python笔记：7.2.1.5 方差分析模型的预测（数码相机销量）

 

# -*- coding: utf-8 -*-
"""
Created on Mon Jul  8 16:46:09 2019

@author: User
"""

# 《Python数据分析基础》中国统计出版社

#import numpy as np
from scipy import stats
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
from statsmodels.stats.multicomp import pairwise_tukeyhsd
from matplotlib.font_manager import FontProperties
myfont=FontProperties(fname='data\msyh.ttc')

dc_sales = pd.read_csv(u'data\\ch7\\dc_sales.csv',encoding = "gbk")

dc_sales['pixel'] = dc_sales['pixel'].astype('category')
dc_sales['pixel'].cat.categories=['500万像素及以下','500-600万像素',
        '600-800万像素','800-1000万像素',
        '1000万像素及以上']
print(dc_sales.head())

formula = 'sales ~ C(pixel)-1'
dc_sales_est = smf.ols(formula, dc_sales).fit()  # dc_sales_est 是一个模型对象

print("\n dc_sales_est.fittedvalues:")
print(dc_sales_est.fittedvalues)

运行：

 dc_sales_est.fittedvalues:
0      81.125
1      95.750
2     107.125
3     124.000
4     122.125
5      81.125
6      95.750
7     107.125
8     124.000
9     122.125
10     81.125
11     95.750
12    107.125
13    124.000
14    122.125
15     81.125
16     95.750
17    107.125
18    124.000
19    122.125
20     81.125
21     95.750
22    107.125
23    124.000
24    122.125
25     81.125
26     95.750
27    107.125
28    124.000
29    122.125
30     81.125
31     95.750
32    107.125
33    124.000
34    122.125
35     81.125
36     95.750
37    107.125
38    124.000
39    122.125
dtype: float64