in five years, youtube has completely reshaped the internet, media, and political landscapes

http://www.fastcompany.com/magazine/142/it-had-to-be-you.html

The History of YouTube

In five years, YouTube has completely reshaped the Internet, media, and political landscapes.

• February 14, 2005- Chad Hurley, Steve Chen, and Jawed Karim begin work on a "Flickr or HotorNot for video." They register youtube.com the next day.

• April 23, 2005- "Me at the Zoo,” 19 seconds of Karim in front of the elephants at the San Diego Zoo, is the first video posted to the site.

• December 15, 2005- YouTube officially debuts.• October 9, 2006- Google buys YouTube for $1.65 billion.• October 12, 2009- YouTube passes 1 billion videos a day but

remains unprofitable.

YouTube Video Categories

• Autos & Vehicles

• Comedy• Education• Entertainment• Film &

Animation

• Gaming• Howto & Style• Music• News &

Politics• Nonprofits &

Activism

• People & Blogs• Pets & Animals• Science &

Technology• Sports• Travel & Events

• What we analyzed:– 30 random videos from each of the 15 YouTube video categories (every third video)– Length of video– Number of views– Number of comments

• How we collected data:– Split up the categories among the three of us- 5

categories each– Recorded the above mentioned variables of every

third video in the assigned category

Observational Study

Linear Regression T-testLength of Video vs. Number of Views

0

20

40

60

80

100

120

140

160

180

200

220

Length_of_Video (thousands)

0 2 4 6 8 10

Number_of_View s = -550Length_of_Video + 3.5e+06; r2 = 0.00025

Collection 1 Scatter Plot

r=0.0158

Linear Regression T-testLength of Video vs. Number of Views Residual Plot

0

20

40

60

80

100

120

140

160

180

200

220


0 2 4 6 8 10

Number_of_View s = -550Length_of_Video + 3.5e+06; r2 = 0.00025

0

50

100

150

200

0 2 4 6 8 10



r=0.0158

• Negative• Linear• Very Weak• Scattered Residual Plot• Correlation (r)= 0.01581• Variance (r2)= 0.00025• .025% of the change in the number of views is due to the change in the

length of the video• Overall, for our population of YouTube viewers, as the length of the

video increases, the number of views does slightly decrease; however, our data is not sufficient enough to show a strong enough relationship between the two variables. Our variance was so small that we could not determine any true relationship between the two.

Linear Regression T-testExploratory Data Analysis

Assumptions• 2 independent SRS• True relationship is linear

Checks• Check• Assumed

Linear Regression T-testAssumptions and Checks

Ho: β= 0 Ha: β< 0

Linear Regression T-testLength of Video vs. Number of Views

3315.0bSE

bt

37.04483315.0 dftPWe fail to reject Ho because P-value is > α=0.05.We have sufficient evidence that the slope of the population regression line of length of video versus number of views is 0. Thus, as the length of video increases, the number of views is not affected.

Linear Regression T-testNumber of Views vs. Number of Comments

0

100

200

300

400

500

600

Number_of_Views (millions)

0 20 40 60 80 100 120 140 160 180 200 220

Number_of_Comments = 0.00183Number_of_View s + 40; r2 = 0.73


r= 0.8544

Linear Regression T-testNumber of Views vs. Number of Comments Residual Plot

0

100

200

300

400

500

600


0 20 40 60 80 100 120 140 160 180 200 220

Number_of_Comments = 0.00183Number_of_View s + 40; r2 = 0.73

-100

0

100

200

300


0 20 40 60 80 100 120 140 160 180 200 220


r= 0.8544

• Positive• Linear• Moderately Strong• Scattered Residual Plot• Correlation (r)= 0.8544• Variance (r2)=0.73• 73% of the change in the number of comments is due to the

change in the number of views• Overall, for our population of YouTube viewers, as the number

of views increases, the number of comments will also increase. Thus, as more people continue to view the video, the number of comments will go up.

Linear Regression T-testExploratory Data Analysis

Assumptions• 2 independent SRS• True relationship is linear

Checks• Check• Assumed

Linear Regression T-testAssumptions and Checks

Ho: β= 0 Ha: β> 0

Linear Regression T-testNumber of Views vs. Number of Comments

66.34bSE

bt

0001.044866.34 dftPWe reject Ho because P-value is < α=0.05.We have sufficient evidence that the slope of the population regression line of number of views versus number of comments is greater than 0. Thus, as the number of views increases, the number of comments also increases.

