Network -80.3666667 -222.10667 61.37334 0.5465350
Nickelodeon-Cartoon Network 0.6333333 -141.10667 142.37334 1.0000000
PBS-Cartoon Network 0.7444444 -140.99556 142.48445 1.0000000
Showtime-Cartoon Network -85.3666667 -464.47404 293.74071 0.9994113
TBS-Cartoon Network 33.6333333 -238.72545 305.99211 0.9999991
Crackle-Comedy Central -60.7500000 -477.71252 356.21252 0.9999942
Disney Channel-Comedy Central -21.2500000 -344.22778 301.72778 1.0000000
FOX-Comedy Central 103.3750000 -125.00478 331.75478 0.8359614
FX-Comedy Central 18.2500000 -398.71252 435.21252 1.0000000
G4-Comedy Central 44.2500000 -372.71252 461.21252 0.9999998
Hulu-Comedy Central -58.7500000 -475.71252 358.21252 0.9999960
Netflix-Comedy Central -61.7500000 -285.86062 162.36062 0.9959708
Nickelodeon-Comedy Central 19.2500000 -204.86062 243.36062 1.0000000
PBS-Comedy Central 19.3611111 -204.74951 243.47173 1.0000000
Showtime-Comedy Central -66.7500000 -483.71252 350.21252 0.9999835
TBS-Comedy Central 52.2500000 -270.72778 375.22778 0.9999815
Disney Channel-Crackle 39.5000000 -417.25956 496.25956 1.0000000
FOX-Crackle 164.1250000 -231.44038 559.69038 0.9020872
FX-Crackle 79.0000000 -448.42051 606.42051 0.9999921
G4-Crackle 105.0000000 -422.42051 632.42051 0.9998317
Hulu-Crackle 2.0000000 -525.42051 529.42051 1.0000000
Netflix-Crackle -1.0000000 -394.11604 392.11604 1.0000000
Nickelodeon-Crackle 80.0000000 -313.11604 473.11604 0.9997889
PBS-Crackle 80.1111111 -313.00492 473.22715 0.9997858
Showtime-Crackle -6.0000000 -533.42051 521.42051 1.0000000
TBS-Crackle 113.0000000 -343.75956 569.75956 0.9985243
FOX-Disney Channel 124.6250000 -170.21203 419.46203 0.8900907
FX-Disney Channel 39.5000000 -417.25956 496.25956 1.0000000
G4-Disney Channel 65.5000000 -391.25956 522.25956 0.9999951
Hulu-Disney Channel -37.5000000 -494.25956 419.25956 1.0000000
Netflix-Disney Channel -40.5000000 -332.04266 251.04266 0.9999966
Nickelodeon-Disney Channel 40.5000000 -251.04266 332.04266 0.9999966
PBS-Disney Channel 40.6111111 -250.93154 332.15377 0.9999965
Showtime-Disney Channel -45.5000000 -502.25956 411.25956 0.9999999
TBS-Disney Channel 73.5000000 -299.44262 446.44262 0.9998485
FX-FOX -85.1250000 -480.69038 310.44038 0.9996265
G4-FOX -59.1250000 -454.69038 336.44038 0.9999922
Hulu-FOX -162.1250000 -557.69038 233.44038 0.9094227
Netflix-FOX -165.1250000 -346.34254 16.09254 0.0287424
Nickelodeon-FOX -84.1250000 -265.34254 97.09254 0.8118260
PBS-FOX -84.0138889 -265.23143 97.20365 0.8131464
Showtime-FOX -170.1250000 -565.69038 225.44038 0.8778977
TBS-FOX -51.1250000 -345.96203 243.71203 0.9999609
G4-FX 26.0000000 -501.42051 553.42051 1.0000000
Hulu-FX -77.0000000 -604.42051 450.42051 0.9999940
Netflix-FX -80.0000000 -473.11604 313.11604 0.9997889
Nickelodeon-FX 1.0000000 -392.11604 394.11604 1.0000000
PBS-FX 1.1111111 -392.00492 394.22715 1.0000000
Showtime-FX -85.0000000 -612.42051 442.42051 0.9999823
TBS-FX 34.0000000 -422.75956 490.75956 1.0000000
Hulu-G4 -103.0000000 -630.42051 424.42051 0.9998622
Netflix-G4 -106.0000000 -499.11604 287.11604 0.9966917
Nickelodeon-G4 -25.0000000 -418.11604 368.11604 1.0000000
PBS-G4 -24.8888889 -418.00492 368.22715 1.0000000
Showtime-G4 -111.0000000 -638.42051 416.42051 0.9997021
TBS-G4 8.0000000 -448.75956 464.75956 1.0000000
Netflix-Hulu -3.0000000 -396.11604 390.11604 1.0000000
Nickelodeon-Hulu 78.0000000 -315.11604 471.11604 0.9998375
PBS-Hulu 78.1111111 -315.00492 471.22715 0.9998351
Showtime-Hulu -8.0000000 -535.42051 519.42051 1.0000000
TBS-Hulu 111.0000000 -345.75956 567.75956 0.9987571
Nickelodeon-Netflix 81.0000000 -94.80684 256.80684 0.8192765
PBS-Netflix 81.1111111 -94.69572 256.91795 0.8179377
Showtime-Netflix -5.0000000 -398.11604 388.11604 1.0000000
TBS-Netflix 114.0000000 -177.54266 405.54266 0.9335787
PBS-Nickelodeon 0.1111111 -175.69572 175.91795 1.0000000
Showtime-Nickelodeon -86.0000000 -479.11604 307.11604 0.9995591
TBS-Nickelodeon 33.0000000 -258.54266 324.54266 0.9999997
Showtime-PBS -86.1111111 -479.22715 307.00492 0.9995533
TBS-PBS 32.8888889 -258.65377 324.43154 0.9999997
TBS-Showtime 119.0000000 -337.75956 575.75956 0.9975931

$`file$Rating`
diff lwr upr p adj
TV-MA-TV-14 46.329035 -63.199178 155.85725 0.6047355
TV-PG-TV-14 147.497292 8.667629 286.32695 0.0052996
TV-Y-TV-14 56.355123 -77.349896 190.06014 0.6079772
TV-Y7-TV-14 51.949774 -50.169122 154.06867 0.4226375
TV-PG-TV-MA 101.168257 -32.481774 234.81829 0.0874937
TV-Y-TV-MA 10.026088 -118.292607 138.34478 0.9988693
TV-Y7-TV-MA 5.620739 -89.336747 100.57822 0.9996220
TV-Y-TV-PG -91.142168 -245.229589 62.94525 0.2719733
TV-Y7-TV-PG -95.547518 -223.196137 32.10110 0.0934011
TV-Y7-TV-Y -4.405350 -126.460775 117.65008 0.9999471

Just like the Tukey’s Test for the model I created in R Lesson 18: ANOVA part 2, each variable’s pair-wise differences are analyzed separately. In other words,  the pair-wise differences for Network are analyzed separately from the pair-wise differences for Rating.

