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Views

Views. 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.

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Views

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  1. Views

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

  3. 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

  4. Observational Study • 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

  5. Linear Regression T-test Length of Video vs. Number of Views r=0.0158

  6. Linear Regression T-test Length of Video vs. Number of Views Residual Plot r=0.0158

  7. Linear Regression T-test Exploratory Data Analysis • 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.

  8. Linear Regression T-test Assumptions and Checks Assumptions Checks Check Assumed • 2 independent SRS • True relationship is linear

  9. Linear Regression T-test Length of Video vs. Number of Views Ho: β= 0 Ha: β< 0 We 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.

  10. Linear Regression T-test Number of Views vs. Number of Comments r= 0.8544

  11. Linear Regression T-test Number of Views vs. Number of Comments Residual Plot r= 0.8544

  12. Linear Regression T-test Exploratory Data Analysis • 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.

  13. Linear Regression T-test Assumptions and Checks Assumptions Checks Check Assumed • 2 independent SRS • True relationship is linear

  14. Linear Regression T-test Number of Views vs. Number of Comments Ho: β= 0 Ha: β> 0 We 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.

  15. Calculated Means Average Number of Views in Each Category Category: Means: Autos and Vehicles Comedy Education Entertainment Film and Animation Gaming Howto and Style Music News and Politics Nonprofits and Activism People and Blogs Pets and Animals Science and Technology Sports Travel and Events • 15189.7 • 40503.7 • 5407.67 • 113041 • 33559.4 • 76944.5 • 27768.9 • 49350400 • 32863.1 • 11691.8 • 24751.4 • 54006.5 • 8613.77 • 109538 • 2032.5

  16. Calculated Means Average Number of Views in Each Category

  17. Calculated Means Exploratory Data Analysis • 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.

  18. 1-Sample T-interval Average Number of Views in Each Category Assumptions: Checks: Check 30=30 • SRS • Normal Populationorn≥30

  19. 1-Sample T-interval Average Number of Views in Each Category Category: We are 95% confident that the true mean number of views for the following categories are: Autos and Vehicles Comedy Education Entertainment Film and Animation Gaming Howto and Style Music News and Politics Nonprofits and Activism People and Blogs Pets and Animals Science and Technology Sports 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)

  20. 1-Sample T-interval Difference Between Max and Min Intervals of Number of Views in Each Category

  21. 1-Sample T-interval Exploratory Data Analysis • 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.

  22. 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.

  23. 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.

  24. 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.

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