用いるデータ

例として分子性物質のデータ（融点・沸点）を使ってみましょう。分子量（molecular weight）から沸点（boiling point）を予測するという問題を解いてみたいと思います。

import pandas as pd
data = [['HF', 19.5, 20.0],
        ['HCl', -84.9, 36.5],
        ['HBr', -67.0, 80.9],
        ['HI', -35.1, 127.9],
        ['H2O', 100.0, 18.0],
        ['H2S', -60.7, 34.1],
        ['H2Se', -42, 81.0],
        ['H2Te', -1.8, 129.6],
        ['NH3', -33.4, 17.0],
        ['PH3', -87, 34.0],
        ['AsH3', -55, 77.9],
        ['SbH3', -17.1, 124.8],
        ['CH4', -161.49, 16.0],
        ['SiH4', -111.8, 32.1],
        ['GeH4', -90, 76.6],
        ['SnH4', -52, 122.7],
        ['He', -268.934, 4.0],
        ['Ne', -246.048, 20.2],
        ['Ar', -185.7, 39.9],
        ['Kr', -152.3, 83.8],
        ['Xe', -108.1, 131.3],
       ]
df = pd.DataFrame(data, columns = ['molecule', 'boiling point', 'molecular weight'])
df

	molecule	boiling point	molecular weight
0	HF	19.500	20.0
1	HCl	-84.900	36.5
2	HBr	-67.000	80.9
3	HI	-35.100	127.9
4	H2O	100.000	18.0
5	H2S	-60.700	34.1
6	H2Se	-42.000	81.0
7	H2Te	-1.800	129.6
8	NH3	-33.400	17.0
9	PH3	-87.000	34.0
10	AsH3	-55.000	77.9
11	SbH3	-17.100	124.8
12	CH4	-161.490	16.0
13	SiH4	-111.800	32.1
14	GeH4	-90.000	76.6
15	SnH4	-52.000	122.7
16	He	-268.934	4.0
17	Ne	-246.048	20.2
18	Ar	-185.700	39.9
19	Kr	-152.300	83.8
20	Xe	-108.100	131.3

回帰は y = f(x) という関係において関数 f を同定するという作業です。今回は分子量を x 沸点を y とします。そのときの f を求めてみましょう。

X = df.loc[:, ['molecular weight']].as_matrix()
X

array([[  20. ],
       [  36.5],
       [  80.9],
       [ 127.9],
       [  18. ],
       [  34.1],
       [  81. ],
       [ 129.6],
       [  17. ],
       [  34. ],
       [  77.9],
       [ 124.8],
       [  16. ],
       [  32.1],
       [  76.6],
       [ 122.7],
       [   4. ],
       [  20.2],
       [  39.9],
       [  83.8],
       [ 131.3]])

Y = df['boiling point'].as_matrix()
Y

array([  19.5  ,  -84.9  ,  -67.   ,  -35.1  ,  100.   ,  -60.7  ,
        -42.   ,   -1.8  ,  -33.4  ,  -87.   ,  -55.   ,  -17.1  ,
       -161.49 , -111.8  ,  -90.   ,  -52.   , -268.934, -246.048,
       -185.7  , -152.3  , -108.1  ])

まずは x と y の関係をプロットします。

%matplotlib inline
import matplotlib.pyplot as plt
# 散布図
plt.figure(figsize=(5,4))
plt.scatter(X, Y, alpha=0.3)
for name, x, y in zip(df.loc[:, ['molecule']].as_matrix(), X, Y):
    plt.text(x, y, name[0], size=8)
plt.xlabel('molecular weight')
plt.ylabel('boiling point')
plt.grid()
plt.show()

これを上手に回帰する直線を求めるという問題です。

まずは一番便利な scikit-learn から

本当は scikit-learn を使うのが一番便利です。まずは線形回帰を実行するための学習器を定義します。

from sklearn import linear_model
lr = linear_model.LinearRegression()

学習（フィッティング）を行います。

lr.fit(X, Y)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

傾きと切片が決まれば直線が得られますが、回帰係数というのが傾きにあたります。

# 回帰係数
lr.coef_

array([ 0.55956295])

そして切片

# 切片
lr.intercept_

-117.75943870102266

つまり、得られた回帰直線は次のようになります。

print("y = f(x) = wx + t; (w, t) = ({0}, {1})".format(lr.coef_[0], lr.intercept_))

y = f(x) = wx + t; (w, t) = (0.5595629540025041, -117.75943870102266)

決定係数は、説明変数 x が従属変数 y のどれくらいを説明できるかを表す値で、1に近いほど相対的な残差が少ないことを表します。回帰方程式のあてはまりの良さの尺度として利用されます。

# 決定係数R2
lr.score(X, Y)

0.083634989644543967

さて、この数字は驚くほど低いわけですが、図示してみましょう。

%matplotlib inline
import matplotlib.pyplot as plt
# 散布図
plt.figure(figsize=(5,4))
plt.scatter(X, Y, alpha=0.3)
 
# 回帰直線
plt.plot(X, lr.predict(X))
for name, x, y in zip(df.loc[:, ['molecule']].as_matrix(), X, Y):
    plt.text(x, y, name[0], size=8)
plt.xlabel('molecular weight')
plt.ylabel('boiling point')
plt.grid()
plt.show()

