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__Data details__

**The number of movies Bill Murray appeared in**

**Source:**The Movie DB

**Additional Info:**Groundhog Day (1993); Lost in Translation (2003); Broken Flowers (2005); The Life Aquatic with Steve Zissou (2004); Ghostbusters (1984); What About Bob? (1991); Ghostbusters II (1989); Quick Change (1990); Stripes (1981); The Razor's Edge (1984); Garfield (2004); The Man Who Knew Too Little (1997); Scrooged (1988); Where the Buffalo Roam (1980); Larger Than Life (1996); Meatballs (1979); Get Low (2010); Loose Shoes (1978); Hyde Park on Hudson (2012); With Friends Like These... (1998); St. Vincent (2014); Saturday Night Live: 25th Anniversary Special (1999); Rock the Kasbah (2015); A Very Murray Christmas (2015); The Dogs (1978); The Dead Don't Die (2019); The Bill Murray Stories: Life Lessons Learned from a Mythical Man (2018); Cut Flowers (1981); Loopers: The Caddie's Long Walk (2019); Fantastic Mr. Murray (2019); For The Fun Of The Game (2018); New Worlds, The Cradle of Civilization (2021); Ballhawks (2010); Bill Murray: The Kennedy Center Mark Twain Prize (2016); TVTV: Video Revolutionaries (2018); Super Bowl (1976); Bill Murray Live from the Second City (1980); Garfield: Bringing the Cat to Life (2004); Rushmore (1998); The Jungle Book (2016); This Is an Adventure (2005); Live from New York: The First 5 Years of Saturday Night Live (2005); On the Rocks (2020); On the Set: 'The Life Aquatic with Steve Zissou' (2005); The Things We Did Last Summer (1978); Mad Dog and Glory (1993); The Limits of Control (2009); City of Ember (2008); The Rodney Dangerfield Show: It's Not Easy Bein' Me (1982); Speaking of Sex (2001); Passion Play (2011); A Glimpse Inside the Mind of Charles Swan III (2013); The Monuments Men (2014); Steve Martin's Best Show Ever (1981); Moonrise Kingdom: Welcome to the Island of New Penzance (2012); The Making of 'Rushmore' (2000); The Best of John Belushi (1985); Exploring the Set of 'Moonrise Kingdom' (2015); Fantastic Mr. Fox (2009); Charlie's Angels (2000); Kingpin (1996); Caddyshack (1980); Garfield: A Tail of Two Kitties (2006); Aloha (2015); Thank You, Del: The Story of the Del Close Marathon (2015); Drunk Stoned Brilliant Dead: The Story of the National Lampoon (2015); Michael Jordan to the Max (2000); The Bill Murray Experience (2017); FCU: Fact Checkers Unit (2007); The Greatest Beer Run Ever (2022); Ghostbusters 1999 (1999); Hamlet (2000); Osmosis Jones (2001); Moonrise Kingdom (2012); Isle of Dogs (2018); Fear and Loathing on the Road to Hollywood (1978); Spirit of Golf (2023); Ed Wood (1994); Wild Things (1998); Tootsie (1982); Zombieland (2009); Nothing Lasts Forever (1984); Lost on Location: Behind the Scenes of 'Lost in Translation' (2004); Matthew Gray Gubler's Life Aquatic Intern Journal (2005); A Better Man: The Making of 'Tootsie' (2008); This Old Cub (2004); R.E.M.: Rough Cut (1995); Groundhog Day: The Weight of Time (2002); Get Smart (2008); The Royal Tenenbaums (2001); The Lost City (2005); Buy the Ticket, Take the Ride (2006); The Making of 'The Darjeeling Limited' (2010); Making The Royal Tenenbaums (2012); Ghostbusters: Afterlife (2021); Ant-Man and the Wasp: Quantumania (2023); Twilight Theatre (1982); Space Jam (1996); Cradle Will Rock (1999); '85: The Greatest Team in Pro Football History (2016); Caddyshack: The 19th Hole (1999); With the Filmmaker: Wes Anderson (2001); The Darjeeling Limited (2007); The Rutles: All You Need Is Cash (1978); The Missing Link (1980); Tarzoon: Shame of the Jungle! (1975); The Grand Budapest Hotel (2014); Mr. Mike's Mondo Video (1979); The French Dispatch (2021); Little Shop of Horrors (1986); David Letterman: The Kennedy Center Mark Twain Prize (2017); Live at Mister Kelly's (2021); Love, Gilda (2018); Bugs Bunny/Looney Tunes All-Star 50th Anniversary (1986); Studio 54 (2018); Saturday Night Live: The Best of Dan Aykroyd (2005); Dumb and Dumber To (2014); TVTV Looks at the Oscars (1976); "That's Awesome!": The Story of 'Dumb and Dumber To' (2015); Cleanin' Up the Town: Remembering Ghostbusters (2020); Coffee and Cigarettes (2004); Ghostbusters (2016); Zombieland: Double Tap (2019); Next Stop, Greenwich Village (1976); Picture Show: A Tribute Celebrating John Prine (2020); The Rutles 2: Can't Buy Me Lunch (2003); Saturday Night Live: 15th Anniversary (1989); Belushi (2020); Always at The Carlyle (2018); She's Having a Baby (1988); The Women of SNL (2010); Saturday Night Live: 40th Anniversary Special (2015); Final Cut: Ladies and Gentlemen (2012)

*See what else correlates with*

**The number of movies Bill Murray appeared in****Violent crime rates**

**Detailed data title:**The violent crime rate per 100,000 residents in United States

**Source:**FBI Criminal Justice Information Services

*See what else correlates with*

**Violent crime rates****Correlation r = -0.5384513**(Pearson correlation coefficient)

Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.

