diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc index 2ba0c81..46acec4 100644 Binary files a/__pycache__/__init__.cpython-36.pyc and b/__pycache__/__init__.cpython-36.pyc differ diff --git a/q01_cond_prob/__pycache__/__init__.cpython-36.pyc b/q01_cond_prob/__pycache__/__init__.cpython-36.pyc index a5c1ab2..cf93890 100644 Binary files a/q01_cond_prob/__pycache__/__init__.cpython-36.pyc and b/q01_cond_prob/__pycache__/__init__.cpython-36.pyc differ diff --git a/q01_cond_prob/__pycache__/build.cpython-36.pyc b/q01_cond_prob/__pycache__/build.cpython-36.pyc index 4654504..88d0f93 100644 Binary files a/q01_cond_prob/__pycache__/build.cpython-36.pyc and b/q01_cond_prob/__pycache__/build.cpython-36.pyc differ diff --git a/q01_cond_prob/build.py b/q01_cond_prob/build.py index 46a16ee..56d104b 100644 --- a/q01_cond_prob/build.py +++ b/q01_cond_prob/build.py @@ -1,3 +1,4 @@ +# %load q01_cond_prob/build.py # So that float division is by default in python 2.7 from __future__ import division @@ -6,7 +7,18 @@ df = pd.read_csv('data/house_pricing.csv') -# Enter Code Here +def cond_prob(df): + + # number of ho + total_house_in_oldtown = len(df[df['Neighborhood']== 'OldTown']) # number of house in oldtown + total_house = len(df) # total number of house + + #calculate conditional probability + cond_probability = ((total_house_in_oldtown)/(total_house))*((total_house_in_oldtown-1)/(total_house-1)) * ((total_house_in_oldtown-2)/(total_house-2)) + + return cond_probability + +cond_prob(df) diff --git a/q01_cond_prob/tests/__pycache__/__init__.cpython-36.pyc b/q01_cond_prob/tests/__pycache__/__init__.cpython-36.pyc index 9e8f52b..f05ebaa 100644 Binary files a/q01_cond_prob/tests/__pycache__/__init__.cpython-36.pyc and b/q01_cond_prob/tests/__pycache__/__init__.cpython-36.pyc differ diff --git a/q01_cond_prob/tests/__pycache__/test_q01_cond_prob.cpython-36.pyc b/q01_cond_prob/tests/__pycache__/test_q01_cond_prob.cpython-36.pyc index e8852e9..06210e6 100644 Binary files a/q01_cond_prob/tests/__pycache__/test_q01_cond_prob.cpython-36.pyc and b/q01_cond_prob/tests/__pycache__/test_q01_cond_prob.cpython-36.pyc differ diff --git a/q02_confidence_interval/__pycache__/__init__.cpython-36.pyc b/q02_confidence_interval/__pycache__/__init__.cpython-36.pyc index 741ad2d..a895e86 100644 Binary files a/q02_confidence_interval/__pycache__/__init__.cpython-36.pyc and b/q02_confidence_interval/__pycache__/__init__.cpython-36.pyc differ diff --git a/q02_confidence_interval/__pycache__/build.cpython-36.pyc b/q02_confidence_interval/__pycache__/build.cpython-36.pyc index b478df2..3a5788c 100644 Binary files a/q02_confidence_interval/__pycache__/build.cpython-36.pyc and b/q02_confidence_interval/__pycache__/build.cpython-36.pyc differ diff --git a/q02_confidence_interval/build.py b/q02_confidence_interval/build.py index 023b81e..85a81bc 100644 --- a/q02_confidence_interval/build.py +++ b/q02_confidence_interval/build.py @@ -1,3 +1,4 @@ +# %load q02_confidence_interval/build.py # Default imports import math import scipy.stats as stats @@ -8,6 +9,19 @@ # Write your solution here : +def confidence_interval(df): + + sample_mean= sample.mean() #find the mean of sample + z_critical = stats.norm.ppf(0.95) # z-value + sample_dev = sample.std() # stardard deviation of sample + standard_error = z_critical * (sample_dev/(len(sample))**0.5) #standa + lower_limit = sample_mean - standard_error + upper_limit = sample_mean + standard_error + + return lower_limit, upper_limit +confidence_interval(df) + + diff --git a/q02_confidence_interval/tests/__pycache__/__init__.cpython-36.pyc b/q02_confidence_interval/tests/__pycache__/__init__.cpython-36.pyc index 2eb0cc4..df041a4 100644 Binary files a/q02_confidence_interval/tests/__pycache__/__init__.cpython-36.pyc and b/q02_confidence_interval/tests/__pycache__/__init__.cpython-36.pyc differ diff --git a/q02_confidence_interval/tests/__pycache__/test_q02_confidence_interval.cpython-36.pyc b/q02_confidence_interval/tests/__pycache__/test_q02_confidence_interval.cpython-36.pyc index c3788ca..696728a 100644 Binary files