Alan Anderson

Summary statistical measures represent the key properties of a sample or population as a single numerical value. This has the advantage of providing important information in a very compact form. It also simplifies comparing multiple samples or populations. Summary statistical measures can be divided into three types: measures of central tendency, measures of central dispersion, and measures of association.

Statistics make it possible to analyze real-world business problems with actual data so that you can determine if a marketing strategy is really working, how much a company should charge for its products, or any of a million other practical questions. The science of statistics uses regression analysis, hypothesis testing, sampling distributions, and more to ensure accurate data analysis.

You can determine the relationship between two variables with two measures of association: covariance and correlation. For example, if an investor wants to understand the risk of a portfolio of stocks, then he can use these measures to properly determine how closely the returns on the stocks track each other. Covariance is used to measure the tendency for two variables to rise above their means or fall below their means at the same time.

Many different techniques have been designed to forecast the future value of a variable. Two of these are time series regression models and simulation models. Time series regression models A time series regression model is used to estimate the trend followed by a variable over time, using regression techniques.

To figure out the probability of the intersection of two events, you use the multiplication rule. This is used to determine the probability that two events are both true. For example, suppose an experiment consists of choosing a card from a standard deck. Event A = "the card is red." Event B = "the card is a king.

For a dataset that consists of observations taken at different points in time (that is, time series data), it's important to determine whether or not the observations are correlated with each other. This is because many techniques for modeling time series data are based on the assumption that the data is uncorrelated with each other (independent).

A statistic is said to be robust if it isn’t strongly influenced by the presence of outliers. For example, the mean is not robust because it can be strongly affected by the presence of outliers. On the other hand, the median is robust — it isn’t affected by outliers. For example, suppose the following data represents a sample of household incomes in a small town (measured in thousands of dollars per year): 32, 47, 20, 25, 56 You compute the sample mean as the sum of the five observations divided by five: The sample mean is $36,000 per year.

Healthcare is one area where big data has the potential to make dramatic improvements in the quality of life. The increasing availability of massive amounts of data and rapidly increasing computer power could enable researchers to make breakthroughs, such as the following: Predicting outbreaks of diseases Gaining a better understanding of the effectiveness and side effects of drugs Developing customized treatments based on patient histories Reducing the cost of developing new treatments One of the biggest challenges facing the use of big data in healthcare is that much of the data is stored in independent "silos.

Before you apply statistical techniques to a dataset, it's important to examine the data to understand its basic properties. You can use a series of techniques that are collectively known as Exploratory Data Analysis (EDA) to analyze a dataset. EDA helps ensure that you choose the correct statistical techniques to analyze and forecast the data.