**r**(Coefficient of determination)

^{2}= 0.2899298This means

**29%**of the change in the one variable

*(i.e., Violent crime rates)*is predictable based on the change in the other

*(i.e., The number of movies Bill Murray appeared in)*over the 38 years from 1985 through 2022.

**p < 0.01,**which is statistically significant(Null hypothesis significance test)

The

*p*-value is 0.00049. 0.0004879978758469051000000000

The

*p*-value is a measure of how probable it is that we would randomly find a result this extreme. More specifically the

*p*-value is a measure of how probable it is that we would randomly find a result this extreme

**if we had only tested one pair of variables one time**.

But I am a p-villain. I absolutely did

**not**test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me?

*p*-value reporting doesn't require me to report how many calculations I had to go through in order to find a low

*p*-value!

On average, you will find a correaltion as strong as -0.54 in 0.049% of random cases. Said differently, if you correlated 2,049 random variables Which I absolutely did.

with the same 37 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is

**37**because we have two variables measured over a period of

**38 years**. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to

**the number of years minus one**.

you would randomly expect to find a correlation as strong as this one.

**[ -0.73, -0.26 ] 95% correlation confidence interval**(using the Fisher z-transformation)

The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

1985 | 1986 | 1987 | 1988 | 1989 | 1990 | 1991 | 1992 | 1993 | 1994 | 1995 | 1996 | 1997 | 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | |

The number of movies Bill Murray appeared in (Movie appearances) | 1 | 2 | 0 | 2 | 2 | 1 | 1 | 0 | 2 | 1 | 1 | 3 | 1 | 3 | 4 | 4 | 4 | 1 | 2 | 6 | 7 | 2 | 2 | 3 | 3 | 4 | 1 | 5 | 1 | 4 | 8 | 4 | 2 | 7 | 4 | 4 | 4 | 1 |

Violent crime rates (Violent crime) | 558.1 | 620.1 | 612.5 | 640.6 | 666.9 | 729.6 | 758.2 | 757.7 | 747.1 | 713.6 | 684.5 | 636.6 | 611 | 567.6 | 523 | 506.5 | 504.5 | 494.4 | 475.8 | 463.2 | 469 | 479.3 | 471.8 | 458.6 | 431.9 | 404.5 | 387.1 | 387.8 | 369.1 | 361.6 | 373.7 | 397.5 | 394.9 | 383.4 | 380.8 | 398.5 | 387 | 380.7 |

__Why this works__

**Data dredging:**I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.**Lack of causal connection:**There is probably Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.

no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.**Observations not independent:**For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly*change*how they are doing that thing on January 1. A simple Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.

*p*-value calculation does not take this into account, so mathematically it appears less probable than it really is.**Y-axis doesn't start at zero:**I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the*points*on the graph, not the*lines*on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!

Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.**Inverted Y-axis:**I inverted the Y-axis on the chart above so that the lines would move together. This is visually pleasing, but not at all intuitive. Below is a line graph that does not invert the Y-axis.

__Try it yourself__

You can calculate the values on this page on your own! Try running the Python code to see the calculation results.
**Step 1:**Download and install Python on your computer.

**Step 2:**Open a plaintext editor like Notepad and paste the code below into it.

**Step 3:**Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

**Step 4:**Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

**Step 5:**Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

**Step 6:**Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

**Step 7:**Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

*"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."*

```
# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats
# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):
# Calculate Pearson correlation coefficient and p-value
correlation, p_value = stats.pearsonr(array1, array2)
# Calculate R-squared as the square of the correlation coefficient
r_squared = correlation**2
return correlation, r_squared, p_value
# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([1,2,0,2,2,1,1,0,2,1,1,3,1,3,4,4,4,1,2,6,7,2,2,3,3,4,1,5,1,4,8,4,2,7,4,4,4,1,])
array_2 = np.array([558.1,620.1,612.5,640.6,666.9,729.6,758.2,757.7,747.1,713.6,684.5,636.6,611,567.6,523,506.5,504.5,494.4,475.8,463.2,469,479.3,471.8,458.6,431.9,404.5,387.1,387.8,369.1,361.6,373.7,397.5,394.9,383.4,380.8,398.5,387,380.7,])
array_1_name = "The number of movies Bill Murray appeared in"
array_2_name = "Violent crime rates"
# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)
# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)
```

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**Download images for these variables:**

- High resolution line chart
The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up
*beyond the size of the observable universe*without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.

If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.

Alternatively you can use a tool like Inkscape. - High resolution line chart, optimized for mobile
- Alternative high resolution line chart
- Scatterplot
- Portable line chart (png)
- Portable line chart (png), optimized for mobile
- Line chart for only
*The number of movies Bill Murray appeared in* - Line chart for only
*Violent crime rates*

**How fun was this correlation?**

Your correlation rating is out of this world!

Correlation ID: 12512 · Black Variable ID: 26506 · Red Variable ID: 20220