Another thing I want to note is that since all of the p-values for the Network pair-wise differences are well above 0.01, there aren’t any statistically significant pair-wise differences for Network. On the other hand, there is a statistically significant pair-wise difference for Rating, which would be the TV-PG - TV-14 pair. Unlike the one-way ANOVA, Rating has a lower statistical significance (since there are two asterisks by Rating instead of three) and thus has fewer statistically significant pair-wise differences.

From the data, I can conclude that a cartoon’s rating (if you don’t factor in any other variables) is a good indicator as to how many episodes that cartoon will air. However, the combination of a cartoon’s rating and the network it airs on makes it less certain to predict how many episodes might air.

Thanks for reading,

Michael

 

 

 

 

R Lesson 18: ANOVA part 2

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Hello everybody,

It’s Michael, and today I will discuss two-way ANOVA. This post will act as a continuation for my previous lesson on one-way ANOVA, although I will not use the same dataset from the previous post. As you may recall from the previous post, two-way ANOVA is just ANOVA with two independent variables instead of one.

Here’s the dataset-Celebrities.

Now, let’s load our dataset into R and learn more about our variables:

This dataset contains the names of 104 celebrities who died in the 2010s (the observations) along with 6 variables (the main focus is on the celebrities’ net worth at the time of their deaths):

  • Name-the celebrity’s name
  • Date.of.Death-the date of the celebrity’s death
  • Age.of.Death-how old the celebrity was when he/she died
  • Occupation-the celebrity’s profession
  • Net.Worth-the celebrity’s net worth at the time of their death; this doesn’t factor in the possibility of the celebrity’s estate continuing to earn money long after their death
    • Net.Worth is given in millions, so for instance Nipsey Hussle’s net worth is listed as 8, which means he had $8 million net worth at the time of his death.
    • There are some special cases, such as Mark Salling, whose net worth is listed as 0.1, meaning that he only had a net worth of $100 thousand when he died. Steve Jobs is the richest person on this list, as his net worth is listed at 10200, or $10.2 billion. On the other hand, Whitney Houston is the poorest person on this list, as her net worth is listed at -20, which means she died with $20 million in debt.
  • Cause-the celebrity’s cause of death

Now, I would usually check for missing data (which I didn’t do for R Lesson 17: ANOVA Part 1), but since I wrote the datasets for both ANOVA posts, I know there’s no missing data.

Now let’s set up our model:

In this model, I used Net.Worth as the dependent variable and Occupation and Age.at.Death as independent variables. Why did I make Age.at.Death a factor? In ANOVA, the independent variables need to be categorical (non-numerical) rather than continuous (numerical). Since Age.at.Death is a numerical variable, using the as.factor function would convert it into a non-numerical/categorical variable.

Both of the variables have high f-values and in turn, high significance levels (due to the p-values being much less than 0.01). This means that we can reject the null hypothesis; in other words, a celebrity’s occupation and age at death are related to their net worth at the time of their death.

Now let’s do a Tukey’s HSD test on our model, using the line TukeyHSD(model2,conf.level=0.99). Since we have two variables, pairwise comparisons for each variable are displayed separately.

First, here are the pairwise comparisons for the Occupation variable:

