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Author: Algorithmica

2019-october/19.hypothesis testing and stats tests/anova test.py

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+##chi-square goodness of fit test
+#it tests whether the distribution of sample categorical data matches an 
+#expected distribution
+from scipy import stats
+import pandas as pd
+import os
+
+dir = 'F:/'
+titanic_train = pd.read_csv(os.path.join(dir, 'train.csv'))
+print(titanic_train.info())
+
+#anova test
+#The one-way ANOVA tests whether the mean of some numeric variable differs 
+#across the levels of one categorical variable(do any of the group means differ from one another?)
+fare_by_class1 = titanic_train.Fare[titanic_train.Pclass==1]
+fare_by_class2 = titanic_train.Fare[titanic_train.Pclass==2]
+fare_by_class3 = titanic_train.Fare[titanic_train.Pclass==3]
+
+stats.f_oneway(fare_by_class1, fare_by_class2, fare_by_class3)

2019-october/19.hypothesis testing and stats tests/chisquare test.py

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+##chi-square goodness of fit test
+#it tests whether the distribution of sample categorical data matches an 
+#expected distribution
+import numpy as np
+from scipy import stats
+import pandas as pd
+import os
+
+n_trails = 120
+n_outcomes = 6
+result = np.random.randint(1, n_outcomes+1, n_trails)
+
+outcomes, observed = np.unique(result, return_counts=True)
+for (o,f) in zip(outcomes, observed):
+    print(o,f)
+expected = np.array(n_outcomes * [n_trails/n_outcomes], dtype=np.int64)
+
+stats.chisquare(f_obs = observed, f_exp = expected)   
+
+observed = [15,29,18,19,20,19]
+observed = [20,20,20,20,20,20]
+observed = [30,10,20,20,20,20]
+observed = [10,30,30,10, 10,30]
+
+#chi-square independence test
+#The chi-squared test of independence tests whether two categorical variables 
+#are independent
+dir = 'F:/'
+titanic_train = pd.read_csv(os.path.join(dir, 'train.csv'))
+print(titanic_train.info())
+
+observed = pd.crosstab(titanic_train.Sex, titanic_train.Survived)
+stats.chi2_contingency(observed = observed)   
+
+observed = pd.crosstab(titanic_train.Pclass, titanic_train.Survived)
+stats.chi2_contingency(observed = observed)   

2019-october/19.hypothesis testing and stats tests/t test.py

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+##chi-square goodness of fit test
+#it tests whether the distribution of sample categorical data matches an 
+#expected distribution
+import numpy as np
+from scipy import stats
+import pandas as pd
+import os
+
+dir = 'F:/'
+titanic_train = pd.read_csv(os.path.join(dir, 'train.csv'))
+print(titanic_train.info())
+
+#one sample t-test
+#A one-sample t-test checks whether a sample mean differs from the population mean.
+fare_sample = titanic_train[['Fare']].sample(frac=0.6)
+stats.ttest_1samp(a = fare_sample, popmean = titanic_train[['Fare']].mean()) 
+
+#two sample t-test
+#A two-sample t-test investigates whether the means of two independent data samples 
+#differ from one another.
+fare_by_non_survived = titanic_train.Fare[titanic_train.Survived==0]
+fare_by_survived = titanic_train.Fare[titanic_train.Survived==1]
+stats.ttest_ind(a = fare_by_non_survived,
+                b = fare_by_survived,
+                equal_var=False)
+
+#paired t-test
+#testing differences between samples of the same group at different points in time.
+#a hospital might want to test whether a weight-loss drug works 
+#by checking the weights of the same group patients before and after treatment. 
+#A paired t-test lets you check whether the means of samples from the same group differ.
+before= stats.norm.rvs(scale=30, loc=250, size=100)
+after = before + stats.norm.rvs(scale=5, loc=-1.25, size=100)
+weight_df = pd.DataFrame({"weight_before":before,
+                          "weight_after":after,
+                          "weight_change":after-before})
+weight_df.describe() 
+stats.ttest_rel(a = before, b = after)
+
+#anova test
+#The one-way ANOVA tests whether the mean of some numeric variable differs 
+#across the levels of one categorical variable(do any of the group means differ from one another?)
+fare_by_class1 = titanic_train.Fare[titanic_train.Pclass==1]
+fare_by_class2 = titanic_train.Fare[titanic_train.Pclass==2]
+fare_by_class3 = titanic_train.Fare[titanic_train.Pclass==3]
+
+stats.f_oneway(fare_by_class1, fare_by_class2, fare_by_class3)