HIHO – quarto-input13f285f2

import numpy as np
import pandas as pd

n_cups = 20
n_milk_first = 10
opposite = n_cups - n_milk_first
P = np.concatenate((np.ones(n_milk_first),np.zeros(opposite)),axis=0) #axis=0

B = 100000

milk_estimates = pd.DataFrame({"milk_estimate":np.zeros(B)}) #딕셔너리를 데이터로 전달
for i in range(B):
    milk_first_choice = np.random.choice(P,n_milk_first,replace=False)
    milk_estimates.loc[i,"milk_estimate"] = np.sum(milk_first_choice)
milk_estimates.head(10)

	milk_estimate
0	6.0
1	6.0
2	4.0
3	5.0
4	6.0
5	5.0
6	4.0
7	5.0
8	4.0
9	5.0

milk_estimates.plot.hist()

<AxesSubplot:ylabel='Frequency'>

observed_val = 8
a,b = milk_estimates[milk_estimates["milk_estimate"] >= observed_val].shape
a

p_value = a/B
p_value

0.01139

n_cups = 8
n_milk_first = 4
opposite = n_cups - n_milk_first
P = np.concatenate((np.ones(n_milk_first),np.zeros(opposite)))
observed_val = 4

B = 1000000
estimates = pd.DataFrame({"milk_estimates":np.zeros(B)})
for i in range(B):
    sample_milk = np.random.choice(P,n_milk_first,replace=False)
    estimates.loc[i,"milk_estimates"] = np.sum(sample_milk)

a,b = estimates[estimates["milk_estimates"]>=observed_val].shape
p_value = a/B
print(p_value)

0.014372

B = 100000

observed_val = 620
sample = np.random.binomial(1000,0.54,B)
p_value = np.sum(sample >= observed_val)/B
p_value

0.0

np.random.binomial?

import scipy.stats as stats
binomial_dist = stats.binom(1000,0.54)
1 -binomial_dist.cdf(619)

1.929786053178617e-07

url = "https://ilovedata.github.io/teaching/bigdata2/data/physical_test_2018_data.csv"
physical_data = pd.read_csv(url)
twogroup = physical_data[["TEST_AGE",   "TEST_SEX", "ITEM_F001",    "ITEM_F002"]].rename(columns = {"ITEM_F001":"height","ITEM_F002":"weight"})
twogroup.head(10)

	TEST_AGE	TEST_SEX	height	weight
0	33	M	159.2	57.2
1	48	F	155.8	52.9
2	22	M	175.2	96.2
3	29	M	178.7	79.4
4	31	F	160.1	50.2
5	23	F	157.8	60.1
6	11	M	165.5	60.3
7	24	M	174.9	74.5
8	18	M	181.0	71.3
9	41	F	160.6	72.7

#randomnization
a,b = twogroup.shape
treatment = np.array([np.random.choice(["A","B"]) for i in np.arange(a)])
twogroup["treatment"] = treatment
twogroup.head(10)

	TEST_AGE	TEST_SEX	height	weight	treatment
0	33	M	159.2	57.2	A
1	48	F	155.8	52.9	B
2	22	M	175.2	96.2	B
3	29	M	178.7	79.4	A
4	31	F	160.1	50.2	B
5	23	F	157.8	60.1	B
6	11	M	165.5	60.3	A
7	24	M	174.9	74.5	B
8	18	M	181.0	71.3	A
9	41	F	160.6	72.7	B

filename = "https://ilovedata.github.io/teaching/bigdata2/data/drug.csv"
three_drug_wide = pd.read_csv(filename)
three_drug_wide

	Placebo	Old	New
0	31	23	23
1	28	17	17
2	34	29	11
3	36	23	11
4	33	17	9
5	27	17	16
6	39	22	16
7	25	17	14
8	23	23	17
9	29	25	18
10	36	17	10
11	36	24	7
12	32	19	14
13	30	18	24
14	33	5	18
15	39	26	17
16	30	33	14
17	32	16	14
18	30	20	16
19	28	27	11

three_drug_long = three_drug_wide.melt(value_vars = ["Placebo","Old","New"],value_name = "Value",var_name = "treatment")

신약그룹이 기존약 그룹보다 효과가 있는가?

귀무가설 : 효과없다.

$H_{0} : {\bar{x}}_{o l d} - {\bar{x}}_{n e w} = 0$

데이터 관측 & 검정통계량 설정

${\bar{x}}_{o l d} - {\bar{x}}_{n e w} = 0$

def diff_mean(df,treatment):
    twodrug = df.loc[(df.treatment == treatment[0]) | (df.treatment == treatment[1])].reset_index(drop=True) #reset_index 반드시 해주기
    group_mean = twodrug.groupby("treatment").mean().reset_index()
    print(group_mean)
    test_stat = float(group_mean.loc[group_mean.treatment == treatment[0],"Value"]) - float(group_mean.loc[group_mean.treatment == treatment[1],"Value"])
    return test_stat
diff_mean(three_drug_long,["Old","New"])

  treatment  Value
0       New  14.85
1       Old  20.90

6.049999999999999

귀무가설 하에서의 분포 설정

cond = (three_drug_long.treatment == "Old")|(three_drug_long.treatment == "New")
twodrug = three_drug_long[cond].reset_index(drop=True)
random_permuted_treat = np.array(twodrug.treatment.sample(frac=1.0,replace=False))
two_drug_permuted = twodrug
two_drug_permuted["permuted_treat"] = random_permuted_treat
two_drug_permuted

	treatment	Value	permuted_treat
0	Old	23	Old
1	Old	17	New
2	Old	29	Old
3	Old	23	New
4	Old	17	New
5	Old	17	Old
6	Old	22	Old
7	Old	17	Old
8	Old	23	Old
9	Old	25	Old
10	Old	17	New
11	Old	24	Old
12	Old	19	Old
13	Old	18	New
14	Old	5	Old
15	Old	26	New
16	Old	33	New
17	Old	16	New
18	Old	20	New
19	Old	27	New
20	New	23	New
21	New	17	New
22	New	11	New
23	New	11	Old
24	New	9	Old
25	New	16	New
26	New	16	Old
27	New	14	Old
28	New	17	Old
29	New	18	Old
30	New	10	Old
31	New	7	New
32	New	14	New
33	New	24	Old
34	New	18	Old
35	New	17	New
36	New	14	New
37	New	14	New
38	New	16	New
39	New	11	Old