散布図を描いたときから既に想像できたことと思いますが、分子量と沸点の関係を直線で表すのは無理ゲーだということがよく分かります。

それはそれとして、この得られた回帰直線を使って、様々な分子量の化合物の沸点を予測してみましょう。

lr.predict(200)

array([-5.8468479])

lr.predict([[150], [100]])

array([-33.8249956, -61.8031433])

非常に疑わしいモデルですが、いちおう直線回帰モデルが完成しました。

次は、ガチPythonで。

さて、scikit-learn という便利なツールに頼ってると先生に「お前は本当に線形回帰の計算手法が分かっとんのかゴルァ」と言われてしまいますので、scikit-learn に頼らずに実装してみましょう。

# 平均値を求める関数
def mean(list):
    sum = 0
    for x in list:
        sum += x
    return sum / len(list)

# 分散を求める関数
def variance(list):
    ave = mean(list)
    sum = 0
    for x in list:
        sum += (x - ave) ** 2
    return sum / len(list)

# 標準偏差を求める関数
import math
def standard_deviation(list):
    return math.sqrt(variance(list))

# 共分散 = 偏差積の平均
def covariance(list1, list2): 
    mean1 = mean(list1)
    mean2 = mean(list2)
    sum = 0
    for d1, d2 in zip(list1, list2):
        sum += (d1 - mean1) * (d2 - mean2)
    return sum / len(list1)

# 相関係数 = 共分散を list1, list2 の標準偏差で割ったもの
def correlation(list1, list2):
    return covariance(list1, list2) / (standard_deviation(list1) * standard_deviation(list2))

# 回帰直線の傾き＝相関係数＊（（yの標準偏差）／（xの標準偏差））
def w_fit(xlist, ylist):
    return correlation(xlist, ylist) * standard_deviation(ylist) / standard_deviation(xlist)

# y切片＝yの平均－（傾き＊xの平均）
def t_fit(xlist, ylist):
    return mean(ylist) - w_fit(xlist, ylist) * mean(xlist)

# 回帰直線の式を表示
w = w_fit(X, Y)
t = t_fit(X, Y)
print("y = f(x) = wx + t; (w, t) = ({0}, {1})".format(w, t))

y = f(x) = wx + t; (w, t) = ([ 0.55956295], [-117.7594387])

# 回帰直線の式を関数として表現
def f(x):
    return w * x + t

f(200)

array([-5.8468479])

f([[150], [100]])

array([[-33.8249956],
       [-61.8031433]])

# 決定係数R2
def r2(xlist, ylist):
    wa1 = 0.
    wa2 = 0.
    for x, y in zip(xlist, ylist):
        wa1 += (y - f(x))**2
        wa2 += (y - mean(ylist))**2
    return 1. - wa1 / wa2

r2(X, Y)

array([ 0.08363499])

さて、表計算で解けと言われたので pandas で書いてみましょうか。

先生はそれでも許してくれません。「計算の途中過程を一個一個確認できるように表計算を使って解けやゴルァ」とおっしゃいますので、表計算といえば pandas ですよね先生！？

import copy
from IPython.display import display
excel = copy.deepcopy(df)
excel

	molecule	boiling point	molecular weight
0	HF	19.500	20.0
1	HCl	-84.900	36.5
2	HBr	-67.000	80.9
3	HI	-35.100	127.9
4	H2O	100.000	18.0
5	H2S	-60.700	34.1
6	H2Se	-42.000	81.0
7	H2Te	-1.800	129.6
8	NH3	-33.400	17.0
9	PH3	-87.000	34.0
10	AsH3	-55.000	77.9
11	SbH3	-17.100	124.8
12	CH4	-161.490	16.0
13	SiH4	-111.800	32.1
14	GeH4	-90.000	76.6
15	SnH4	-52.000	122.7
16	He	-268.934	4.0
17	Ne	-246.048	20.2
18	Ar	-185.700	39.9
19	Kr	-152.300	83.8
20	Xe	-108.100	131.3

excel['y'] = excel['boiling point']
excel['x'] = excel['molecular weight']
mean_y = mean(excel['y'])
mean_x = mean(excel['x'])
display(excel, pd.DataFrame([[mean_y, mean_x]], columns=['y','x'], index=['mean']))

	molecule	boiling point	molecular weight	y	x
0	HF	19.500	20.0	19.500	20.0
1	HCl	-84.900	36.5	-84.900	36.5
2	HBr	-67.000	80.9	-67.000	80.9
3	HI	-35.100	127.9	-35.100	127.9
4	H2O	100.000	18.0	100.000	18.0
5	H2S	-60.700	34.1	-60.700	34.1
6	H2Se	-42.000	81.0	-42.000	81.0
7	H2Te	-1.800	129.6	-1.800	129.6
8	NH3	-33.400	17.0	-33.400	17.0
9	PH3	-87.000	34.0	-87.000	34.0
10	AsH3	-55.000	77.9	-55.000	77.9
11	SbH3	-17.100	124.8	-17.100	124.8
12	CH4	-161.490	16.0	-161.490	16.0
13	SiH4	-111.800	32.1	-111.800	32.1
14	GeH4	-90.000	76.6	-90.000	76.6
15	SnH4	-52.000	122.7	-52.000	122.7
16	He	-268.934	4.0	-268.934	4.0
17	Ne	-246.048	20.2	-246.048	20.2
18	Ar	-185.700	39.9	-185.700	39.9
19	Kr	-152.300	83.8	-152.300	83.8
20	Xe	-108.100	131.3	-108.100	131.3

