commit-live-students · akshaylakade · Dec 4, 2018 · Dec 26, 2018 · Dec 26, 2018 · Jan 10, 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
@@ -1,12 +1,21 @@
+# %load q01_cond_prob/build.py
 # So that float division is by default in python 2.7
 from __future__ import division
 
 import pandas as pd
+import numpy as np
 
 df = pd.read_csv('data/house_pricing.csv')
-
+data = pd.DataFrame(df)
 
 # Enter Code Here
+def cond_prob(df):
+    result = data.shape[0]
+    ho = data[data['Neighborhood'] == 'OldTown'].shape[0]
+    cp = float((ho/result)*((ho - 1)/(result - 1))*((ho - 2)/(result - 2)))
+    return cp
+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
@@ -7,7 +8,24 @@
 sample = df['GrLivArea']
 
 
-# Write your solution here :
+def confidence_interval(n):
+    n = sample.size
+    sample_mean = sample.mean()
+
+    z_critical = stats.norm.ppf(q = 0.95)  
+
+    print('z-critical value:') 
+    print(z_critical)                        
+
+    Std_Error = (sample.std())/math.sqrt(n)
+
+    est = (z_critical) * (Std_Error)
+
+    conf_interval = (sample_mean - est,
+                           sample_mean + est)
+    return conf_interval
+confidence_interval(sample)
+sample.size
 
 
 
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 +1,21 @@
+# %load q03_t_test/build.py
 # Default imports
 import scipy.stats as stats
-import pandas as pd
+import pandas as pd,numpy as np
 
 df = pd.read_csv('data/house_pricing.csv')
+df = pd.DataFrame(df)
 
-
+from statsmodels.stats.weightstats import ztest
 # Enter Code Here
+def t_statistic(df):
+    statistic, p_value = stats.ttest_1samp(a= df[df['Neighborhood'] == 'OldTown']['GrLivArea'],popmean= df['GrLivArea'].mean()) 
+    pval=np.array(p_value)
+    test_result=np.all(pval<0.01)
+    return p_value,test_result
+
+
+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 +1,26 @@
+# %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
+
+ # Enter Code Here	# 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