diff lwr upr p adj
activist-41st president -2.237500e+01 -353.196211 308.44621 1.0000000
actor-41st president -5.898696e+00 -281.822660 270.02527 1.0000000
actress-41st president 9.444444e+01 -190.281121 379.17001 0.9912907
animator-41st president 9.500000e+01 -286.999431 476.99943 0.9998272
artist-41st president 1.533333e+01 -296.567896 327.23456 1.0000000
astronaut-41st president -1.850000e+01 -349.321211 312.32121 1.0000000
athlete-41st president -1.440714e+01 -303.171570 274.35728 1.0000000
author-41st president 5.200000e+01 -243.895487 347.89549 0.9999996
businessman-41st president 1.711750e+03 1419.993115 2003.50688 0.0000000
chef-41st president -9.000000e+00 -390.999431 372.99943 1.0000000
comedian-41st president 1.250000e+02 -256.999431 506.99943 0.9925625
director-41st president 1.666667e+01 -295.234563 328.56790 1.0000000
DJ-41st president 4.000000e+01 -290.821211 370.82121 1.0000000
fashion designer-41st president 1.516667e+02 -160.234563 463.56790 0.7685610
film critic-41st president -1.600000e+01 -397.999431 365.99943 1.0000000
first lady-41st president -1.666667e+00 -313.567896 310.23456 1.0000000
football coach-41st president -1.500000e+01 -396.999431 366.99943 1.0000000
journalist-41st president -2.100000e+01 -402.999431 360.99943 1.0000000
pastor-41st president -3.268497e-13 -381.999431 381.99943 1.0000000
physicist-41st president -5.000000e+00 -386.999431 376.99943 1.0000000
producer-41st president 8.250000e+01 -248.321211 413.32121 0.9998196
rapper-41st president -2.019167e+01 -311.948551 271.56522 1.0000000
singer-41st president 4.140000e+01 -236.545412 319.34541 1.0000000
sportscaster-41st president -1.550000e+01 -397.499431 366.49943 1.0000000
US Senator-41st president -9.000000e+00 -390.999431 372.99943 1.0000000
actor-activist 1.647630e+01 -182.654664 215.60727 1.0000000
actress-activist 1.168194e+02 -94.338686 327.97757 0.5702341
animator-activist 1.173750e+02 -213.446211 448.19621 0.9817419
artist-activist 3.770833e+01 -208.871239 284.28791 1.0000000
astronaut-activist 3.875000e+00 -266.239388 273.98939 1.0000000
athlete-activist 7.967857e+00 -208.605463 224.54118 1.0000000
author-activist 7.437500e+01 -151.618911 300.36891 0.9920491
businessman-activist 1.734125e+03 1513.577526 1954.67247 0.0000000
chef-activist 1.337500e+01 -317.446211 344.19621 1.0000000
comedian-activist 1.473750e+02 -183.446211 478.19621 0.8670545
director-activist 3.904167e+01 -207.537906 285.62124 1.0000000
DJ-activist 6.237500e+01 -207.739388 332.48939 0.9999475
fashion designer-activist 1.740417e+02 -72.537906 420.62124 0.1927916
film critic-activist 6.375000e+00 -324.446211 337.19621 1.0000000
first lady-activist 2.070833e+01 -225.871239 267.28791 1.0000000
football coach-activist 7.375000e+00 -323.446211 338.19621 1.0000000
journalist-activist 1.375000e+00 -329.446211 332.19621 1.0000000
pastor-activist 2.237500e+01 -308.446211 353.19621 1.0000000
physicist-activist 1.737500e+01 -313.446211 348.19621 1.0000000
producer-activist 1.048750e+02 -165.239388 374.98939 0.9559022
rapper-activist 2.183333e+00 -218.364141 222.73081 1.0000000
singer-activist 6.377500e+01 -138.147661 265.69766 0.9951348
sportscaster-activist 6.875000e+00 -323.946211 337.69621 1.0000000
US Senator-activist 1.337500e+01 -317.446211 344.19621 1.0000000
actress-actor 1.003431e+02 -5.860043 206.54632 0.0181925
animator-actor 1.008987e+02 -175.025269 376.82266 0.9751205
artist-actor 2.123203e+01 -144.577638 187.04170 1.0000000
astronaut-actor -1.260130e+01 -211.732273 186.52966 1.0000000
athlete-actor -8.508447e+00 -125.107603 108.09071 1.0000000
author-actor 5.789870e+01 -75.385242 191.18263 0.8890175
businessman-actor 1.717649e+03 1593.824027 1841.47336 0.0000000
chef-actor -3.101304e+00 -279.025269 272.82266 1.0000000
comedian-actor 1.308987e+02 -145.025269 406.82266 0.7997856
director-actor 2.256536e+01 -143.244305 188.37503 1.0000000
DJ-actor 4.589870e+01 -153.232273 245.02966 0.9999491
fashion designer-actor 1.575654e+02 -8.244305 323.37503 0.0171557
film critic-actor -1.010130e+01 -286.025269 265.82266 1.0000000
first lady-actor 4.232029e+00 -161.577638 170.04170 1.0000000
football coach-actor -9.101304e+00 -285.025269 266.82266 1.0000000
journalist-actor -1.510130e+01 -291.025269 260.82266 1.0000000
pastor-actor 5.898696e+00 -270.025269 281.82266 1.0000000
physicist-actor 8.986957e-01 -275.025269 276.82266 1.0000000
producer-actor 8.839870e+01 -110.732273 287.52966 0.8702814
rapper-actor -1.429297e+01 -138.117640 109.53170 1.0000000
singer-actor 4.729870e+01 -39.096449 133.69384 0.5878943
sportscaster-actor -9.601304e+00 -285.525269 266.32266 1.0000000
US Senator-actor -3.101304e+00 -279.025269 272.82266 1.0000000
animator-actress 5.555556e-01 -284.170009 285.28112 1.0000000
artist-actress -7.911111e+01 -259.187370 100.96515 0.8795454
astronaut-actress -1.129444e+02 -324.102575 98.21369 0.6264662
athlete-actress -1.088516e+02 -244.976444 27.27327 0.0813248
author-actress -4.244444e+01 -193.107052 108.21816 0.9989104
businessman-actress 1.617306e+03 1474.942773 1759.66834 0.0000000
chef-actress -1.034444e+02 -388.170009 181.28112 0.9766920
comedian-actress 3.055556e+01 -254.170009 315.28112 1.0000000
director-actress -7.777778e+01 -257.854036 102.29848 0.8936327
DJ-actress -5.444444e+01 -265.602575 156.71369 0.9996991
fashion designer-actress 5.722222e+01 -122.854036 237.29848 0.9947597
film critic-actress -1.104444e+02 -395.170009 174.28112 0.9562763
first lady-actress -9.611111e+01 -276.187370 83.96515 0.6299987
football coach-actress -1.094444e+02 -394.170009 175.28112 0.9597755
journalist-actress -1.154444e+02 -400.170009 169.28112 0.9355653
pastor-actress -9.444444e+01 -379.170009 190.28112 0.9912907
physicist-actress -9.944444e+01 -384.170009 185.28112 0.9845452
producer-actress -1.194444e+01 -223.102575 199.21369 1.0000000
rapper-actress -1.146361e+02 -256.998894 27.72667 0.0769968
singer-actress -5.304444e+01 -164.394048 58.30516 0.7947154
sportscaster-actress -1.099444e+02 -394.670009 174.78112 0.9580517
US Senator-actress -1.034444e+02 -388.170009 181.28112 0.9766920
artist-animator -7.966667e+01 -391.567896 232.23456 0.9997391
astronaut-animator -1.135000e+02 -444.321211 217.32121 0.9872684
athlete-animator -1.094071e+02 -398.171570 179.35728 0.9648528
author-animator -4.300000e+01 -338.895487 252.89549 1.0000000
businessman-animator 1.616750e+03 1324.993115 1908.50688 0.0000000
chef-animator -1.040000e+02 -485.999431 277.99943 0.9993283
comedian-animator 3.000000e+01 -351.999431 411.99943 1.0000000
director-animator -7.833333e+01 -390.234563 233.56790 0.9997987
DJ-animator -5.500000e+01 -385.821211 275.82121 0.9999999
fashion designer-animator 5.666667e+01 -255.234563 368.56790 0.9999993
film critic-animator -1.110000e+02 -492.999431 270.99943 0.9983352
first lady-animator -9.666667e+01 -408.567896 215.23456 0.9961502
football coach-animator -1.100000e+02 -491.999431 271.99943 0.9985272
journalist-animator -1.160000e+02 -497.999431 265.99943 0.9970282
pastor-animator -9.500000e+01 -476.999431 286.99943 0.9998272