	y	x
mean	-82.898667	62.3

excel['y-mean(y)'] = [y - mean_y for y in excel['y']]
excel['x-mean(x)'] = [x - mean_x for x in excel['x']]
display(excel, pd.DataFrame([[mean_y, mean_x]], columns=['y','x'], index=['mean']))

	molecule	boiling point	molecular weight	y	x	y-mean(y)	x-mean(x)
0	HF	19.500	20.0	19.500	20.0	102.398667	-42.3
1	HCl	-84.900	36.5	-84.900	36.5	-2.001333	-25.8
2	HBr	-67.000	80.9	-67.000	80.9	15.898667	18.6
3	HI	-35.100	127.9	-35.100	127.9	47.798667	65.6
4	H2O	100.000	18.0	100.000	18.0	182.898667	-44.3
5	H2S	-60.700	34.1	-60.700	34.1	22.198667	-28.2
6	H2Se	-42.000	81.0	-42.000	81.0	40.898667	18.7
7	H2Te	-1.800	129.6	-1.800	129.6	81.098667	67.3
8	NH3	-33.400	17.0	-33.400	17.0	49.498667	-45.3
9	PH3	-87.000	34.0	-87.000	34.0	-4.101333	-28.3
10	AsH3	-55.000	77.9	-55.000	77.9	27.898667	15.6
11	SbH3	-17.100	124.8	-17.100	124.8	65.798667	62.5
12	CH4	-161.490	16.0	-161.490	16.0	-78.591333	-46.3
13	SiH4	-111.800	32.1	-111.800	32.1	-28.901333	-30.2
14	GeH4	-90.000	76.6	-90.000	76.6	-7.101333	14.3
15	SnH4	-52.000	122.7	-52.000	122.7	30.898667	60.4
16	He	-268.934	4.0	-268.934	4.0	-186.035333	-58.3
17	Ne	-246.048	20.2	-246.048	20.2	-163.149333	-42.1
18	Ar	-185.700	39.9	-185.700	39.9	-102.801333	-22.4
19	Kr	-152.300	83.8	-152.300	83.8	-69.401333	21.5
20	Xe	-108.100	131.3	-108.100	131.3	-25.201333	69.0

	y	x
mean	-82.898667	62.3

excel['(y-mean(y))**2'] = [sa ** 2 for sa in excel['y-mean(y)']]
excel['(x-mean(x))**2'] = [sa ** 2 for sa in excel['x-mean(x)']]
display(excel, pd.DataFrame([[mean_y, mean_x]], columns=['y','x'], index=['mean']))

	molecule	boiling point	molecular weight	y	x	y-mean(y)	x-mean(x)	(y-mean(y))**2	(x-mean(x))**2
0	HF	19.500	20.0	19.500	20.0	102.398667	-42.3	10485.486935	1789.29
1	HCl	-84.900	36.5	-84.900	36.5	-2.001333	-25.8	4.005335	665.64
2	HBr	-67.000	80.9	-67.000	80.9	15.898667	18.6	252.767602	345.96
3	HI	-35.100	127.9	-35.100	127.9	47.798667	65.6	2284.712535	4303.36
4	H2O	100.000	18.0	100.000	18.0	182.898667	-44.3	33451.922268	1962.49
5	H2S	-60.700	34.1	-60.700	34.1	22.198667	-28.2	492.780802	795.24
6	H2Se	-42.000	81.0	-42.000	81.0	40.898667	18.7	1672.700935	349.69
7	H2Te	-1.800	129.6	-1.800	129.6	81.098667	67.3	6576.993735	4529.29
8	NH3	-33.400	17.0	-33.400	17.0	49.498667	-45.3	2450.118002	2052.09
9	PH3	-87.000	34.0	-87.000	34.0	-4.101333	-28.3	16.820935	800.89
10	AsH3	-55.000	77.9	-55.000	77.9	27.898667	15.6	778.335602	243.36
11	SbH3	-17.100	124.8	-17.100	124.8	65.798667	62.5	4329.464535	3906.25
12	CH4	-161.490	16.0	-161.490	16.0	-78.591333	-46.3	6176.597675	2143.69
13	SiH4	-111.800	32.1	-111.800	32.1	-28.901333	-30.2	835.287068	912.04
14	GeH4	-90.000	76.6	-90.000	76.6	-7.101333	14.3	50.428935	204.49
15	SnH4	-52.000	122.7	-52.000	122.7	30.898667	60.4	954.727602	3648.16
16	He	-268.934	4.0	-268.934	4.0	-186.035333	-58.3	34609.145248	3398.89
17	Ne	-246.048	20.2	-246.048	20.2	-163.149333	-42.1	26617.704967	1772.41
18	Ar	-185.700	39.9	-185.700	39.9	-102.801333	-22.4	10568.114135	501.76
19	Kr	-152.300	83.8	-152.300	83.8	-69.401333	21.5	4816.545068	462.25
20	Xe	-108.100	131.3	-108.100	131.3	-25.201333	69.0	635.107202	4761.00

