是极其潦草的学习笔记 w

# Python 篇

# package 的准备

pandas
statsmodels
openpyxl

# 读取文件

要指定好文件的路径
最好使用 names=['x','y'] 来指定说明变量和目标变量。

	import pandas as pd

	file_pass = r"D:\python\data.xlsx"
	df = pd.read_excel(file_pass, header=0, names=['cost', 'sales'])
	import statsmodels.formula.api as smf

	model = smf.ols('sales ~ cost', data=df)
	result = model.fit()
	print(result.summary())

输出结果为：

	OLS Regression Results
	==============================================================================
	Dep. Variable: sales R-squared: 1.000
	Model: OLS Adj. R-squared: 1.000
	Method: Least Squares F-statistic: 1.981e+31
	Date: Tue, 02 May 2023 Prob (F-statistic): 2.50e-47
	Time: 14:12:47 Log-Likelihood: 153.61
	No. Observations: 5 AIC: -303.2
	Df Residuals: 3 BIC: -304.0
	Df Model: 1
	Covariance Type: nonrobust
	==============================================================================
	coef std err t P>\|t\| [0.025 0.975]
	------------------------------------------------------------------------------
	Intercept 10.0000 1.49e-14 6.71e+14 0.000 10.000 10.000
	cost 2.0000 4.49e-16 4.45e+15 0.000 2.000 2.000
	==============================================================================
	Omnibus: nan Durbin-Watson: 0.667
	Prob(Omnibus): nan Jarque-Bera (JB): 0.375
	Skew: -0.344 Prob(JB): 0.829
	Kurtosis: 1.847 Cond. No. 77.8
	==============================================================================

# Stata 篇

# 简单使用 Stata

Log in
import data
save as
use data

	use D:\python\data_回归分析.dta
	regress 売上高万円広告費

** 注意：** 使用的数据文件一定是.dta 格式，<u> 而这个必须要先导入数据后保存再得到。</u>

输出：

-------------+----------------------------------   F(1, 3)         =         .
       Model |        4000         1        4000   Prob > F        =         .
    Residual |           0         3           0   R-squared       =    1.0000
-------------+----------------------------------   Adj R-squared   =    1.0000
       Total |        4000         4        1000   Root MSE        =         0

------------------------------------------------------------------------------
  売上高万円 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      広告費 |          2          .        .       .            .           .
       _cons |         10          .        .       .            .           .
------------------------------------------------------------------------------

# 附录

cost	sales
10	30
20	50
30	70
40	90
50	110

经济统计 Python Stata