physicist-animator -1.000000e+02 -481.999431 281.99943 0.9996220
producer-animator -1.250000e+01 -343.321211 318.32121 1.0000000
rapper-animator -1.151917e+02 -406.948551 176.56522 0.9487637
singer-animator -5.360000e+01 -331.545412 224.34541 0.9999978
sportscaster-animator -1.105000e+02 -492.499431 271.49943 0.9984337
US Senator-animator -1.040000e+02 -485.999431 277.99943 0.9993283
astronaut-artist -3.383333e+01 -280.412906 212.74624 1.0000000
athlete-artist -2.974048e+01 -216.137112 156.65616 0.9999999
author-artist 3.666667e+01 -160.596991 233.93032 0.9999989
businessman-artist 1.696417e+03 1505.416951 1887.41638 0.0000000
chef-artist -2.433333e+01 -336.234563 287.56790 1.0000000
comedian-artist 1.096667e+02 -202.234563 421.56790 0.9834023
director-artist 1.333333e+00 -219.214141 221.88081 1.0000000
DJ-artist 2.466667e+01 -221.912906 271.24624 1.0000000
fashion designer-artist 1.363333e+02 -84.214141 356.88081 0.3818703
film critic-artist -3.133333e+01 -343.234563 280.56790 1.0000000
first lady-artist -1.700000e+01 -237.547474 203.54747 1.0000000
football coach-artist -3.033333e+01 -342.234563 281.56790 1.0000000
journalist-artist -3.633333e+01 -348.234563 275.56790 1.0000000
pastor-artist -1.533333e+01 -327.234563 296.56790 1.0000000
physicist-artist -2.033333e+01 -332.234563 291.56790 1.0000000
producer-artist 6.716667e+01 -179.412906 313.74624 0.9993232
rapper-artist -3.552500e+01 -226.524716 155.47472 0.9999989
singer-artist 2.606667e+01 -143.085525 195.21886 1.0000000
sportscaster-artist -3.083333e+01 -342.734563 281.06790 1.0000000
US Senator-artist -2.433333e+01 -336.234563 287.56790 1.0000000
athlete-astronaut 4.092857e+00 -212.480463 220.66618 1.0000000
author-astronaut 7.050000e+01 -155.493911 296.49391 0.9958237
businessman-astronaut 1.730250e+03 1509.702526 1950.79747 0.0000000
chef-astronaut 9.500000e+00 -321.321211 340.32121 1.0000000
comedian-astronaut 1.435000e+02 -187.321211 474.32121 0.8902022
director-astronaut 3.516667e+01 -211.412906 281.74624 1.0000000
DJ-astronaut 5.850000e+01 -211.614388 328.61439 0.9999823
fashion designer-astronaut 1.701667e+02 -76.412906 416.74624 0.2201389
film critic-astronaut 2.500000e+00 -328.321211 333.32121 1.0000000
first lady-astronaut 1.683333e+01 -229.746239 263.41291 1.0000000
football coach-astronaut 3.500000e+00 -327.321211 334.32121 1.0000000
journalist-astronaut -2.500000e+00 -333.321211 328.32121 1.0000000
pastor-astronaut 1.850000e+01 -312.321211 349.32121 1.0000000
physicist-astronaut 1.350000e+01 -317.321211 344.32121 1.0000000
producer-astronaut 1.010000e+02 -169.114388 371.11439 0.9690196
rapper-astronaut -1.691667e+00 -222.239141 218.85581 1.0000000
singer-astronaut 5.990000e+01 -142.022661 261.82266 0.9978071
sportscaster-astronaut 3.000000e+00 -327.821211 333.82121 1.0000000
US Senator-astronaut 9.500000e+00 -321.321211 340.32121 1.0000000
author-athlete 6.640714e+01 -91.755648 224.56993 0.9143646
businessman-athlete 1.726157e+03 1575.879370 1876.43492 0.0000000
chef-athlete 5.407143e+00 -283.357284 294.17157 1.0000000
comedian-athlete 1.394071e+02 -149.357284 428.17157 0.7779470
director-athlete 3.107381e+01 -155.322827 217.47045 0.9999999
DJ-athlete 5.440714e+01 -162.166178 270.98046 0.9997979
fashion designer-athlete 1.660738e+02 -20.322827 352.47045 0.0322196
film critic-athlete -1.592857e+00 -290.357284 287.17157 1.0000000
first lady-athlete 1.274048e+01 -173.656160 199.13711 1.0000000
football coach-athlete -5.928571e-01 -289.357284 288.17157 1.0000000
journalist-athlete -6.592857e+00 -295.357284 282.17157 1.0000000
pastor-athlete 1.440714e+01 -274.357284 303.17157 1.0000000
physicist-athlete 9.407143e+00 -279.357284 298.17157 1.0000000
producer-athlete 9.690714e+01 -119.666178 313.48046 0.8629084
rapper-athlete -5.784524e+00 -156.062296 144.49325 1.0000000
singer-athlete 5.580714e+01 -65.498180 177.11247 0.8348572
sportscaster-athlete -1.092857e+00 -289.857284 287.67157 1.0000000
US Senator-athlete 5.407143e+00 -283.357284 294.17157 1.0000000
businessman-author 1.659750e+03 1496.187615 1823.31238 0.0000000
chef-author -6.100000e+01 -356.895487 234.89549 0.9999925
comedian-author 7.300000e+01 -222.895487 368.89549 0.9998476
director-author -3.533333e+01 -232.596991 161.93032 0.9999995
DJ-author -1.200000e+01 -237.993911 213.99391 1.0000000
fashion designer-author 9.966667e+01 -97.596991 296.93032 0.7152060
film critic-author -6.800000e+01 -363.895487 227.89549 0.9999515
first lady-author -5.366667e+01 -250.930325 143.59699 0.9993352
football coach-author -6.700000e+01 -362.895487 228.89549 0.9999622
journalist-author -7.300000e+01 -368.895487 222.89549 0.9998476
pastor-author -5.200000e+01 -347.895487 243.89549 0.9999996
physicist-author -5.700000e+01 -352.895487 238.89549 0.9999978
producer-author 3.050000e+01 -195.493911 256.49391 1.0000000
rapper-author -7.219167e+01 -235.754051 91.37072 0.8754649
singer-author -1.060000e+01 -148.019890 126.81989 1.0000000
sportscaster-author -6.750000e+01 -363.395487 228.39549 0.9999571
US Senator-author -6.100000e+01 -356.895487 234.89549 0.9999925
chef-businessman -1.720750e+03 -2012.506885 -1428.99312 0.0000000
comedian-businessman -1.586750e+03 -1878.506885 -1294.99312 0.0000000
director-businessman -1.695083e+03 -1886.083049 -1504.08362 0.0000000
DJ-businessman -1.671750e+03 -1892.297474 -1451.20253 0.0000000
fashion designer-businessman -1.560083e+03 -1751.083049 -1369.08362 0.0000000
film critic-businessman -1.727750e+03 -2019.506885 -1435.99312 0.0000000
first lady-businessman -1.713417e+03 -1904.416382 -1522.41695 0.0000000
football coach-businessman -1.726750e+03 -2018.506885 -1434.99312 0.0000000
journalist-businessman -1.732750e+03 -2024.506885 -1440.99312 0.0000000
pastor-businessman -1.711750e+03 -2003.506885 -1419.99312 0.0000000
physicist-businessman -1.716750e+03 -2008.506885 -1424.99312 0.0000000
producer-businessman -1.629250e+03 -1849.797474 -1408.70253 0.0000000
rapper-businessman -1.731942e+03 -1887.892281 -1575.99105 0.0000000
singer-businessman -1.670350e+03 -1798.616000 -1542.08400 0.0000000
sportscaster-businessman -1.727250e+03 -2019.006885 -1435.49312 0.0000000
US Senator-businessman -1.720750e+03 -2012.506885 -1428.99312 0.0000000
comedian-chef 1.340000e+02 -247.999431 515.99943 0.9838100
director-chef 2.566667e+01 -286.234563 337.56790 1.0000000
DJ-chef 4.900000e+01 -281.821211 379.82121 1.0000000
fashion designer-chef 1.606667e+02 -151.234563 472.56790 0.6861785
film critic-chef -7.000000e+00 -388.999431 374.99943 1.0000000
first lady-chef 7.333333e+00 -304.567896 319.23456 1.0000000
football coach-chef -6.000000e+00 -387.999431 375.99943 1.0000000
journalist-chef -1.200000e+01 -393.999431 369.99943 1.0000000
pastor-chef 9.000000e+00 -372.999431 390.99943 1.0000000
physicist-chef 4.000000e+00 -377.999431 385.99943 1.0000000
producer-chef 9.150000e+01 -239.321211 422.32121 0.9991584
rapper-chef -1.119167e+01 -302.948551 280.56522 1.0000000
singer-chef 5.040000e+01 -227.545412 328.34541 0.9999993
