初探 Internet Component Download

這幾天都在研究 "Internet Component Download” 的資料，以下是一些網上資料的整理 ：

MSDN offical defination

Internet Component Download is a system service for downloading and installing software from Web sites on the Internet and intranets. This service also provides certificate checking. Internet Component Download is supported by Microsoft Internet Explorer 3.0 and later.
You, the software provider or Web master, can "package" components and place them on your Web servers for download. When users visit your Web site, Internet Component Download enables their browsers to pull down and install the programs.

Key Concept

There is some key concepts for this type of software distribution method:

• Carbinet File (.cab) – a package that include all files for the software distribution (e.g.active X controls (.ocx), dll, exe, etc.).   (詳細要怎樣打包我不懂，本身我是JAVA人，m記的東西本來就不太懂，因為工作關係我才接觸這topic)
• Packaging – Code authors need to package the software for user to download. The packaging machanism is usually a cabinet file addressed by the “codebase” attribute of an “object” element on a web page. Here is a piece of sample html code:
  <object classid="clsid:xxxx-xxxx-xxxx"
  codebase="/cab/testing.cab">
  </object>
• Signing – To protect the user from downloading software from the internet, users with user security level set to default level normally cannot download and execute the software (e.g. User Access Control, UAC, in windows 7).
  
  Digitial signature help to associate the software distribution to the code author, providing accountability for software developers.
  
  According to MSDN:
  When a user's security level is set at the default level, any object identified by the OBJECT tag on a Web page must be digitally signed. Internet Explorer's default security setting won't install a component that doesn't have a digital signature.
  
  
  This is a screen capture for the pop-up when you try to download and install an unsigned cabinet file
• Information file (.inf) – This file contain the setup information for Internet Explorer the use to install and register your software distribution. For the usage of INF file, we will discuss in another post.

Reference: MSDN - Introduction to Internet Component Download

Covariance and Correlation Coefficient

Covariance

In probability theory and statistics, covariance is a measure of how much two variables change together.
Variance is a special case of the covariance when the two variables are identical.

For any two assets A & B, the covariance of the return of these two asset in n months is calculated by the following formula:

Cov = 1/(n-1) * Σ _{i = 1 to n} (RA_i – avgRA_i) * (RB_i – avgRB_i)

where
RA_i = Return of asset A in month i
avgRA_i = Average return of asset A in n months
RB_i = Return of asset B in month i
avgRB_i = Average return of asset B in n months

Large positive covariance does not meaning two asset have high correlation. It also depends on their volatility (standard deviation). If the volatility is high, then the corrlation is not high. To solve this problem, we can make use of correlation coefficient of these two assets.

Correlation Coefficient

Correlation coefficient = Cov / (StdA * StdB)

where
StdA = Standard deviation of A
StdB = Standard deviation of B

The value of correlation coefficient will be lie between –1  to +1, for positive value, it mean positive correlation, vice versa.

 

Reference:
綠角財經筆記: 共變異數與相關係數(Covariance and Correlation Cofficient of Financial Assets)
Covariance - Wikipedia, the free encyclopedia
Correlation and dependence - Wikipedia, the free encyclopedia

Statistics Formulas

Parameters

• Population mean = μ = ( Σ X_i ) / N
• Population standard deviation = σ = sqrt [ Σ ( X_i - μ )² / N ]
• Population variance = σ² = Σ ( X_i - μ )² / N
• Variance of population proportion = σ_P² = PQ / n
• Standardized score = Z = (X - μ) / σ
• Population correlation coefficient = ρ = [ 1 / N ] * Σ { [ (X_i - μ_X) / σ_x ] * [ (Y_i - μ_Y) / σ_y ] }

Statistics

Unless otherwise noted, these formulas assume simple random sampling.

• Sample mean = x = ( Σ x_i ) / n
• Sample standard deviation = s = sqrt [ Σ ( x_i - x )² / ( n - 1 ) ]
• Sample variance = s² = Σ ( x_i - x )² / ( n - 1 )
• Variance of sample proportion = s_p² = pq / (n - 1)
• Pooled sample proportion = p = (p₁ * n₁ + p₂ * n₂) / (n₁ + n₂)
• Pooled sample standard deviation = s_p = sqrt [ (n₁ - 1) * s₁² + (n₂ - 1) * s₂² ] / (n₁ + n₂ - 2) ]
• Sample correlation coefficient = r = [ 1 / (n - 1) ] * Σ { [ (x_i - x) / s_x ] * [ (y_i - y) / s_y ] }

Correlation

• Pearson product-moment correlation = r = Σ (xy) / sqrt [ ( Σ x² ) * ( Σ y² ) ]
• Linear correlation (sample data) = r = [ 1 / (n - 1) ] * Σ { [ (x_i - x) / s_x ] * [ (y_i - y) / s_y ] }
• Linear correlation (population data) = ρ = [ 1 / N ] * Σ { [ (X_i - μ_X) / σ_x ] * [ (Y_i - μ_Y) / σ_y ] }

 

Reference: http://stattrek.com/Lesson1/Formulas.aspx?Tutorial=Stat