	y	x
mean	-82.898667	62.3

variance_y = mean(excel['(y-mean(y))**2'])
variance_x = mean(excel['(x-mean(x))**2'])
sd_y = math.sqrt(variance_y)
sd_x = math.sqrt(variance_x)
display(excel, pd.DataFrame([[mean_y, mean_x], [variance_y, variance_x], [sd_y, sd_x]], 
                            columns=['y','x'], index=['mean', 'variance', 'sd']))

	molecule	boiling point	molecular weight	y	x	y-mean(y)	x-mean(x)	(y-mean(y))**2	(x-mean(x))**2
0	HF	19.500	20.0	19.500	20.0	102.398667	-42.3	10485.486935	1789.29
1	HCl	-84.900	36.5	-84.900	36.5	-2.001333	-25.8	4.005335	665.64
2	HBr	-67.000	80.9	-67.000	80.9	15.898667	18.6	252.767602	345.96
3	HI	-35.100	127.9	-35.100	127.9	47.798667	65.6	2284.712535	4303.36
4	H2O	100.000	18.0	100.000	18.0	182.898667	-44.3	33451.922268	1962.49
5	H2S	-60.700	34.1	-60.700	34.1	22.198667	-28.2	492.780802	795.24
6	H2Se	-42.000	81.0	-42.000	81.0	40.898667	18.7	1672.700935	349.69
7	H2Te	-1.800	129.6	-1.800	129.6	81.098667	67.3	6576.993735	4529.29
8	NH3	-33.400	17.0	-33.400	17.0	49.498667	-45.3	2450.118002	2052.09
9	PH3	-87.000	34.0	-87.000	34.0	-4.101333	-28.3	16.820935	800.89
10	AsH3	-55.000	77.9	-55.000	77.9	27.898667	15.6	778.335602	243.36
11	SbH3	-17.100	124.8	-17.100	124.8	65.798667	62.5	4329.464535	3906.25
12	CH4	-161.490	16.0	-161.490	16.0	-78.591333	-46.3	6176.597675	2143.69
13	SiH4	-111.800	32.1	-111.800	32.1	-28.901333	-30.2	835.287068	912.04
14	GeH4	-90.000	76.6	-90.000	76.6	-7.101333	14.3	50.428935	204.49
15	SnH4	-52.000	122.7	-52.000	122.7	30.898667	60.4	954.727602	3648.16
16	He	-268.934	4.0	-268.934	4.0	-186.035333	-58.3	34609.145248	3398.89
17	Ne	-246.048	20.2	-246.048	20.2	-163.149333	-42.1	26617.704967	1772.41
18	Ar	-185.700	39.9	-185.700	39.9	-102.801333	-22.4	10568.114135	501.76
19	Kr	-152.300	83.8	-152.300	83.8	-69.401333	21.5	4816.545068	462.25
20	Xe	-108.100	131.3	-108.100	131.3	-25.201333	69.0	635.107202	4761.00

	y	x
mean	-82.898667	62.300000
variance	7050.465101	1883.249524
sd	83.967048	43.396423

excel['(y-mean(y)) * (x-mean(x))'] = excel['y-mean(y)'] * excel['x-mean(x)']
display(excel, pd.DataFrame([[mean_y, mean_x], [variance_y, variance_x], [sd_y, sd_x]], 
                            columns=['y','x'], index=['mean', 'variance', 'sd']))

	molecule	boiling point	molecular weight	y	x	y-mean(y)	x-mean(x)	(y-mean(y))**2	(x-mean(x))**2	(y-mean(y)) * (x-mean(x))
0	HF	19.500	20.0	19.500	20.0	102.398667	-42.3	10485.486935	1789.29	-4331.463600
1	HCl	-84.900	36.5	-84.900	36.5	-2.001333	-25.8	4.005335	665.64	51.634400
2	HBr	-67.000	80.9	-67.000	80.9	15.898667	18.6	252.767602	345.96	295.715200
3	HI	-35.100	127.9	-35.100	127.9	47.798667	65.6	2284.712535	4303.36	3135.592533
4	H2O	100.000	18.0	100.000	18.0	182.898667	-44.3	33451.922268	1962.49	-8102.410933
5	H2S	-60.700	34.1	-60.700	34.1	22.198667	-28.2	492.780802	795.24	-626.002400
6	H2Se	-42.000	81.0	-42.000	81.0	40.898667	18.7	1672.700935	349.69	764.805067
7	H2Te	-1.800	129.6	-1.800	129.6	81.098667	67.3	6576.993735	4529.29	5457.940267
8	NH3	-33.400	17.0	-33.400	17.0	49.498667	-45.3	2450.118002	2052.09	-2242.289600
9	PH3	-87.000	34.0	-87.000	34.0	-4.101333	-28.3	16.820935	800.89	116.067733
10	AsH3	-55.000	77.9	-55.000	77.9	27.898667	15.6	778.335602	243.36	435.219200
11	SbH3	-17.100	124.8	-17.100	124.8	65.798667	62.5	4329.464535	3906.25	4112.416667
12	CH4	-161.490	16.0	-161.490	16.0	-78.591333	-46.3	6176.597675	2143.69	3638.778733
13	SiH4	-111.800	32.1	-111.800	32.1	-28.901333	-30.2	835.287068	912.04	872.820267
14	GeH4	-90.000	76.6	-90.000	76.6	-7.101333	14.3	50.428935	204.49	-101.549067
15	SnH4	-52.000	122.7	-52.000	122.7	30.898667	60.4	954.727602	3648.16	1866.279467
16	He	-268.934	4.0	-268.934	4.0	-186.035333	-58.3	34609.145248	3398.89	10845.859933
17	Ne	-246.048	20.2	-246.048	20.2	-163.149333	-42.1	26617.704967	1772.41	6868.586933
18	Ar	-185.700	39.9	-185.700	39.9	-102.801333	-22.4	10568.114135	501.76	2302.749867
19	Kr	-152.300	83.8	-152.300	83.8	-69.401333	21.5	4816.545068	462.25	-1492.128667
20	Xe	-108.100	131.3	-108.100	131.3	-25.201333	69.0	635.107202	4761.00	-1738.892000

