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歌手Youtube點擊量迴歸分析
450810201 廖婉丞
1
大綱
一.研究動機與目的
二.資料來源
三.自變數說明
四.Spearman相關係數
五.試一階模型
六.Box-cox轉換
七.轉換後試一階模型
2
八.模型選擇
九.模型比較
十.選定一階模型
十一.追加變數圖
十?.多重共線性診斷
十?.交互作用項
十?.離群值檢測
十?.目前結論
一、研究動機與目的
• 探討近年來,對歌手來說,長相、音樂專業與網路社群等,到底
哪個因素和youtube影片點擊量影響最深?
• 偶像都靠臉吃飯?
• 發EP比發專輯省錢又有效促進知名度?
3
二、資料來源
• 歌手名單
• 2011-2014年美國billboard排行榜周冠軍
• 2011-2014年韓國melon排行榜周冠軍
• 其餘資料→上網蒐集&整理
• 樣本數:83位歌手
4
三、自變數說明
• Youtube(Y):歌手youtube官方影片點擊量平均
• Sex(x1):性別(男性與女性)
• Country(x2):國家(西方國家與東方國家)
• Type(x3)類型(solo歌手或團體歌手)
• Face(x4):臉的長寬比(臉長/臉寬)
• Eye(x5):指眼睛面積佔臉面積的比例,單位為%
• Album(x6):專輯數
• EP(x7):單曲數
• Filmography(x8):指歌手參與電影或電視劇演出次數
• Facebook(x9):歌手臉書粉絲量 5
三、自變數說明
• Youtube(Y) • Sex(x1)
6
sex
sex 次數 百分
比
累計
次數
累計
百分
比
女性 32 38.55 32 38.55
男性 51 61.45 83 100.0
0
三、自變數說明
• Country(x2) • Type(x3)
7
country
countr
y
次數 百分比 累計
次數
累計
百分比
west 47 56.63 47 56.63
east 36 43.37 83 100.00
type
type 次數 百分比 累計
次數
累計
百分比
solo 58 69.88 58 69.88
group 25 30.12 83 100.00
三、自變數說明
• Face(X4) • Eye(X5)
8
三、自變數說明
• Album(X6)
• EP(x7)
• Filmography(X8)
• Facebook(X9)
9
四、Spearman相關係數
10
五、試一階模型
• 變異數分析
• Adj R-sq
• 參數估計值
11
五、試一階模型-殘差常態檢定
• Shapiro-wilk檢定
• Kolmogorov-smirnov檢定
12
六、Box-cox轉換
• 取λ=0.25
• 也就是將youtube(歌手
youtube官方影片點擊量平均)
開根號兩次4
𝑦𝑜𝑢𝑡𝑢𝑏𝑒
13
七、轉換後試一階模型-相關係數
14
Spearman 相關係數, N = 83
Prob > |r| (位於 H0 底下): Rho=0
y2 male country1 type1 face eye album ep filmography facebook
y2 1 -0.24487 0.58143 0.17209 -0.43146 0.29417 0.13588 -0.21558 0.19843 0.70824
0.0257 <.0001 0.1198 <.0001 0.0069 0.2206 0.0503 0.0721 <.0001
male -0.24487 1 0.05597 0.07346 0.03772 -0.22424 -0.15312 -0.05263 -0.4385 -0.18701
0.0257 0.6153 0.5093 0.735 0.0416 0.167 0.6365 <.0001 0.0905
country1 0.58143 0.05597 1 0.43222 -0.46837 0.34302 0.01122 -0.36695 0.01536 0.69102
<.0001 0.6153 <.0001 <.0001 0.0015 0.9198 0.0006 0.8904 <.0001
type1 0.17209 0.07346 0.43222 1 -0.16445 -0.05481 -0.18283 -0.16419 -0.04177 0.27732
0.1198 0.5093 <.0001 0.1374 0.6226 0.0981 0.138 0.7077 0.0111
face -0.43146 0.03772 -0.46837 -0.16445 1 -0.15336 0.09744 0.38439 0.04999 -0.27219
face <.0001 0.735 <.0001 0.1374 0.1663 0.3809 0.0003 0.6536 0.0128
eye 0.29417 -0.22424 0.34302 -0.05481 -0.15336 1 -0.07814 -0.04123 0.24146 0.27632
eye 0.0069 0.0416 0.0015 0.6226 0.1663 0.4826 0.7113 0.0279 0.0115
album 0.13588 -0.15312 0.01122 -0.18283 0.09744 -0.07814 1 0.19067 0.41026 0.33101
album 0.2206 0.167 0.9198 0.0981 0.3809 0.4826 0.0842 0.0001 0.0022
ep -0.21558 -0.05263 -0.36695 -0.16419 0.38439 -0.04123 0.19067 1 0.07205 -0.1381
ep 0.0503 0.6365 0.0006 0.138 0.0003 0.7113 0.0842 0.5174 0.2131
filmography 0.19843 -0.4385 0.01536 -0.04177 0.04999 0.24146 0.41026 0.07205 1 0.33443
filmography 0.0721 <.0001 0.8904 0.7077 0.6536 0.0279 0.0001 0.5174 0.002
facebook 0.70824 -0.18701 0.69102 0.27732 -0.27219 0.27632 0.33101 -0.1381 0.33443 1
facebook <.0001 0.0905 <.0001 0.0111 0.0128 0.0115 0.0022 0.2131 0.002
