第三章-数据分析方法

Models
Variable
Simple
Multiple
Linear
NonLinear
Linear
编辑版ppt
11
Types of Regression Models
1 Explanatory Variable
Regression 2+ Explanatole
➢ Z-score assumes xi obeys Gaussian distribution. If xi has a different distribution, then the normalization will twist the
pattern (variance will be far away from the standard
S 编辑版ppt
(xi x)2 n
3
6.2 Simple Linear Regression
Learning Objectives: •1. Describe the Linear Regression Model •2. Explain Ordinary Least Squares •3. Compute Regression Coefficients •4. Evaluate the linear regression model •5. Predict Response Variable
(Explanatory) Variables
• 3. Used Mainly for Prediction & Estimation
编辑版ppt
5
Types of Regression Models
Regression Models
编辑版ppt
6
Types of Regression Models
1
Linear normalization:
(6.1)
x'iX'm in (X'm aX X xm 'm a)i x X n(m xi in Xm)in
Ratio normalization:
(6.2)
x 'i
| xi |
n
| xi |
i 1
Z-score normalization:
(6.3)
deviation) and leads to incorrect pattern recognition.
σ Is population standard deviation, in general, it can be approximated by sample standard deviation (S)
Chapter 4 The Methods of Data Analysis
6.1 Data normalization
Data normalization is the basis for comparing experiments within large series when experimental conditions may not be identical.
编辑版ppt
4
6.2.1 Describe the Linear Regression Model
Regression Models:
• 1. Answer ‘What Is the Relationship Between the Variables?’
• 2. Equation Used
– 1 Numerical Dependent (Response) Variable – 1 or More Numerical or Categorical Independent
Multiple
Linear
NonLinear
Linear
1 Explanatory Variable
Regression 2+ Explanatory Models Variable
Simple
Multiple
Linear
NonLinear
编辑版ppt
10
Types of Regression Models
1 Explanatory Variable
Regression 2+ Explanatory
x'i
(xi
x)
编辑版ppt
2
➢ Generally, linear normalization is recommended (if
X’max = 1 and X’min = 0, x’i is normalized in percentage by formula (6.1)).
➢ After Ratio normalization, the sum of normalized variables will be equal to 1.
Normalization ensures that the experimental quality
of the data is comparable and, sound mathematical
algorithms have been employed. Normalization includes
various options to standardize data and to adjust
background levels and correct gradients. The commonly
used normalization functio编n辑s版apprte as follows:
1 Explanatory Variable
Regression Models
Simple
编辑版ppt
7
Types of Regression Models
1 Explanatory Variable
Regression 2+ Explanatory
Models
Variables
Simple
Multiple
编辑版ppt
8
Types of Regression Models
1 Explanatory Variable
Regression Models
2+ Explanatory Variable
Simple
Multiple
Linear
编辑版ppt
9
Types of Regression Models