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## T-Tests

# Import packages import pandas as pd import scipy.stats as stats %matplotlib inline # Read in the data data = […]

## git

Further Research Git Internals – Plumbing and Porcelain (advanced – bookmark this and check it out later) Customizing Git – Git […]

## Multiple Linear Regression

In this lesson, you will be extending your knowledge of simple linear regression, where you were predicting a quantitative response […]

## Logistic Regression

Fitting Logistic Regression import numpy as np import pandas as pd import statsmodels.api as sm df = pd.read_csv(‘./fraud_dataset.csv’) df.head() 1. As […]

## Simple Linear Regression

In this lesson, you will: Identify Regression Applications Learn How Regression Works Apply Regression to Problems Using Python Machine Learning is […]

## Case Study: A/B Tests

A/B tests are used to test changes on a web page by running an experiment where a control group sees the old […]

## Hypothesis Testing

rules for setting up null and alternative hypotheses: The H_0H0 is true before you collect any data. The H_0H0 usually states there is no […]

## Confidence Intervals – Udacity

import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline np.random.seed(42) full_data = pd.read_csv(‘../data/coffee_dataset.csv’) sample_data = […]

## Statistics – Udacity

Descriptive Statistics Descriptive statistics is about describing our collected data using the measures discussed throughout this lesson: measures of center, measures of […]