	y	x
mean	-82.898667	62.300000
variance	7050.465101	1883.249524
sd	83.967048	43.396423

covar_xy = mean(excel['(y-mean(y)) * (x-mean(x))'])
corr_xy = covar_xy / (sd_x * sd_y)
display(excel, pd.DataFrame([[mean_y, mean_x], [variance_y, variance_x], [sd_y, sd_x]], 
                            columns=['y','x'], index=['mean', 'variance', 'sd']),
       pd.DataFrame([covar_xy, corr_xy], index=['covariance', 'correlation'], columns=['x,y']))

	molecule	boiling point	molecular weight	y	x	y-mean(y)	x-mean(x)	(y-mean(y))**2	(x-mean(x))**2	(y-mean(y)) * (x-mean(x))
0	HF	19.500	20.0	19.500	20.0	102.398667	-42.3	10485.486935	1789.29	-4331.463600
1	HCl	-84.900	36.5	-84.900	36.5	-2.001333	-25.8	4.005335	665.64	51.634400
2	HBr	-67.000	80.9	-67.000	80.9	15.898667	18.6	252.767602	345.96	295.715200
3	HI	-35.100	127.9	-35.100	127.9	47.798667	65.6	2284.712535	4303.36	3135.592533
4	H2O	100.000	18.0	100.000	18.0	182.898667	-44.3	33451.922268	1962.49	-8102.410933
5	H2S	-60.700	34.1	-60.700	34.1	22.198667	-28.2	492.780802	795.24	-626.002400
6	H2Se	-42.000	81.0	-42.000	81.0	40.898667	18.7	1672.700935	349.69	764.805067
7	H2Te	-1.800	129.6	-1.800	129.6	81.098667	67.3	6576.993735	4529.29	5457.940267
8	NH3	-33.400	17.0	-33.400	17.0	49.498667	-45.3	2450.118002	2052.09	-2242.289600
9	PH3	-87.000	34.0	-87.000	34.0	-4.101333	-28.3	16.820935	800.89	116.067733
10	AsH3	-55.000	77.9	-55.000	77.9	27.898667	15.6	778.335602	243.36	435.219200
11	SbH3	-17.100	124.8	-17.100	124.8	65.798667	62.5	4329.464535	3906.25	4112.416667
12	CH4	-161.490	16.0	-161.490	16.0	-78.591333	-46.3	6176.597675	2143.69	3638.778733
13	SiH4	-111.800	32.1	-111.800	32.1	-28.901333	-30.2	835.287068	912.04	872.820267
14	GeH4	-90.000	76.6	-90.000	76.6	-7.101333	14.3	50.428935	204.49	-101.549067
15	SnH4	-52.000	122.7	-52.000	122.7	30.898667	60.4	954.727602	3648.16	1866.279467
16	He	-268.934	4.0	-268.934	4.0	-186.035333	-58.3	34609.145248	3398.89	10845.859933
17	Ne	-246.048	20.2	-246.048	20.2	-163.149333	-42.1	26617.704967	1772.41	6868.586933
18	Ar	-185.700	39.9	-185.700	39.9	-102.801333	-22.4	10568.114135	501.76	2302.749867
19	Kr	-152.300	83.8	-152.300	83.8	-69.401333	21.5	4816.545068	462.25	-1492.128667
20	Xe	-108.100	131.3	-108.100	131.3	-25.201333	69.0	635.107202	4761.00	-1738.892000

	y	x
mean	-82.898667	62.300000
variance	7050.465101	1883.249524
sd	83.967048	43.396423

	x,y
covariance	1053.796667
correlation	0.289197

w = corr_xy * sd_y / sd_x
t = mean_y - w * mean_x
display(excel, pd.DataFrame([[mean_y, mean_x], [variance_y, variance_x], [sd_y, sd_x]], 
                            columns=['y','x'], index=['mean', 'variance', 'sd']),
        pd.DataFrame([covar_xy, corr_xy], index=['covariance', 'correlation'], columns=['x,y']),
        pd.DataFrame([[w, t]], columns=["w", "t"], index=["y = f(x) = wx + t"]))