sportscaster-chef -6.500000e+00 -388.499431 375.49943 1.0000000
US Senator-chef -2.842171e-13 -381.999431 381.99943 1.0000000
director-comedian -1.083333e+02 -420.234563 203.56790 0.9854411
DJ-comedian -8.500000e+01 -415.821211 245.82121 0.9997146
fashion designer-comedian 2.666667e+01 -285.234563 338.56790 1.0000000
film critic-comedian -1.410000e+02 -522.999431 240.99943 0.9726971
first lady-comedian -1.266667e+02 -438.567896 185.23456 0.9346884
football coach-comedian -1.400000e+02 -521.999431 241.99943 0.9745592
journalist-comedian -1.460000e+02 -527.999431 235.99943 0.9618505
pastor-comedian -1.250000e+02 -506.999431 256.99943 0.9925625
physicist-comedian -1.300000e+02 -511.999431 251.99943 0.9883604
producer-comedian -4.250000e+01 -373.321211 288.32121 1.0000000
rapper-comedian -1.451917e+02 -436.948551 146.56522 0.7370064
singer-comedian -8.360000e+01 -361.545412 194.34541 0.9973727
sportscaster-comedian -1.405000e+02 -522.499431 241.49943 0.9736403
US Senator-comedian -1.340000e+02 -515.999431 247.99943 0.9838100
DJ-director 2.333333e+01 -223.246239 269.91291 1.0000000
fashion designer-director 1.350000e+02 -85.547474 355.54747 0.3980601
film critic-director -3.266667e+01 -344.567896 279.23456 1.0000000
first lady-director -1.833333e+01 -238.880808 202.21414 1.0000000
football coach-director -3.166667e+01 -343.567896 280.23456 1.0000000
journalist-director -3.766667e+01 -349.567896 274.23456 1.0000000
pastor-director -1.666667e+01 -328.567896 295.23456 1.0000000
physicist-director -2.166667e+01 -333.567896 290.23456 1.0000000
producer-director 6.583333e+01 -180.746239 312.41291 0.9994942
rapper-director -3.685833e+01 -227.858049 154.14138 0.9999978
singer-director 2.473333e+01 -144.418858 193.88552 1.0000000
sportscaster-director -3.216667e+01 -344.067896 279.73456 1.0000000
US Senator-director -2.566667e+01 -337.567896 286.23456 1.0000000
fashion designer-DJ 1.116667e+02 -134.912906 358.24624 0.8512039
film critic-DJ -5.600000e+01 -386.821211 274.82121 0.9999998
first lady-DJ -4.166667e+01 -288.246239 204.91291 0.9999998
football coach-DJ -5.500000e+01 -385.821211 275.82121 0.9999999
journalist-DJ -6.100000e+01 -391.821211 269.82121 0.9999990
pastor-DJ -4.000000e+01 -370.821211 290.82121 1.0000000
physicist-DJ -4.500000e+01 -375.821211 285.82121 1.0000000
producer-DJ 4.250000e+01 -227.614388 312.61439 1.0000000
rapper-DJ -6.019167e+01 -280.739141 160.35581 0.9993042
singer-DJ 1.400000e+00 -200.522661 203.32266 1.0000000
sportscaster-DJ -5.550000e+01 -386.321211 275.32121 0.9999998
US Senator-DJ -4.900000e+01 -379.821211 281.82121 1.0000000
film critic-fashion designer -1.676667e+02 -479.567896 144.23456 0.6182614
first lady-fashion designer -1.533333e+02 -373.880808 67.21414 0.2109002
football coach-fashion designer -1.666667e+02 -478.567896 145.23456 0.6280663
journalist-fashion designer -1.726667e+02 -484.567896 139.23456 0.5691260
pastor-fashion designer -1.516667e+02 -463.567896 160.23456 0.7685610
physicist-fashion designer -1.566667e+02 -468.567896 155.23456 0.7237307
producer-fashion designer -6.916667e+01 -315.746239 177.41291 0.9989741
rapper-fashion designer -1.718583e+02 -362.858049 19.14138 0.0293733
singer-fashion designer -1.102667e+02 -279.418858 58.88552 0.2986636
sportscaster-fashion designer -1.671667e+02 -479.067896 144.73456 0.6231663
US Senator-fashion designer -1.606667e+02 -472.567896 151.23456 0.6861785
first lady-film critic 1.433333e+01 -297.567896 326.23456 1.0000000
football coach-film critic 1.000000e+00 -380.999431 382.99943 1.0000000
journalist-film critic -5.000000e+00 -386.999431 376.99943 1.0000000
pastor-film critic 1.600000e+01 -365.999431 397.99943 1.0000000
physicist-film critic 1.100000e+01 -370.999431 392.99943 1.0000000
producer-film critic 9.850000e+01 -232.321211 429.32121 0.9976983
rapper-film critic -4.191667e+00 -295.948551 287.56522 1.0000000
singer-film critic 5.740000e+01 -220.545412 335.34541 0.9999923
sportscaster-film critic 5.000000e-01 -381.499431 382.49943 1.0000000
US Senator-film critic 7.000000e+00 -374.999431 388.99943 1.0000000
football coach-first lady -1.333333e+01 -325.234563 298.56790 1.0000000
journalist-first lady -1.933333e+01 -331.234563 292.56790 1.0000000
pastor-first lady 1.666667e+00 -310.234563 313.56790 1.0000000
physicist-first lady -3.333333e+00 -315.234563 308.56790 1.0000000
producer-first lady 8.416667e+01 -162.412906 330.74624 0.9879665
rapper-first lady -1.852500e+01 -209.524716 172.47472 1.0000000
singer-first lady 4.306667e+01 -126.085525 212.21886 0.9997516
sportscaster-first lady -1.383333e+01 -325.734563 298.06790 1.0000000
US Senator-first lady -7.333333e+00 -319.234563 304.56790 1.0000000
journalist-football coach -6.000000e+00 -387.999431 375.99943 1.0000000
pastor-football coach 1.500000e+01 -366.999431 396.99943 1.0000000
physicist-football coach 1.000000e+01 -371.999431 391.99943 1.0000000
producer-football coach 9.750000e+01 -233.321211 428.32121 0.9979882
rapper-football coach -5.191667e+00 -296.948551 286.56522 1.0000000
singer-football coach 5.640000e+01 -221.545412 334.34541 0.9999944
sportscaster-football coach -5.000000e-01 -382.499431 381.49943 1.0000000
US Senator-football coach 6.000000e+00 -375.999431 387.99943 1.0000000
pastor-journalist 2.100000e+01 -360.999431 402.99943 1.0000000
physicist-journalist 1.600000e+01 -365.999431 397.99943 1.0000000
producer-journalist 1.035000e+02 -227.321211 434.32121 0.9956713
rapper-journalist 8.083333e-01 -290.948551 292.56522 1.0000000
singer-journalist 6.240000e+01 -215.545412 340.34541 0.9999672
sportscaster-journalist 5.500000e+00 -376.499431 387.49943 1.0000000
US Senator-journalist 1.200000e+01 -369.999431 393.99943 1.0000000
physicist-pastor -5.000000e+00 -386.999431 376.99943 1.0000000
producer-pastor 8.250000e+01 -248.321211 413.32121 0.9998196
rapper-pastor -2.019167e+01 -311.948551 271.56522 1.0000000
singer-pastor 4.140000e+01 -236.545412 319.34541 1.0000000
sportscaster-pastor -1.550000e+01 -397.499431 366.49943 1.0000000
US Senator-pastor -9.000000e+00 -390.999431 372.99943 1.0000000
producer-physicist 8.750000e+01 -243.321211 418.32121 0.9995594
rapper-physicist -1.519167e+01 -306.948551 276.56522 1.0000000
singer-physicist 4.640000e+01 -231.545412 324.34541 0.9999999
sportscaster-physicist -1.050000e+01 -392.499431 371.49943 1.0000000
US Senator-physicist -4.000000e+00 -385.999431 377.99943 1.0000000
rapper-producer -1.026917e+02 -323.239141 117.85581 0.8216250
singer-producer -4.110000e+01 -243.022661 160.82266 0.9999941
sportscaster-producer -9.800000e+01 -428.821211 232.82121 0.9978473
US Senator-producer -9.150000e+01 -422.321211 239.32121 0.9991584
singer-rapper 6.159167e+01 -66.674333 189.85767 0.7847897
sportscaster-rapper 4.691667e+00 -287.065218 296.44855 1.0000000
US Senator-rapper 1.119167e+01 -280.565218 302.94855 1.0000000
sportscaster-singer -5.690000e+01 -334.845412 221.04541 0.9999934
US Senator-singer -5.040000e+01 -328.345412 227.54541 0.9999993
US Senator-sportscaster 6.500000e+00 -375.499431 388.49943 1.0000000