七、轉換後試一階模型
• 變異數分析
• Adj R-sq
• 參數估計值
15
七、轉換後試一階模型-殘差分析
16
八、模型選擇-Cp
• 最佳組合出現在P=9
17
八、模型選擇-R平方
• 最大值出現在P=10
18
八、模型選擇-調整R平方
• 最大值出現在P=5
19
八、模型選擇-AIC
• 最小值出現在P=5
20
八、模型選擇-SBC
• 最小值出現在P=5
21
八、模型選擇-逐步、前進、向後
22
八、模型選擇
• 模型1:包含male country1 type1 face eye ep filmography
facebook
• 模型2:包含male country1 type1 face eye album
filmography facebook
• 模型3:包含male country1 face facebook
• 模型4:全部參數都納入
23
八、模型選擇-是否可以剔除EP、或Album?
• 𝐻0:𝛽 𝐸𝑃=0
• 𝐻0:𝛽 𝑎𝑙𝑏𝑢𝑚=0
24
九、模型比較
• 模型1:包含male country1 type1 face eye ep filmography
facebook
• 模型2:包含male country1 type1 face eye album
filmography facebook
• 模型3:包含male country1 face facebook
25
九、模型比較
• type1的迴歸係數正負號改變
• eye的迴歸係數正負號改變
• 比較調整R平方
• 比較AIC
• 比較SBC
• 結論:選擇模型3
26
模型1 模型1 模型2 模型2 模型3 模型3
train test train test train test
截距 61.1545 102.6093 62.13779 103.92157 64.34901 98.18795
s{截距} 28.97648 29.11862 28.52613 28.8393 26.54824 27.43707
male -11.1007 -4.495 -11.20379 -4.51743 -11.8067 -4.84387
s{male} 4.98173 5.26311 4.89596 5.09356 4.33397 4.59911
country1 4.40365 18.82204 5.87853 19.76296 4.92039 12.86947
s{country1} 6.04233 7.81769 6.15942 7.56773 5.22618 6.09126
type1 0.4629 -13.5211 0.67517 -13.86753
s{type1} 5.33156 6.92983 5.1346 6.67633
face -8.30164 -23.1668 -10.15466 -24.00883 -7.58055 -28.7596
s{face} 18.10418 18.84506 17.99498 17.62156 17.01622 16.91405
eye 0.72375 -1.76E+00 7.71E-01 -1.96E+00
s{eye} 1.74552 2.07E+00 1.71E+00 1.95E+00
album 0.25524 0.05829
s{album} 0.69287 0.86924
ep 0.47285 0.23956
s{ep} 0.50292 0.71625
filmography -0.02212 -0.07034 0.02284 -0.07262
s{filmography} 0.19259 0.21583 0.18192 0.18559
facebook 5.72E-07 2.52E-07 5.54E-07 2.50E-07 5.77E-07 2.38E-07
s{facebook} 1.09E-07 1.32E-07 1.10E-07 1.25E-07 1.02E-07 1.19E-07
R平方 0.6865 0.5642 0.6937 0.5657 0.6839 0.5086
調整R平方 0.6082 0.4586 0.6171 0.4604 0.6488 0.4554
Cp 9 9 9 9 5 5
AIC 220.7935 229.4248 219.8498 229.2884 213.1337 226.4761
SBC 236.2157 245.0638 235.2719 244.92739 221.7015 235.1644
MSPR 280.2861 290.75513 281.1316
十、選定一階模型
• 𝟒
𝒚𝒐𝒖𝒕𝒖𝒃𝒆=90.11919-7.65830*male+9.25493*country1-
24.22813*face+4.171587E-7*facebook
• Male:性別
• Country1:國家
• Face:臉長/臉寬
• Facebook:臉書粉絲量
27
十、選定一階模型-殘差常態檢定
• Shapiro-wilk檢定
• Kolmogorov-smirnov檢定
28
十一、追加變數圖
• 探討eye這個參數是否需要加入模型中?
• 變異數分析
• 殘差
29
十?-多重共線性診斷
• 多重共線性診斷
• 變異數膨脹因子
30
十?-交互作用項
• Malecoun:性別&國籍
• Malefb:性別&臉書粉絲量
• Facefb:臉的長寬比&臉書粉絲量
31
十?-離群值檢測
• 槓桿作用
• ID=25
32
目前結論
• 目前一階模型
• 𝟒
𝒚𝒐𝒖𝒕𝒖𝒃𝒆=90.11919-
7.65830*male+9.25493*count
ry1-
24.22813*face+4.171587E-
7*facebook
1. 性別
2. 國家
3. 臉的長寬比
4. 臉書粉絲量
• 交互作用項:
• P-value<0.05的有
→性別&臉的長寬比
→國家&臉的長寬比
→國家&臉書粉絲量
33
謝謝大家
34

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歌手Youtube點擊量迴歸分析

Editor's Notes

  1. 平均值為24405618.82
  2. 平均值為1.507 平均值為3.45
  3. 平均值為6 平均值為3.964 平均值為8.482 平均值為17324799.08
  4. 由spearman相關係數可知: p-value<0.05的有 male(男性=1)、country1(西方國家=1)、face(臉的長寬比)、eye(眼睛面積佔臉的面積比)、facebook(臉書粉絲量)。