# All posts by Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics Andrey Kamenov is a data scientist working for Advameg Inc. His background includes teaching statistics, stochastic processes and financial mathematics in Moscow State University and working for a hedge fund. His academic interests range from statistical data analysis to optimal stopping theory. Andrey also enjoys his hobbies of photography, reading and powerlifting.

# Are people living outside the metro areas cushioned against falling home prices?

Andrey Kamenov, Ph.D. Probability and Statistics

The U.S. Census Bureau provides us with a lot of interesting data about new homes in their Survey of Construction. Today we look at the sale prices of homes in regard to their location (i.e. whether or not they are located in a Metropolitan Statistical Area).

# Owners prefer to build small homes outside of metro areas

Andrey Kamenov, Ph.D. Probability and Statistics

As anyone who has looked to buy a new house likely knows, there are basically four types of houses. Some are built for sale or for rent, and some are built by owners, either with help from a contractor or by themselves. The U.S. Census Bureau’s Survey of Construction provides us with this data on each surveyed home.

# Exercise or sleep: which is more important for your health?

Andrey Kamenov, Ph.D. Probability and Statistics

A lot of people exercise first thing in the morning. With many of them leading quite busy lives, this often means getting less sleep. And while nearly everyone understands the benefits of regular exercise for their health, the sleep aspect is also quite important.

But is it actually more important? We are not talking about people who don’t get enough sleep even without trying to fit gym sessions into their schedule, but maybe you should ditch that morning workout if it puts you under the coveted seven hours of sleep?

# Measuring health levels in the U.S.

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:

# Where to be born 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?

# Locally popular baby names

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.

# What were the trending baby names in the past decades?

Andrey Kamenov, Ph.D. Probability and Statistics

The most popular baby names do not change frequently — Michael was on the top of the list from the 1960s until it was passed by Jacob in the 2000s. Mary was the most popular name for girls for the entire first half of the century.

On the other hand, it’s still interesting to see which names rapidly gained popularity in the past years. Since the official Social Security website provides data from Social Security card applications for each state, we can trace the geographical nature of such changes.

# Finished basements are becoming more popular, especially in large houses

Andrey Kamenov, Ph.D. Probability and Statistics

According to the U.S. Census Bureau’s Survey of Construction, the number of new houses with basements is declining across the U.S., down by almost a quarter from 40 percent of new homes in 2000 to 32 percent in 2013. Surprisingly, not only did the relative number of finished basements rise, but there has also been an increase in the absolute percentage: 9 percent of all new homes in the U.S. now have a finished basement.

# Top 10 cities for professionals in the U.S.

Andrey Kamenov, Ph.D. Probability and Statistics

What are the top cities for professionals? Should you move to the opposite coast in search of a larger paycheck? Or maybe head to Houston or Chicago?

Below is an interactive map showing the top 10 cities in the country by average payroll for the entire sector as well as for each of the subsectors (as defined by the NAICS). It also shows the relative change since 2010.

# The “half your age plus seven” rule — how often is it broken?

Andrey Kamenov, Ph.D. Probability and Statistics

When is an age difference between partners a little too large? A common rule is that you cannot date someone younger than half your age plus seven. For example, if you are 30, dating someone 22 years old is just barely acceptable under this rule.

The following chart illustrates this concept: Couples between the dashed lines are the ones following the rule.