Recall that diff is the pairwise difference between the two observations, lwr and upr are the lower and upper limits for the pair at a 99% confidence level, respectively, and p adj is the p-value for the pair. Remember that a pairwise difference is statistically significant if the p-value is 0.01 or less.

Some interesting pairwise differences include (rounded to two decimal places):

  • artist-activist-(-208.87)
    • Makes sense. After all, artists (e.g. Thomas Kinkade) and activists (e.g. Elie Wiesel) have very different lines of work.
  • fashion designer-artist-136.33
    • This is an interesting difference. After all, if you’ve got a very broad definition of the word “artist”, fashion designers such as Kate Spade and Alexander McQueen could be considered artists.
  • producer-DJ-42.5
    • Another interesting difference since DJs such as AVICII can be viewed as producers of music.
  • singer-rapper-61.59
    • Yet another interesting difference since rappers are singers (I just decided to put them in separate categories when I wrote this dataset).

Now here are the pairwise differences for the Age.at.Death variable:

diff lwr upr p adj
22-20 -6.579167e+01 -490.449623 358.866290 1.0000000
26-20 4.000000e+00 -420.657956 428.657956 1.0000000
27-20 -2.553972e+01 -372.271490 321.192049 1.0000000
28-20 -1.519167e+01 -439.849623 409.466290 1.0000000
31-20 -1.729297e+01 -441.950927 407.364985 1.0000000
33-20 3.607738e+00 -364.156840 371.372316 1.0000000
34-20 -1.329297e+01 -437.950927 411.364985 1.0000000
35-20 -1.919297e+01 -443.850927 405.464985 1.0000000
38-20 -1.928797e+01 -443.945927 405.369985 1.0000000
40-20 -7.057565e+01 -438.340230 297.188926 1.0000000
41-20 -2.004583e+01 -387.810412 347.718745 1.0000000
42-20 -1.028452e+01 -434.942480 414.373433 1.0000000
44-20 -4.650000e+00 -429.307956 420.007956 1.0000000
45-20 -1.421085e-12 -424.657956 424.657956 1.0000000
46-20 1.570703e+01 -408.950927 440.364985 1.0000000
47-20 -3.175595e+00 -370.940173 364.588983 1.0000000
48-20 -5.613377e+01 -402.865538 290.598001 1.0000000
49-20 -1.914297e+01 -443.800927 405.514985 1.0000000
51-20 2.192435e+01 -345.840230 389.688926 1.0000000
52-20 -7.942319e+00 -375.706897 359.822259 1.0000000
53-20 1.334083e+02 -291.249623 558.066290 0.9997168
54-20 6.299168e+00 -340.432601 353.030938 1.0000000
55-20 -8.319000e+02 -1199.664578 -464.135422 0.0000000
56-20 8.463058e+03 8038.400377 8887.716290 0.0000000
57-20 1.166083e+02 -251.156245 484.372912 0.9996587
60-20 -9.463611e+01 -519.294068 330.021845 1.0000000
61-20 6.774565e+00 -339.957204 353.506335 1.0000000
63-20 3.070703e+01 -393.950927 455.364985 1.0000000
65-20 -8.585667e+02 -1226.331245 -490.802088 0.0000000
66-20 1.256083e+02 -242.156245 493.372912 0.9986051
67-20 -1.068452e+01 -435.342480 413.973433 1.0000000
69-20 5.608568e+01 -290.646093 402.817446 1.0000000
70-20 -3.547315e+01 -382.204918 311.258621 1.0000000
71-20 -1.629297e+01 -440.950927 408.364985 1.0000000
73-20 -1.129297e+01 -435.950927 413.364985 1.0000000
74-20 -2.432262e+01 -371.054389 322.409150 1.0000000
75-20 -9.269167e+01 -517.349623 331.966290 1.0000000
76-20 -2.581944e+01 -361.541037 309.902148 1.0000000
77-20 -1.636942e+03 -2061.599623 -1212.283710 0.0000000
79-20 4.803639e+02 55.705932 905.021845 0.0021269
80-20 -5.963611e+01 -484.294068 365.021845 1.0000000
81-20 -1.916667e-01 -367.956245 367.572912 1.0000000
82-20 1.310807e+01 -315.830566 342.046711 1.0000000
83-20 -1.025920e+01 -356.990972 336.472567 1.0000000
84-20 -1.696454e+01 -384.729119 350.800037 1.0000000
85-20 1.110463e+01 -335.627140 357.836399 1.0000000
86-20 -6.719167e+01 -491.849623 357.466290 1.0000000
87-20 2.183333e+00 -422.474623 426.841290 1.0000000
88-20 -1.129297e+01 -435.950927 413.364985 1.0000000
89-20 -3.926906e+02 -721.629194 -63.751917 0.0010138
90-20 -3.952500e+01 -464.182956 385.132956 1.0000000
91-20 -5.711421e+02 -917.873871 -224.410332 0.0000060
92-20 1.475000e+00 -423.182956 426.132956 1.0000000
93-20 -3.525000e+00 -428.182956 421.132956 1.0000000
94-20 6.416667e-01 -367.122912 368.406245 1.0000000
95-20 3.891667e+00 -363.872912 371.656245 1.0000000
97-20 8.036389e+01 -344.294068 505.021845 1.0000000
99-20 -1.916667e-01 -424.849623 424.466290 1.0000000
26-22 6.979167e+01 -354.866290 494.449623 1.0000000
27-22 4.025195e+01 -306.479823 386.983716 1.0000000
28-22 5.060000e+01 -374.057956 475.257956 1.0000000
31-22 4.849870e+01 -376.159261 473.156652 1.0000000
33-22 6.939940e+01 -298.365173 437.163983 1.0000000
34-22 5.249870e+01 -372.159261 477.156652 1.0000000
35-22 4.659870e+01 -378.059261 471.256652 1.0000000
38-22 4.650370e+01 -378.154261 471.161652 1.0000000
40-22 -4.783986e+00 -372.548564 362.980593 1.0000000
41-22 4.574583e+01 -322.018745 413.510412 1.0000000
42-22 5.550714e+01 -369.150814 480.165099 1.0000000
44-22 6.114167e+01 -363.516290 485.799623 1.0000000
45-22 6.579167e+01 -358.866290 490.449623 1.0000000
46-22 8.149870e+01 -343.159261 506.156652 1.0000000
47-22 6.261607e+01 -305.148507 430.380650 1.0000000
48-22 9.657899e+00 -337.073871 356.389668 1.0000000
49-22 4.664870e+01 -378.009261 471.306652 1.0000000
51-22 8.771601e+01 -280.048564 455.480593 0.9999997
52-22 5.784935e+01 -309.915230 425.613926 1.0000000
53-22 1.992000e+02 -225.457956 623.857956 0.9034355
54-22 7.209084e+01 -274.640934 418.822605 1.0000000
55-22 -7.661083e+02 -1133.872912 -398.343755 0.0000001
56-22 8.528850e+03 8104.192044 8953.507956 0.0000000
57-22 1.824000e+02 -185.364578 550.164578 0.8447064
60-22 -2.884444e+01 -453.502401 395.813512 1.0000000
61-22 7.256623e+01 -274.165538 419.298001 1.0000000
63-22 9.649870e+01 -328.159261 521.156652 0.9999999
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85-82 -2.003443e+00 -221.295869 217.288983 1.0000000
86-82 -8.029974e+01 -409.238378 248.638899 0.9999994
87-82 -1.092474e+01 -339.863378 318.013899 1.0000000
88-82 -2.440104e+01 -353.339682 304.537595 1.0000000
89-82 -4.057986e+02 -595.711440 -215.885816 0.0000000
