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# Statistical Inference and Hypothesis Testing

Whenever we observe data, we are usually observing one or a few samples from a much larger population. For example, if we are looking at daily stock market returns for AAPL for last year, we are looking at only a small portion of the overall daily returns. More often than

# Multivariate Regression and Interpreting Regression Results

Simple linear regression (univariate regression) is an important tool for understanding relationships between quantitative data, but it has its limitations. One obvious deficiency is the constraint of one independent variable, limiting models to one factor, such as the effect of the systematic risk of a stock on its expected returns.

# Correlation, Covariance and Linear Regression

Statistical inference helps us understand the data, and hypothesis testing helps us understand if the data is different from another set of data. These techniques are important when exploring data sets, as they help us guide our analysis. However, these techniques are not enough. Most times, we are looking to