commit-live-students · akashhchatterjee · Oct 21, 2018 · Oct 21, 2018 · Oct 22, 2018 · Oct 24, 2018
diff --git a/__pycache__/__init__.cpython-36.pyc b/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_cond_prob/__pycache__/__init__.cpython-36.pyc b/q01_cond_prob/__pycache__/__init__.cpython-36.pyc
diff --git a/q01_cond_prob/__pycache__/build.cpython-36.pyc b/q01_cond_prob/__pycache__/build.cpython-36.pyc
diff --git 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
 
@@ -7,6 +8,10 @@
 
 
 # Enter Code Here
-
-
+def cond_prob(df):
+    all_houses = df.shape[0]
+    houses_in_OldTown = df[df['Neighborhood'] == 'OldTown'].shape[0]
+    conditional_prob = (houses_in_OldTown/all_houses) * ((houses_in_OldTown - 1)/(all_houses - 1)) * ((houses_in_OldTown - 2)/(all_houses - 2))
+    return conditional_prob
+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
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
diff --git a/q02_confidence_interval/__pycache__/__init__.cpython-36.pyc b/q02_confidence_interval/__pycache__/__init__.cpython-36.pyc
diff --git a/q02_confidence_interval/__pycache__/build.cpython-36.pyc b/q02_confidence_interval/__pycache__/build.cpython-36.pyc
diff --git 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
@@ -6,8 +7,15 @@
 df = pd.read_csv('data/house_pricing.csv')
 sample = df['GrLivArea']
 
-
 # Write your solution here :
-
-
+def confidence_interval(sample):
+    sample_size = 1460
+    sample_mean = sample.mean()
+    z_critical = stats.norm.ppf(q = 0.95)
+    stan_dev = sample.std()
+    estimate = z_critical * (stan_dev/math.sqrt(sample_size))
+    conf_inter = (sample_mean - estimate,
+                       sample_mean + estimate)
+    return conf_inter
+confidence_interval(sample)
 
diff --git a/q02_confidence_interval/tests/__pycache__/__init__.cpython-36.pyc b/q02_confidence_interval/tests/__pycache__/__init__.cpython-36.pyc
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
diff --git a/q03_t_test/__pycache__/__init__.cpython-36.pyc b/q03_t_test/__pycache__/__init__.cpython-36.pyc
diff --git a/q03_t_test/__pycache__/build.cpython-36.pyc b/q03_t_test/__pycache__/build.cpython-36.pyc
diff --git a/q03_t_test/build.py b/q03_t_test/build.py
@@ -1,9 +1,19 @@
+# %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')
 
 
 # Enter Code Here
+def t_statistic(df):
+    t_test,pval = stats.ttest_1samp(a= df[df['Neighborhood'] == 'OldTown']['GrLivArea'],
+                               popmean = df['GrLivArea'].mean())
+    if pval < 0.005:
+        t_test = np.True_
+    else:
+        test = np.False_
+    return pval, test
+t_statistic(df)
 
diff --git a/q03_t_test/tests/__pycache__/__init__.cpython-36.pyc b/q03_t_test/tests/__pycache__/__init__.cpython-36.pyc
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
diff --git a/q04_chi2_test/__pycache__/__init__.cpython-36.pyc b/q04_chi2_test/__pycache__/__init__.cpython-36.pyc
diff --git a/q04_chi2_test/__pycache__/build.cpython-36.pyc b/q04_chi2_test/__pycache__/build.cpython-36.pyc
diff --git a/q04_chi2_test/build.py b/q04_chi2_test/build.py
@@ -1,10 +1,21 @@
+# %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 = pd.qcut(df['SalePrice'], 3, labels = ['High', 'Medium', 'Low'])
+    freqtab = pd.crosstab(df.LandSlope,price)
+    chi2,pval,dof,expected = stats.chi2_contingency(freqtab)
+
+    if pval < 0.005:
+        chi2 = np.True_
+    else:
+        chi2 = np.False_
+    return pval, chi2
 
+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
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