1、数据准备

import urllib.request
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import pandas as pd
# 下载同时显示进度条
def jdt(down, pic, all):
    """
    @down : 已经下载的数据块
    @pic : 数据块的大小
    @all : 远程文件的大小
    """
    jd = 100.0 * down * pic / all
    if jd > 100:
        jd = 100
    print('%.2f%%'%jd)

url = 'http://lib.stat.cmu.edu/datasets/CPS_85_Wages'
fil_out =  r'D:\Python_data\wages.txt'
urllib.request.urlretrieve(url, fil_out,jdt)

2、数据读取及简单探索

查看下载文件，前27行为无效数据, 后6行也是无效
数据描述如下：

EDUCATION: Number of years of education.
SOUTH: Indicator variable for Southern Region (1=Person lives in
South, 0=Person lives elsewhere).
SEX: Indicator variable for sex (1=Female, 0=Male).
EXPERIENCE: Number of years of work experience.
UNION: Indicator variable for union membership (1=Union member,
0=Not union member).
WAGE: Wage (dollars per hour).
AGE: Age (years).
RACE: Race (1=Other, 2=Hispanic, 3=White).
OCCUPATION: Occupational category (1=Management, 2=Sales, 3=Clerical, 4=Service, 5=Professional, 6=Other).
SECTOR: Sector (0=Other, 1=Manufacturing, 2=Construction).
MARR: Marital Status (0=Unmarried, 1=Married)

name = ['EDUCATION', 'SOUTH', 'SEX', 'EXPERIENCE', 'UNION',
        'WAGE', 'AGE', 'RACE', 'OCCUPATION', 'SECTOR', 'MARR']
data_wage = pd.read_table(fil_out, skiprows=27, skipfooter = 6, sep = '\t', header=None )
data_wage.columns = name
# 去掉大部分分类变量看相关
d = ['EDUCATION', 'SEX', 'EXPERIENCE', 'WAGE', 'AGE']
pd.plotting.scatter_matrix(data_wage[d], color='steelblue')
plt.show()

3、绘制交互作用相关性散点

# 绘制回归相关性矩阵+交互作用
import seaborn
seaborn.pairplot(data_wage, vars=['WAGE', 'AGE', 'EDUCATION']
                ,kind = 'reg', hue = 'SEX', size=5)
plt.show()

4、建模分析

# 单变量回归绘图+交互作用
seaborn.lmplot(y='WAGE', x = 'EDUCATION', hue = 'SEX',data=data_wage)
plt.show()
# 建模
from statsmodels.formula.api import ols
results = ols('WAGE~EDUCATION + C(SEX) + EDUCATION*C(SEX)', data = data_wage).fit()
print(results.summary())

结果如下：

==============================================================================
Dep. Variable:                   WAGE   R-squared:                       0.190
Model:                            OLS   Adj. R-squared:                  0.186
Method:                 Least Squares   F-statistic:                     41.50
Date:                Sun, 30 Sep 2018   Prob (F-statistic):           4.24e-24
Time:                        18:07:55   Log-Likelihood:                -1575.0
No. Observations:                 534   AIC:                             3158.
Df Residuals:                     530   BIC:                             3175.
Df Model:                           3
Covariance Type:            nonrobust
=========================================================================================
                            coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------------
Intercept                 1.1046      1.314      0.841      0.401      -1.476       3.685
C(SEX)[T.1]              -4.3704      2.085     -2.096      0.037      -8.466      -0.274
EDUCATION                 0.6831      0.099      6.918      0.000       0.489       0.877
EDUCATION:C(SEX)[T.1]     0.1725      0.157      1.098      0.273      -0.136       0.481
==============================================================================
Omnibus:                      208.151   Durbin-Watson:                   1.863
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1278.081
Skew:                           1.587   Prob(JB):                    2.94e-278
Kurtosis:                       9.883   Cond. No.                         170.
==============================================================================

就该数据而言，男性受教育作用要比女性受教育作用大，这可能是女性存在生育期工作空挡和社会对男性偏向造成的