	molecule	boiling point	molecular weight	y	x	y-mean(y)	x-mean(x)	(y-mean(y))**2	(x-mean(x))**2	(y-mean(y)) * (x-mean(x))
0	HF	19.500	20.0	19.500	20.0	102.398667	-42.3	10485.486935	1789.29	-4331.463600
1	HCl	-84.900	36.5	-84.900	36.5	-2.001333	-25.8	4.005335	665.64	51.634400
2	HBr	-67.000	80.9	-67.000	80.9	15.898667	18.6	252.767602	345.96	295.715200
3	HI	-35.100	127.9	-35.100	127.9	47.798667	65.6	2284.712535	4303.36	3135.592533
4	H2O	100.000	18.0	100.000	18.0	182.898667	-44.3	33451.922268	1962.49	-8102.410933
5	H2S	-60.700	34.1	-60.700	34.1	22.198667	-28.2	492.780802	795.24	-626.002400
6	H2Se	-42.000	81.0	-42.000	81.0	40.898667	18.7	1672.700935	349.69	764.805067
7	H2Te	-1.800	129.6	-1.800	129.6	81.098667	67.3	6576.993735	4529.29	5457.940267
8	NH3	-33.400	17.0	-33.400	17.0	49.498667	-45.3	2450.118002	2052.09	-2242.289600
9	PH3	-87.000	34.0	-87.000	34.0	-4.101333	-28.3	16.820935	800.89	116.067733
10	AsH3	-55.000	77.9	-55.000	77.9	27.898667	15.6	778.335602	243.36	435.219200
11	SbH3	-17.100	124.8	-17.100	124.8	65.798667	62.5	4329.464535	3906.25	4112.416667
12	CH4	-161.490	16.0	-161.490	16.0	-78.591333	-46.3	6176.597675	2143.69	3638.778733
13	SiH4	-111.800	32.1	-111.800	32.1	-28.901333	-30.2	835.287068	912.04	872.820267
14	GeH4	-90.000	76.6	-90.000	76.6	-7.101333	14.3	50.428935	204.49	-101.549067
15	SnH4	-52.000	122.7	-52.000	122.7	30.898667	60.4	954.727602	3648.16	1866.279467
16	He	-268.934	4.0	-268.934	4.0	-186.035333	-58.3	34609.145248	3398.89	10845.859933
17	Ne	-246.048	20.2	-246.048	20.2	-163.149333	-42.1	26617.704967	1772.41	6868.586933
18	Ar	-185.700	39.9	-185.700	39.9	-102.801333	-22.4	10568.114135	501.76	2302.749867
19	Kr	-152.300	83.8	-152.300	83.8	-69.401333	21.5	4816.545068	462.25	-1492.128667
20	Xe	-108.100	131.3	-108.100	131.3	-25.201333	69.0	635.107202	4761.00	-1738.892000

	y	x
mean	-82.898667	62.300000
variance	7050.465101	1883.249524
sd	83.967048	43.396423

	x,y
covariance	1053.796667
correlation	0.289197

	w	t
y = f(x) = wx + t	0.559563	-117.759439

# 回帰直線の式を関数として表現
def f(x):
    return w * x + t

excel['f(x)'] = f(excel['x'])
display(excel, pd.DataFrame([[mean_y, mean_x], [variance_y, variance_x], [sd_y, sd_x]], 
                            columns=['y','x'], index=['mean', 'variance', 'sd']),
        pd.DataFrame([covar_xy, corr_xy], index=['covariance', 'correlation'], columns=['x,y']),
        pd.DataFrame([[w, t]], columns=["w", "t"], index=["y = f(x) = wx + t"]))

	molecule	boiling point	molecular weight	y	x	y-mean(y)	x-mean(x)	(y-mean(y))**2	(x-mean(x))**2	(y-mean(y)) * (x-mean(x))	f(x)
0	HF	19.500	20.0	19.500	20.0	102.398667	-42.3	10485.486935	1789.29	-4331.463600	-106.568180
1	HCl	-84.900	36.5	-84.900	36.5	-2.001333	-25.8	4.005335	665.64	51.634400	-97.335391
2	HBr	-67.000	80.9	-67.000	80.9	15.898667	18.6	252.767602	345.96	295.715200	-72.490796
3	HI	-35.100	127.9	-35.100	127.9	47.798667	65.6	2284.712535	4303.36	3135.592533	-46.191337
4	H2O	100.000	18.0	100.000	18.0	182.898667	-44.3	33451.922268	1962.49	-8102.410933	-107.687306
5	H2S	-60.700	34.1	-60.700	34.1	22.198667	-28.2	492.780802	795.24	-626.002400	-98.678342
6	H2Se	-42.000	81.0	-42.000	81.0	40.898667	18.7	1672.700935	349.69	764.805067	-72.434839
7	H2Te	-1.800	129.6	-1.800	129.6	81.098667	67.3	6576.993735	4529.29	5457.940267	-45.240080
8	NH3	-33.400	17.0	-33.400	17.0	49.498667	-45.3	2450.118002	2052.09	-2242.289600	-108.246868
9	PH3	-87.000	34.0	-87.000	34.0	-4.101333	-28.3	16.820935	800.89	116.067733	-98.734298
10	AsH3	-55.000	77.9	-55.000	77.9	27.898667	15.6	778.335602	243.36	435.219200	-74.169485
11	SbH3	-17.100	124.8	-17.100	124.8	65.798667	62.5	4329.464535	3906.25	4112.416667	-47.925982
12	CH4	-161.490	16.0	-161.490	16.0	-78.591333	-46.3	6176.597675	2143.69	3638.778733	-108.806431
13	SiH4	-111.800	32.1	-111.800	32.1	-28.901333	-30.2	835.287068	912.04	872.820267	-99.797468
14	GeH4	-90.000	76.6	-90.000	76.6	-7.101333	14.3	50.428935	204.49	-101.549067	-74.896916
15	SnH4	-52.000	122.7	-52.000	122.7	30.898667	60.4	954.727602	3648.16	1866.279467	-49.101064
16	He	-268.934	4.0	-268.934	4.0	-186.035333	-58.3	34609.145248	3398.89	10845.859933	-115.521187
17	Ne	-246.048	20.2	-246.048	20.2	-163.149333	-42.1	26617.704967	1772.41	6868.586933	-106.456267
18	Ar	-185.700	39.9	-185.700	39.9	-102.801333	-22.4	10568.114135	501.76	2302.749867	-95.432877
19	Kr	-152.300	83.8	-152.300	83.8	-69.401333	21.5	4816.545068	462.25	-1492.128667	-70.868063
20	Xe	-108.100	131.3	-108.100	131.3	-25.201333	69.0	635.107202	4761.00	-1738.892000	-44.288823