90-82 -5.263307e+01 -381.571711 276.305566 1.0000000
91-82 -5.842502e+02 -803.542600 -364.957748 0.0000000
92-82 -1.163307e+01 -340.571711 317.305566 1.0000000
93-82 -1.663307e+01 -345.571711 312.305566 1.0000000
94-82 -1.246641e+01 -263.697441 238.764629 1.0000000
95-82 -9.216406e+00 -260.447441 242.014629 1.0000000
97-82 6.725582e+01 -261.682822 396.194455 1.0000000
99-82 -1.329974e+01 -342.238378 315.638899 1.0000000
84-83 -6.705338e+00 -280.820870 267.410194 1.0000000
85-83 2.136383e+01 -223.812553 266.540218 1.0000000
86-83 -5.693246e+01 -403.664233 289.799306 1.0000000
87-83 1.244254e+01 -334.289233 359.174306 1.0000000
88-83 -1.033768e+00 -347.765538 345.698001 1.0000000
89-83 -3.824314e+02 -601.723778 -163.138927 0.0000022
90-83 -2.926580e+01 -375.997567 317.465972 1.0000000
91-83 -5.608829e+02 -806.059284 -315.706513 0.0000000
92-83 1.173420e+01 -334.997567 358.465972 1.0000000
93-83 6.734203e+00 -339.997567 353.465972 1.0000000
94-83 1.090087e+01 -263.214663 285.016402 1.0000000
95-83 1.415087e+01 -259.964663 288.266402 1.0000000
97-83 9.062309e+01 -256.108678 437.354861 0.9999960
99-83 1.006754e+01 -336.664233 356.799306 1.0000000
85-84 2.806917e+01 -246.046361 302.184703 1.0000000
86-84 -5.022713e+01 -417.991704 317.537453 1.0000000
87-84 1.914787e+01 -348.616704 386.912453 1.0000000
88-84 5.671570e+00 -362.093008 373.436148 1.0000000
89-84 -3.757260e+02 -626.957050 -124.494979 0.0000314
90-84 -2.256046e+01 -390.325037 345.204119 1.0000000
91-84 -5.541776e+02 -828.293093 -280.062028 0.0000001
92-84 1.843954e+01 -349.325037 386.204119 1.0000000
93-84 1.343954e+01 -354.325037 381.204119 1.0000000
94-84 1.760621e+01 -282.672313 317.884728 1.0000000
95-84 2.085621e+01 -279.422313 321.134728 1.0000000
97-84 9.732843e+01 -270.436148 465.093008 0.9999945
99-84 1.677287e+01 -350.991704 384.537453 1.0000000
86-85 -7.829630e+01 -425.028066 268.435473 0.9999999
87-85 -8.921296e+00 -355.653066 337.810473 1.0000000
88-85 -2.239760e+01 -369.129370 324.334169 1.0000000
89-85 -4.037952e+02 -623.087611 -184.502759 0.0000008
90-85 -5.062963e+01 -397.361399 296.102140 1.0000000
91-85 -5.822467e+02 -827.423117 -337.070346 0.0000000
92-85 -9.629630e+00 -356.361399 337.102140 1.0000000
93-85 -1.462963e+01 -361.361399 332.102140 1.0000000
94-85 -1.046296e+01 -284.578495 263.652569 1.0000000
95-85 -7.212963e+00 -281.328495 266.902569 1.0000000
97-85 6.925926e+01 -277.472510 415.991029 1.0000000
99-85 -1.129630e+01 -358.028066 335.435473 1.0000000
87-86 6.937500e+01 -355.282956 494.032956 1.0000000
88-86 5.589870e+01 -368.759261 480.556652 1.0000000
89-86 -3.254989e+02 -654.437527 3.439750 0.0112988
90-86 2.766667e+01 -396.991290 452.324623 1.0000000
91-86 -5.039504e+02 -850.682204 -157.218665 0.0000503
92-86 6.866667e+01 -355.991290 493.324623 1.0000000
93-86 6.366667e+01 -360.991290 488.324623 1.0000000
94-86 6.783333e+01 -299.931245 435.597912 1.0000000
95-86 7.108333e+01 -296.681245 438.847912 1.0000000
97-86 1.475556e+02 -277.102401 572.213512 0.9981131
99-86 6.700000e+01 -357.657956 491.657956 1.0000000
88-87 -1.347630e+01 -438.134261 411.181652 1.0000000
89-87 -3.948739e+02 -723.812527 -65.935250 0.0009374
90-87 -4.170833e+01 -466.366290 382.949623 1.0000000
91-87 -5.733254e+02 -920.057204 -226.593665 0.0000056
92-87 -7.083333e-01 -425.366290 423.949623 1.0000000
93-87 -5.708333e+00 -430.366290 418.949623 1.0000000
94-87 -1.541667e+00 -369.306245 366.222912 1.0000000
95-87 1.708333e+00 -366.056245 369.472912 1.0000000
97-87 7.818056e+01 -346.477401 502.838512 1.0000000
99-87 -2.375000e+00 -427.032956 422.282956 1.0000000
89-88 -3.813976e+02 -710.336223 -52.458946 0.0015213
90-88 -2.823203e+01 -452.889985 396.425927 1.0000000
91-88 -5.598491e+02 -906.580900 -213.117361 0.0000085
92-88 1.276797e+01 -411.889985 437.425927 1.0000000
93-88 7.767971e+00 -416.889985 432.425927 1.0000000
94-88 1.193464e+01 -355.829940 379.699216 1.0000000
95-88 1.518464e+01 -352.579940 382.949216 1.0000000
97-88 9.165686e+01 -333.001097 516.314816 1.0000000
99-88 1.110130e+01 -413.556652 435.759261 1.0000000
90-89 3.531656e+02 24.226917 682.104194 0.0042028
91-89 -1.784515e+02 -397.743972 40.840880 0.0808911
92-89 3.941656e+02 65.226917 723.104194 0.0009616
93-89 3.891656e+02 60.226917 718.104194 0.0011506
94-89 3.933322e+02 142.101187 644.563257 0.0000145
95-89 3.965822e+02 145.351187 647.813257 0.0000126
97-89 4.730544e+02 144.115806 801.993083 0.0000598
99-89 3.924989e+02 63.560250 721.437527 0.0010208
91-90 -5.316171e+02 -878.348871 -184.885332 0.0000207
92-90 4.100000e+01 -383.657956 465.657956 1.0000000
93-90 3.600000e+01 -388.657956 460.657956 1.0000000
94-90 4.016667e+01 -327.597912 407.931245 1.0000000
95-90 4.341667e+01 -324.347912 411.181245 1.0000000
97-90 1.198889e+02 -304.769068 544.546845 0.9999729
99-90 3.933333e+01 -385.324623 463.991290 1.0000000
92-91 5.726171e+02 225.885332 919.348871 0.0000058
93-91 5.676171e+02 220.885332 914.348871 0.0000067
94-91 5.717838e+02 297.668236 845.899300 0.0000001
95-91 5.750338e+02 300.918236 849.149300 0.0000001
97-91 6.515060e+02 304.774221 998.237760 0.0000006
99-91 5.709504e+02 224.218665 917.682204 0.0000061
93-92 -5.000000e+00 -429.657956 419.657956 1.0000000
94-92 -8.333333e-01 -368.597912 366.931245 1.0000000
95-92 2.416667e+00 -365.347912 370.181245 1.0000000
97-92 7.888889e+01 -345.769068 503.546845 1.0000000
99-92 -1.666667e+00 -426.324623 422.991290 1.0000000
94-93 4.166667e+00 -363.597912 371.931245 1.0000000
95-93 7.416667e+00 -360.347912 375.181245 1.0000000
97-93 8.388889e+01 -340.769068 508.546845 1.0000000
99-93 3.333333e+00 -421.324623 427.991290 1.0000000
95-94 3.250000e+00 -297.028521 303.528521 1.0000000
97-94 7.972222e+01 -288.042356 447.486800 1.0000000
99-94 -8.333333e-01 -368.597912 366.931245 1.0000000
97-95 7.647222e+01 -291.292356 444.236800 1.0000000
99-95 -4.083333e+00 -371.847912 363.681245 1.0000000
99-97 -8.055556e+01 -505.213512 344.102401 1.0000000

