commit-live-students · soham009 · Dec 27, 2018 · Dec 27, 2018 · Dec 27, 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 +8,13 @@
 
 
 # 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
+
 
 
 
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
@@ -8,6 +9,20 @@
 
 
 # Write your solution here :
-
-
+def confidence_interval(sample):
+    sample_mean = sample.mean()
+
+    sample_std_dev = sample.std()
+
+    z_critical = stats.norm.ppf(q = 0.95)  # Get the z-critical value*
+
+    margin_of_error = z_critical * (sample_std_dev/math.sqrt(len(sample)))
+
+    confidence_interval = (sample_mean - margin_of_error, sample_mean + margin_of_error)
+
+    low = confidence_interval[0]
+
+    high = confidence_interval[1]
+
+    return low, high 
 
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 +1,23 @@
+# %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):
+    z_statistic, p_value = stats.ttest_1samp(a= df[df['Neighborhood'] == 'OldTown']['GrLivArea'], popmean= df['GrLivArea'].mean())
+
+    t_stat = stats.norm.ppf(0.9)
+
+    if p_value < t_stat:
+        test_result = np.False_
+    else:
+        test_result = np.True_
+
+    return p_value, test_result
+
 
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 +1,26 @@
+# %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):
+    SalesPrice = pd.qcut(df['SalePrice'], 3, labels=['High','medium','low'])
+
+    freqtab = pd.crosstab(df['LandSlope'],SalesPrice)
+
+    chi2,p_value,dof,expected = stats.chi2_contingency(freqtab)
+
+    if p_value < 0.05:
+        test_result = np.True_
+    else:
+        test_result = np.False_
+
+    return p_value, test_result
+
 
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