commit-live-students · NIkhila79 · Jan 27, 2019 · Jan 27, 2019 · Jan 27, 2019 · Jan 27, 2019
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):
+    total_rows = df.shape[0]
+    old_town_rows = df[df['Neighborhood'] == 'OldTown'].shape[0]
+    return (old_town_rows / total_rows) * ((old_town_rows - 1)/(total_rows-1)) * ((old_town_rows-2) / (total_rows - 2))
 
 
 
+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
@@ -8,6 +9,21 @@
 
 
 # Write your solution here :
+def confidence_interval(sample):
+    sigma = sample.std()
+    n = sample.count()
+    standard_error = sigma/math.sqrt(n)
+
+    z_value = stats.norm.ppf(q = 0.95)
+
+    estimate = z_value * standard_error
 
+    lower_limit = sample.mean() - estimate
+    upper_limit = sample.mean() + estimate
+    return lower_limit, upper_limit
+
+
+
+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 +1,23 @@
+# %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):
+    population = df['GrLivArea']
+    sample = df[df['Neighborhood'] == 'OldTown']['GrLivArea']
+
+    statistic, p_value = stats.ttest_1samp(sample, popmean= population.mean())
+
+    nullHypothesisIsCorrect = np.greater(p_value, 0.9)
+
+    return p_value, nullHypothesisIsCorrect
+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):
+    salesPrice = pd.qcut(df['SalePrice'], 3, ['High', 'Medium', 'Low'])
+    landSlope = df['LandSlope']
+    crosstab = pd.crosstab(landSlope, salesPrice)
+    chi2,pval,dof,expected = stats.chi2_contingency(crosstab)
+    return pval, np.greater(pval, 0.5)
+
+
+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