This Tukey’s HSD test displays the pairwise difference for different values of Age.at.Death. All ages come from the dataset and range from 20 (XXXTentacion) to 99 (Billy Graham).

Some interesting pairwise differences include (rounded to two decimal places):

  • 85-77-1648.05
    • Let’s take any person on this dataset who died at 85-Karl Lagerfeld or Shirley Temple-and anybody who died at 77-Roger Ailes-and analyze how much they have in common. Roger Ailes has more in common with Karl Lagerfeld than with Shirley Temple, as Ailes and Lagerfeld both ran successful businesses.
  • 69-65-914.65
    • Granted, either of the two people on this dataset who died at 69-David Bowie and Alan Rickman-wouldn’t have much in common with Craig Sager (the only person in this dataset who died at 65). Well, the only thing that connects all three of these people is that all died from cancer.
  • 22-20-(-65.79)
    • This isn’t a huge difference, but still interesting since the people in this dataset who died at 22 and 20 are Christina Grimmie and XXXTentacion, respectively. Both were young singers who were sadly victims of gun violence. Both also happened to die in Florida in the month of June.
  • 56-45-8463.06
    • This is a considerably large difference which is made even more interesting given that the two people in this dataset who died at these ages-Steve Jobs at 56 and Phife Dawg at 45-have almost nothing in common..

Thanks for reading,

Michael