本次美国代考主要为统计相关的限时测试
1. Facebook：在这个例子中，我们调查了在 Facebook 上发布的帖子的性能指标
页。使用了从化妆品公司页面中提取的不同帖子的绩效指标
模拟帖子上的点赞数。有关此问题的有用输出，请参见图 (1)。全部
下面详细列出了变量。
响应变量（y）：喜欢！出版物上的“赞”数。
解释变量 (x1)：终生帖子总展示次数（1000 秒）！印象是
页面上的帖子显示的次数，无论该帖子是否被点击。人们可能
查看同一帖子的多次展示。例如，有人可能会在
如果朋友分享它，新闻提要一次，然后第二次。单位为 1000 秒。
解释变量 (x2)：终生后总覆盖率（1000 秒）！的人数
看到一个页面帖子（唯一身份用户）。单位为 1000 秒。
解释变量 (x3)：终身参与的用户！参与的用户数量。
解释变量 (x4)：终生后消费者！点击的人数
在帖子中的任何位置。
解释变量 (x5)：终生后消费！任何地方的点击次数
在一个帖子中。
我们讨论了几种用于检测多重共线性指标的工具（程序）。为了
部分 (1a, 1b, 1c) 阅读下面的每个描述。圈出所有表明存在问题的变量（如果有）
估计模型中的多重共线性与提供的描述一致。
(a) 在估计模型中观察大的方差因子（即 > 10）。
终身帖子总覆盖面 终身帖子总印象数 终身参与用户
终生后消费 终生后消费 无
(b) 与解释变量的估计斜率的符号相反的符号
该解释变量与响应变量之间的相关性。这里关心的是
与原始相关性中等或强（即 jrj > 0:3）时的不一致有关。
终身帖子总覆盖面 终身帖子总印象数 终身参与用户
终生后消费 终生后消费 无
(c) 观察解释变量之间的强相关性（即 jrj > 0:7）。
终身帖子总覆盖面 终身帖子总印象数 终身参与用户
终生后消费 终生后消费 无
1. Facebook: In this example we investigate the performance metrics for posts published on Facebook
pages. Di erent posts’ performance metrics extracted from a cosmetic company’s page were used
to model the number of Likes on a post. See Figures (1) for output helpful for this problem. All
variables are listed in detail below.
Response Variable (y): Likes ! Number of \Likes” on the publication.
Explanatory Variable (x1): Lifetime post total impressions (1000s) ! Impressions are the
number of times a post from a page is displayed, whether the post is clicked or not. People may
see multiple impressions of the same post. For example, someone might see a Page update in
News Feed once and then a second time if a friend shares it. Units are in 1000s.
Explanatory Variable (x2): Lifetime post total reach (1000s) ! The number of people who
saw a page post (unique users). Units are in 1000s.
Explanatory Variable (x3): Lifetime Engaged Users ! The number of engaged users.
Explanatory Variable (x4): Lifetime Post Consumers ! The number of people who clicked
anywhere in a post.
Explanatory Variable (x5): Lifetime Post Consumptions !The number of clicks anywhere
in a post.
We discussed several tools (procedures) that are used to detect indicators of multicollinearity. For
parts (1a, 1b, 1c) read each description below. Circle all variables (if any) which suggest a problem
with multicollinearity in the estimated model that is consistent with the provided description.
(a) Observing large Variance In ation Factors (i.e. > 10) in an estimated model.
Lifetime Post Total Reach Lifetime Post Total Impressions Lifetime Engaged Users
Lifetime Post Consumers Lifetime Post Consumptions None
(b) An opposite sign of the estimated slope for an explanatory variable compared to the sign of the
correlation between this explanatory variable and the response variable. Here the concern is
related to inconsistencies when the original correlation is moderate or strong (i.e. jrj > 0:3).
Lifetime Post Total Reach Lifetime Post Total Impressions Lifetime Engaged Users
Lifetime Post Consumers Lifetime Post Consumptions None
(c) Observing strong correlations (i.e. jrj > 0:7) between explanatory variables.
Lifetime Post Total Reach Lifetime Post Total Impressions Lifetime Engaged Users
Lifetime Post Consumers Lifetime Post Consumptions None