	y	x
mean	-82.898667	62.300000
variance	7050.465101	1883.249524
sd	83.967048	43.396423

	x,y
covariance	1053.796667
correlation	0.289197

	w	t
y = f(x) = wx + t	0.559563	-117.759439

excel['(y-f(x))**2'] = (excel['y'] - excel['f(x)'])**2
display(excel, pd.DataFrame([[mean_y, mean_x], [variance_y, variance_x], [sd_y, sd_x]], 
                            columns=['y','x'], index=['mean', 'variance', 'sd']),
        pd.DataFrame([covar_xy, corr_xy], index=['covariance', 'correlation'], columns=['x,y']),
        pd.DataFrame([[w, t]], columns=["w", "t"], index=["y = f(x) = wx + t"]))

	molecule	boiling point	molecular weight	y	x	y-mean(y)	x-mean(x)	(y-mean(y))**2	(x-mean(x))**2	(y-mean(y)) * (x-mean(x))	f(x)	(y-f(x))**2
0	HF	19.500	20.0	19.500	20.0	102.398667	-42.3	10485.486935	1789.29	-4331.463600	-106.568180	15893.185913
1	HCl	-84.900	36.5	-84.900	36.5	-2.001333	-25.8	4.005335	665.64	51.634400	-97.335391	154.638946
2	HBr	-67.000	80.9	-67.000	80.9	15.898667	18.6	252.767602	345.96	295.715200	-72.490796	30.148838
3	HI	-35.100	127.9	-35.100	127.9	47.798667	65.6	2284.712535	4303.36	3135.592533	-46.191337	123.017754
4	H2O	100.000	18.0	100.000	18.0	182.898667	-44.3	33451.922268	1962.49	-8102.410933	-107.687306	43134.016878
5	H2S	-60.700	34.1	-60.700	34.1	22.198667	-28.2	492.780802	795.24	-626.002400	-98.678342	1442.354459
6	H2Se	-42.000	81.0	-42.000	81.0	40.898667	18.7	1672.700935	349.69	764.805067	-72.434839	926.279451
7	H2Te	-1.800	129.6	-1.800	129.6	81.098667	67.3	6576.993735	4529.29	5457.940267	-45.240080	1887.040538
8	NH3	-33.400	17.0	-33.400	17.0	49.498667	-45.3	2450.118002	2052.09	-2242.289600	-108.246868	5602.053722
9	PH3	-87.000	34.0	-87.000	34.0	-4.101333	-28.3	16.820935	800.89	116.067733	-98.734298	137.693756
10	AsH3	-55.000	77.9	-55.000	77.9	27.898667	15.6	778.335602	243.36	435.219200	-74.169485	367.469139
11	SbH3	-17.100	124.8	-17.100	124.8	65.798667	62.5	4329.464535	3906.25	4112.416667	-47.925982	950.241169
12	CH4	-161.490	16.0	-161.490	16.0	-78.591333	-46.3	6176.597675	2143.69	3638.778733	-108.806431	2775.558397
13	SiH4	-111.800	32.1	-111.800	32.1	-28.901333	-30.2	835.287068	912.04	872.820267	-99.797468	144.060777
14	GeH4	-90.000	76.6	-90.000	76.6	-7.101333	14.3	50.428935	204.49	-101.549067	-74.896916	228.103133
15	SnH4	-52.000	122.7	-52.000	122.7	30.898667	60.4	954.727602	3648.16	1866.279467	-49.101064	8.403829
16	He	-268.934	4.0	-268.934	4.0	-186.035333	-58.3	34609.145248	3398.89	10845.859933	-115.521187	23535.491228
17	Ne	-246.048	20.2	-246.048	20.2	-163.149333	-42.1	26617.704967	1772.41	6868.586933	-106.456267	19485.851914
18	Ar	-185.700	39.9	-185.700	39.9	-102.801333	-22.4	10568.114135	501.76	2302.749867	-95.432877	8148.153524
19	Kr	-152.300	83.8	-152.300	83.8	-69.401333	21.5	4816.545068	462.25	-1492.128667	-70.868063	6631.160338
20	Xe	-108.100	131.3	-108.100	131.3	-25.201333	69.0	635.107202	4761.00	-1738.892000	-44.288823	4071.866330

