## Try This At Home

We are all awash in statistics. Every day, we see the probability of precipitation, the results of opinion polls, changes in the stock market, your grades in school, or the batting average of the baseball team you follow. It’s surprising, then, that many people believe that data analysis is something you do only in school or at work. The evolution of computer hardware and software that has fed the growth of statistics didn’t stop at the door to your office or school. Why should your use of statistics stop there?

No skill improves without practice. You can practice your data analysis skills at home without making it feel like homework. Start with something you love to do, like your favorite hobby or interest. Design a study to answer some question that is interesting to you. Collect the data and then do your analysis and see what happens. Here are eight ideas for how to do that.

### Personal Behaviors

Ever wonder where the time goes? Time is a major component of many data analyses, so what better place to start than an analysis of your own time. Keep a timesheet of what you do each day for at least a month. For example, categorize how you spend your day into work/school, commuting, chores, errands, sleep, and personal time. Before you start, write down how you think you allot your time to each category. Then calculate the percentages from your data. How close are your predictions to the actual percentages? Do the percentages change much from day to day? Do they vary by day of the week?

There may also be some specific activities you might want to collect data on, like how much you smoke, drink, do drugs, look at porn, gamble, curse, and watch reality TV. Keep this data in a hidden directory on your computer.

Don’t make me hungry. You wouldn’t like me when I’m hungry.

### Consumption

Have you ever been on a diet and kept a food diary? You can expand this concept to create a dataset. Convert the types and amounts of foods you eat in a day into estimates of calories. Get a pedometer to estimate your exercise. Record your weight. Then see if you can see any correlations between your weights, the foods and calories you eat, your exercise, the season, and anything else you record. You might find the information quite valuable.

You may already be keeping track of your car’s mileage and fuel costs. It’s a good way to see the effects of driving styles, seasons, maintenance, and other factors on your miles per gallon. If you keep your household financial data on software, like Quicken, you can do many analyses and graphs of your spending patterns. For example, do you spend more on lattes than laundry?

If you have a cell phone, put your usage records in a spreadsheet. Figure out the minimum, maximum, and average amount of time you spend on the phone in a day. Who do you talk to the most often and for the longest duration? What is your most connected time of day and day of the week? Save these records so your family can sue Nokia in thirty years after you die from brain cancer.

Other good sources of data you can analyze are your utility bills. Some utility companies will report your past year of electricity, gas, oil, and water consumption, as well as some supporting information such as average temperature. In fact, they may have many years of your energy usage data that they can retrieve for you. You can use the data to test the effects of seasons, vacations, holidays, energy conservation measures, and more significant lifestyle changes, like the kids finally moving out.

### Screen Time

Do you relax by watching TV, surfing the Internet, playing video games, or all three? Keep a log of how much time you spend in front of a view screen. You might record date, day of the week, the weather, hours watching TV, hours surfing the Internet, hours playing video games, hours sleeping, and so on, every day for a month. What is the average proportion of your day that you spend looking at a view screen? Does it vary by day of the week or by weather? At the end of a month, revise your data collection to look at other ways you spend your time? Does the act of collecting the data influence how you spend your free time?

### Hobbies

Everybody has hobbies and interests that they enjoy, so why not use your favorite pastimes as opportunities to design statistical studies and collect data you can practice analyzing. Here are a few ideas for what you might do.

• Hunting and Fishing – Record where you hunt or fish, what bait or other aids you use, the time, the weather, and what results you had. Likewise with treasure hunting, record where you search, what detector settings you use, the time, the weather, and what results you had. Are there any notable patterns?
• Gardening—Keep a diary (or better, a spreadsheet) of how much time you spend in your garden, what you do, and the weather. What proportions of you time do you spend planting, weeding, maintaining, and harvesting? How do the percentages change with the date and the weather? If you plant seeds, do you get similar germination rates for the same plant from different suppliers?
• Reading—Keep a log of what you read and when you read it. How much of your reading is for enjoyment versus work? What are your reading preferences? Format (books, ebooks, magazines)? Genre (e.g., nonfiction, science fiction, religion, mystery, romance)? Are there differences in how fast you read different genre or formats?
• Music—Build a database of music; music you like and music you don’t like. Include variables like genre, length, artist, year released, theme of lyrics, instruments, time, key, and so on. Set up a rating scale for each song as the dependent variables and see if you can find patterns that explain why you like or dislike the music that you do. Extend your findings to artists you haven’t listened to before. You may even find something unexpected, like Prisencolinensinainciusol.

### Kids and Other Pets

If you have a youngster in the family, start early recording height (length) and weight. Don’t just make marks on a doorframe; set up a spreadsheet to organize your data. Do this daily for a few months. How much variation in height and weight occurs from day to day? Is the variation natural or attributable to how you measure the variables? Graph the data over time. Are there changes in growth rates? How do the height and weight compare to standards for the age and species? How much can you change the frequency of data collection without losing the resolution you need to see changes?

### Medical Conditions

If you have any chronic medical condition, start collecting relevant data using equipment you can find at most drug stores. For example, you might record your weight, heart rate, blood glucose, blood pressure, and temperature. Be sure to note the date and time of each measurement. You can also record qualitative variables like what and when you ate, how you feel, what exercise you did, and so on. Put the data in a graph and show your Doctor on your next visit. She or he may be impressed enough to prescribe you some medical marijuana.

### Sports

No matter how you like sports—professional, amateur, personal, or fantasy—you’ll always be served a side dish of statistics. Relish the experience by analyzing data in ways no one else has. Google sabermetrics to see what I mean. Figure out what baseball player is paid the most per hit. What basketball player scores the most points per minute played? Is there a relationship between height and the number of catches a receiver makes? You can find data for almost every sport imaginable on the Internet, no vuvuzela needed.

### Politics

Don’t get me going on politics. Suffice it to say that you could spend a lifetime and not analyze all the data that is currently available for free from government web sites. If you come up with anything good, write a blog about it. Most political blogs are fanatical fluff made of anti-data. Annihilate them with a real analysis.

Read more about using statistics at the Stats with Cats blog. Join other fans at the Stats with Cats Facebook group and the Stats with Cats Facebook page. Order Stats with Cats: The Domesticated Guide to Statistics, Models, Graphs, and Other Breeds of Data Analysis at Wheatmark, amazon.combarnesandnoble.com, or other online booksellers.