a/q02_confidence_interval/tests/__pycache__/test_q02_confidence_interval.cpython-36.pyc and b/q02_confidence_interval/tests/__pycache__/test_q02_confidence_interval.cpython-36.pyc differ diff --git a/q03_t_test/__pycache__/__init__.cpython-36.pyc b/q03_t_test/__pycache__/__init__.cpython-36.pyc index cac7d29..c419c1d 100644 Binary files a/q03_t_test/__pycache__/__init__.cpython-36.pyc and b/q03_t_test/__pycache__/__init__.cpython-36.pyc differ diff --git a/q03_t_test/__pycache__/build.cpython-36.pyc b/q03_t_test/__pycache__/build.cpython-36.pyc index d55dfcf..5562c19 100644 Binary files a/q03_t_test/__pycache__/build.cpython-36.pyc and b/q03_t_test/__pycache__/build.cpython-36.pyc differ diff --git a/q03_t_test/build.py b/q03_t_test/build.py index f966b62..5b5ff11 100644 --- a/q03_t_test/build.py +++ b/q03_t_test/build.py @@ -1,9 +1,31 @@ +# %load q03_t_test/build.py # Default imports import scipy.stats as stats import pandas as pd +import numpy as np df = pd.read_csv('data/house_pricing.csv') +def t_statistic(df): + # Enter Code Here + # alpha 0.05 for 95% significance level, its means 5% risk + # alpha 0.10 for 90% significance level, its means 10% risk + alpha = 0.10 + t_test, p_value = stats.ttest_1samp(df[df['Neighborhood' ]=='OldTown']['GrLivArea'], df['GrLivArea'].mean()) + + if p_value < alpha: + + return p_value, np.bool_(True) + else: + + return p_value, np.bool_(False) + + +t_statistic(df) + + + + + -# Enter Code Here diff --git a/q03_t_test/tests/__pycache__/__init__.cpython-36.pyc b/q03_t_test/tests/__pycache__/__init__.cpython-36.pyc index c489290..19eed31 100644 Binary files a/q03_t_test/tests/__pycache__/__init__.cpython-36.pyc and b/q03_t_test/tests/__pycache__/__init__.cpython-36.pyc differ diff --git a/q03_t_test/tests/__pycache__/test_q03_t_test.cpython-36.pyc b/q03_t_test/tests/__pycache__/test_q03_t_test.cpython-36.pyc index ffd3551..09d979f 100644 Binary files a/q03_t_test/tests/__pycache__/test_q03_t_test.cpython-36.pyc and b/q03_t_test/tests/__pycache__/test_q03_t_test.cpython-36.pyc differ diff --git a/q04_chi2_test/__pycache__/__init__.cpython-36.pyc b/q04_chi2_test/__pycache__/__init__.cpython-36.pyc index 07afcf0..4520460 100644 Binary files a/q04_chi2_test/__pycache__/__init__.cpython-36.pyc and b/q04_chi2_test/__pycache__/__init__.cpython-36.pyc differ diff --git a/q04_chi2_test/__pycache__/build.cpython-36.pyc b/q04_chi2_test/__pycache__/build.cpython-36.pyc index 699bd6a..f4793f3 100644 Binary files a/q04_chi2_test/__pycache__/build.cpython-36.pyc and b/q04_chi2_test/__pycache__/build.cpython-36.pyc differ diff --git a/q04_chi2_test/build.py b/q04_chi2_test/build.py index 4f20455..c3fab26 100644 --- a/q04_chi2_test/build.py +++ b/q04_chi2_test/build.py @@ -1,10 +1,30 @@ +# %load q04_chi2_test/build.py # Default imports import scipy.stats as stats import pandas as pd +import numpy as np df = pd.read_csv('data/house_pricing.csv') # Enter Code Here +def chi_square(df): + + #price converted into catogrical on basic of quantile + price = pd.qcut(df['SalePrice'],3, labels =['low', 'medium', 'High']) + + #contingency table group variable to show correlation between two varable + freq_table = pd.crosstab(df.LandSlope, price) + + #calculting chi and p value from + chi, p, dof, expected = stats.chi2_contingency(freq_table) + + if p < 0.05: # if value of p is very low than variable are independent + return p, np.bool_(True) + else: + return p, np.bool_(False) #else not independent + +chi_square(df) + diff --git a/q04_chi2_test/tests/__pycache__/__init__.cpython-36.pyc b/q04_chi2_test/tests/__pycache__/__init__.cpython-36.pyc index 45a1b92..8a6684b 100644 Binary files a/q04_chi2_test/tests/__pycache__/__init__.cpython-36.pyc and b/q04_chi2_test/tests/__pycache__/__init__.cpython-36.pyc differ diff --git a/q04_chi2_test/tests/__pycache__/test_q04_chi2_test.cpython-36.pyc b/q04_chi2_test/tests/__pycache__/test_q04_chi2_test.cpython-36.pyc index b2a8c04..4e4e728 100644 Binary files a/q04_chi2_test/tests/__pycache__/test_q04_chi2_test.cpython-36.pyc and b/q04_chi2_test/tests/__pycache__/test_q04_chi2_test.cpython-36.pyc differ