	y	x
mean	-82.898667	62.300000
variance	7050.465101	1883.249524
sd	83.967048	43.396423

	x,y
covariance	1053.796667
correlation	0.289197

	w	t
y = f(x) = wx + t	0.559563	-117.759439

r2 = 1. - sum(excel['(y-f(x))**2']) / sum(excel['(y-mean(y))**2'])
display(excel, pd.DataFrame([[mean_y, mean_x], [variance_y, variance_x], [sd_y, sd_x]], 
                            columns=['y','x'], index=['mean', 'variance', 'sd']),
        pd.DataFrame([covar_xy, corr_xy], index=['covariance', 'correlation'], columns=['x,y']),
        pd.DataFrame([[w, t, r2]], columns=["w", "t", "R2"], index=["y = f(x) = wx + t"]))

	molecule	boiling point	molecular weight	y	x	y-mean(y)	x-mean(x)	(y-mean(y))**2	(x-mean(x))**2	(y-mean(y)) * (x-mean(x))	f(x)	(y-f(x))**2
0	HF	19.500	20.0	19.500	20.0	102.398667	-42.3	10485.486935	1789.29	-4331.463600	-106.568180	15893.185913
1	HCl	-84.900	36.5	-84.900	36.5	-2.001333	-25.8	4.005335	665.64	51.634400	-97.335391	154.638946
2	HBr	-67.000	80.9	-67.000	80.9	15.898667	18.6	252.767602	345.96	295.715200	-72.490796	30.148838
3	HI	-35.100	127.9	-35.100	127.9	47.798667	65.6	2284.712535	4303.36	3135.592533	-46.191337	123.017754
4	H2O	100.000	18.0	100.000	18.0	182.898667	-44.3	33451.922268	1962.49	-8102.410933	-107.687306	43134.016878
5	H2S	-60.700	34.1	-60.700	34.1	22.198667	-28.2	492.780802	795.24	-626.002400	-98.678342	1442.354459
6	H2Se	-42.000	81.0	-42.000	81.0	40.898667	18.7	1672.700935	349.69	764.805067	-72.434839	926.279451
7	H2Te	-1.800	129.6	-1.800	129.6	81.098667	67.3	6576.993735	4529.29	5457.940267	-45.240080	1887.040538
8	NH3	-33.400	17.0	-33.400	17.0	49.498667	-45.3	2450.118002	2052.09	-2242.289600	-108.246868	5602.053722
9	PH3	-87.000	34.0	-87.000	34.0	-4.101333	-28.3	16.820935	800.89	116.067733	-98.734298	137.693756
10	AsH3	-55.000	77.9	-55.000	77.9	27.898667	15.6	778.335602	243.36	435.219200	-74.169485	367.469139
11	SbH3	-17.100	124.8	-17.100	124.8	65.798667	62.5	4329.464535	3906.25	4112.416667	-47.925982	950.241169
12	CH4	-161.490	16.0	-161.490	16.0	-78.591333	-46.3	6176.597675	2143.69	3638.778733	-108.806431	2775.558397
13	SiH4	-111.800	32.1	-111.800	32.1	-28.901333	-30.2	835.287068	912.04	872.820267	-99.797468	144.060777
14	GeH4	-90.000	76.6	-90.000	76.6	-7.101333	14.3	50.428935	204.49	-101.549067	-74.896916	228.103133
15	SnH4	-52.000	122.7	-52.000	122.7	30.898667	60.4	954.727602	3648.16	1866.279467	-49.101064	8.403829
16	He	-268.934	4.0	-268.934	4.0	-186.035333	-58.3	34609.145248	3398.89	10845.859933	-115.521187	23535.491228
17	Ne	-246.048	20.2	-246.048	20.2	-163.149333	-42.1	26617.704967	1772.41	6868.586933	-106.456267	19485.851914
18	Ar	-185.700	39.9	-185.700	39.9	-102.801333	-22.4	10568.114135	501.76	2302.749867	-95.432877	8148.153524
19	Kr	-152.300	83.8	-152.300	83.8	-69.401333	21.5	4816.545068	462.25	-1492.128667	-70.868063	6631.160338
20	Xe	-108.100	131.3	-108.100	131.3	-25.201333	69.0	635.107202	4761.00	-1738.892000	-44.288823	4071.866330

	y	x
mean	-82.898667	62.300000
variance	7050.465101	1883.249524
sd	83.967048	43.396423

	x,y
covariance	1053.796667
correlation	0.289197

	w	t	R2
y = f(x) = wx + t	0.559563	-117.759439	0.083635

先生！できました！（ドヤ）

続編として線形重回帰を本当はPythonで解きたいけど表計算で解けと言われたのでをお楽しみください。