Andrey Kamenov, Ph.D. Probability and Statistics

A question in a survey conducted by the Centers for Disease Control and Prevention asked a respondent to assess his or her own health. Just one in every six people considered their own health “excellent;” most stuck to “very good” or even just “good.” But how do we use this data to visualize the relationship between health levels and other factors?

The problem is, the scale itself is not quantitative. It doesn’t explicitly state how much better “good” is than “fair.”

In order to measure the health levels of specific groups in the general population, we’ll use a quite common procedure. It provides values between 0 percent (meaning all respondents have poor health) and 100 percent (where everyone has excellent health). The exact values of everything in between are based on the (empirical) nationwide percentages.

Let’s see how our health index fares against two of the most obvious factors. The first chart shows the speed of peoples’ self-perceived decline in health with age:

Continue reading Measuring health levels in the U.S. →

Alexander Fishkov, Ph.D. student Computer Science

Personal income in the U.S. has been constantly rising in recent years — personal income per capita increased by 3.5 percent in 2015, according to data from the U.S. Bureau of Economic Analysis. We used this data, along with CPI data from the U.S. Bureau of Labor Statistics, to calculate inflation-adjusted personal income changes for different areas. The dollar amounts in this post correspond to 2016 dollars.

Continue reading Rise of personal income in the U.S. →

Andrey Kamenov, Ph.D. Probability and Statistics

Have you ever seen “quality of life” comparisons between different countries? There are many ways to measure such things (some involving much more guesswork than the others). Among the most prominent is the Economist’s where-to-be-born index, which we have thoroughly discussed in previous posts.

There are many fewer studies on how quality of life differs between the states in our country. Moreover, this is not an empty question: California alone could be the fifth-largest economy in the world. Wouldn’t it be logical to assume that life in North Dakota may be somewhat different?

Continue reading Where to be born in the U.S. →

Pavel Prikhodko, Ph.D. Machine Learning

According to information published by the United States Environmental Protection Agency, American people generated about 254 million tons of trash and recycled around 87 million tons of material in 2013. In terms of actively recycling and composting waste, the United States falls far behind other countries. The U.S. recycles about 35 percent of all its municipal waste.  Germany leads the world in this regard, composting or recycling 65 percent of all its municipal waste. South Korea takes second place with a recycling rate of 59 percent.

Continue reading Waste recycling and composting →

Pavel Prikhodko, Ph.D. Machine Learning

In the past few decades, there has been a significant increase in the number of motorcycle sales and registrations in the United States. Motorcycle registrations in the United States have grown by 75 percent, from 3,826,373 in 1997 to 6,678,958 in 2006 (according to the United States Department of Transportation). If we examine the information showing the number of registered motorcycles (including private, commercial and publicly owned) published at Statista.com in 2013, we see that California had the highest number of registered motorcycles (799,990). Florida came in second with 545,452 registered motorcycles. In 2013, there were 443,856 motorcycles registered in Texas. In Ohio and Pennsylvania, the numbers of registered motorcycles amounted to 402,264 and 400,908 motorcycles respectively.

Continue reading Motorcycle industry in the United States →

Pavel Prikhodko, Ph.D. Machine Learning

Sleep deprivation can truly be a problem for everyone. According to the information at Statista.com, nearly 8 percent of adults who are living without children took medication to sleep four or more times in the past week. In 2013-2014, it was found that 6.2 percent of adult men and 9.7 percent of adult women took medication to sleep. If we look at single parents, almost 7.5 percent took medication to sleep four or more times in the past week.

According to Statista.com, about 44.5 percent of the surveyed people had slight troubles with sleeping. During the research, about 24 percent of the respondents revealed that they have moderate trouble sleeping. Over 15 percent said that they have a lot of trouble sleeping, while 14 percent said they have no trouble sleeping. A small portion (2 percent) revealed their extreme troubles with sleeping.

Continue reading Problems with sleeping in the U.S. →

Pavel Prikhodko, Ph.D. Machine Learning

The American bar, tavern and nightclub industry generated $24.35 billion in income in 2015. It grows slightly each year; in comparison, the revenue of the industry was only $19.14 billion in 2003. By the end of 2016, the industry’s revenue will be approximately $25 billion according to a forecast published on Statista.com. In 2017, the income of U.S. bars and nightclubs is forecasted to reach $25.74 billion.

Continue reading Nightlife in America: bars, taverns and clubs →

Andrey Kamenov, Ph.D. Probability and Statistics

The most popular baby names change frequently. We saw earlier which names were trending during the past decades. Usually, the growth in popularity is widespread — the most popular names are very common in every state.

But for some names, this isn’t the case. According to the official Social Security website data, almost all names exhibit some geographical patterns, though we are interested in those where such patterns are the most evident.

Continue reading Locally popular baby names →

Alexander Fishkov, Ph.D. student Computer Science

In this post, we analyze migration data provided by the IRS.  In an earlier post, we explored the nationwide characteristics of relocation. This time we focus on a lower level and study which states are the most popular relocation destinations for people across the U.S.

From the following map, we can see that most people relocate locally, usually to the neighboring states. The obvious exceptions are Alaska and Hawaii, but even for these states, the distribution of out-migrants is non-uniform.

Continue reading Relocations: Will you leave NYC for California? →

Alexander Fishkov, Ph.D. student Computer Science

Every year, millions of people in the U.S. relocate for various reasons: a job offer, family circumstances or simply striving for a change of scenery. To study relocation patterns we analyzed migration data provided by IRS. The data is based on taxpayers’ reports and is provided at the county and state level. In this post, we will focus on migration between states.

Continue reading Relocation patterns: recovery after recession? →