Calculated MeansAverage Number of Views in Each Category

Category: Means:Autos and Vehicles

ComedyEducation

EntertainmentFilm and Animation

GamingHowto and Style

MusicNews and Politics

Nonprofits and ActivismPeople and BlogsPets and Animals

Science and TechnologySports

Travel and Events

15189.740503.75407.6711304133559.476944.527768.94935040032863.111691.824751.454006.58613.771095382032.5

Autos and VehiclesComedy

EducationEntertainment

Film and AnimationGaming

Howto and StyleMusic

News and PoliticsNonprofits and Activism

People and BlogsPets and Animals


Travel and Events

0

10000000

20000000

30000000

40000000

50000000

60000000

Series1

Number of Views

Cate

gory

Calculated MeansAverage Number of Views in Each Category




Howto and StyleNews and Politics



Travel and Events

0 20000 40000 60000 80000 100000 120000

Series1

Number of Views

Cate

gory

• Music had the most extreme average number of views of all of the categories at 49,350,400. Sports, Entertainment and Gaming had the next highest average number of views respectively, although none of them came close to Music.

• Thus, the majority of YouTube viewers use it for Music. • Travel and Events, Education and Nonprofits and

Activism had the least average number of views respectively. This is most likely due to the fact that people use YouTube for entertainment purposes.

Calculated MeansExploratory Data Analysis

1-Sample T-intervalAverage Number of Views in Each Category

Assumptions: Checks:• Check• 30=30

• SRS• Normal Population

orn≥30

...

n

st

1-Sample T-intervalAverage Number of Views in Each Category

Category:We are 95% confident that the true mean number

of views for the following categories are:




Howto and StyleMusic




Travel and Events

(3000.53, 27378.9)(-386.52, 81393.9)(96.4158, 10718.9)(14718.7, 211363)(5052.9, 62065.9)(51236, 102653)(10583.1, 44954.7)(3.04757 x 107, 6.82251 x 107)(21382.2, 44344)(5045.54, 18388.1)(10304.3, 39198.5)(-5397.94, 113411)(3529.7, 13697.8)(-58631.4, 277707)(970.212, 3094.79)

1-Sample T-intervalDifference Between Max and Min Intervals of Number of Views in Each

Category

Autos and VehicalsComedy

EducationEntertainmnet


How To and SytleNews and Politics



Travel and Events

0 50000 100000 150000 200000 250000 300000 350000 400000

Series1

Number of Views

Cate

gory

Autos and VehicalsComedy

EducationEntertainmnet


How To and SytleMusic




Travel and Events

0 20000000 40000000 60000000 80000000

Series1

Number of Views

Cate

gory

• Music had the largest difference between max and min of the interval at 68,225,100, which is why we removed it from the rest of the data in order to get a better view of the other categories.

• Travel and Events had the smallest difference between max and min of the interval at 2,124.578.

• We think that Music had the largest interval because the standard deviation was so large at 50,547,300 due to several outliers in the hundred million views.

• Following Music was Sports and then Entertainment. We think these intervals were large for the same reasons as Music because there were a few data points that were outliers.

• The smaller intervals following Travel and Events included Science and Technology, Nonprofits and Activism and Education.

1-Sample T-intervalExploratory Data Analysis

Application to Population• For the population of YouTube viewers, as the length of the video

increases, the number of views does slightly decrease; however, our data is not sufficient enough to show a strong enough relationship between the two variables. Thus, the length of the video will not deter the average viewer.

• For the population of YouTube viewers, as the number of views increases, the number of comments will also increase. Thus, as more people continue to view the video, the more likely it is that people will comment.

• The extreme variation in average number of views is most likely due to the fact that people use YouTube for entertainment purposes.

• YouTube is a social site, so popularity and recognition will affect whether a person watches the video or not.

Bias and Error• Even if you do not watch the video all of the way through it will register

as a view.• There are many videos on YouTube all dispersed within the given

categories. Because they must fit in one, some videos may be more loosely grounded in the topic. – Some of the categories leave less room for variation, such as news

and politics, while others, like comedy, could encompass many different types of video.

• Because there is a variation in amount of videos in each category, in those with less videos there is less variation in the videos that we choose.

• There are some videos that do not have much appeal, whether because of subject matter or other better versions in existence, because of this they are not going to get views regardless of time.

• There were some videos that disabled comments, which we counted as zero views. Perhaps they would have received comments if allowed.

• There are some strong outliers, like the hour and a half long video.

Personal Opinions• Overall, the length of the video did not affect the number

of views. This surprised us because we expected that people would not want to wait to watch a longer video.

• The margin of error for the mean number of views for Music was surprisingly extreme. We attributed this to Justin Bieber because his three videos included in the data had enormous numbers of views.

• We were not surprised that the less “entertaining” categories had a lesser number of views because YouTube is a social site, so popularity and recognition will affect whether a person watches the video or not.

in five years, youtube has completely reshaped the internet, media, and political landscapes

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