1.1 What is Statistics?

You are exposed to statistics regularly. If you are a sports fan, then you have the statistics for your favorite player. If you are interested in politics, then you look at the polls to see how people feel about certain issues or candidates. If you are an environmentalist, then you research arsenic levels in the water of a town or analyze the global temperatures. If you are in the business profession, then you may track the monthly sales of a store or use quality control processes to monitor the number of defective parts manufactured. If you are in the health profession, then you may look at how successful a procedure is or the percentage of people infected with a disease. There are many other examples from other areas. To understand how to collect data and analyze it, you need to understand what the field of statistics is and the basic definitions.

There are two branches of statistics. One is called descriptive statistics, which is where you collect and organize data. The other is called inferential statistics, which is where you analyze and interpret data. First you need to look at descriptive statistics since you will use the descriptive statistics when making inferences.

To understand how to create descriptive statistics and then conduct inferences, there are a few definitions that you need to look at. Note, many of the words that are defined have common definitions that are used in non-statistical terminology. In statistics, some have slightly different definitions. It is important that you notice the difference and utilize the statistical definitions.

The first thing to decide in a statistical study is whom you want to measure and what you want to measure. You always want to make sure that you can answer the question of whom you measured and what you measured. The who is known as the individual and the what is the variable.

If you put the individual and the variable into one statement, then you obtain a population.

Notice, the population answers who you want to measure and what you want to measure. Make sure that your population always answers both of these questions. If it doesn’t, then you haven’t given someone who is reading your study the entire picture. As an example, if you just say that you are going to collect data from the senators in the U.S. Congress, you haven’t told your reader want you are going to collect. Do you want to know their income, their highest degree earned, their voting record, their age, their political party, their gender, their marital status, or how they feel about a particular issue? Without telling what you want to measure, your reader has no idea what your study is actually about.

Sometimes the population is very easy to collect. Such as if you are interested in finding the average age of all of the current senators in the U.S. Congress, there are only 100 senators. This wouldn’t be hard to find. However, if instead you were interested in knowing the average age that a senator in the U.S. Congress first took office for all senators that ever served in the U.S. Congress, then this would be a bit more work. It is still doable, but it would take a bit of time to collect. But what if you are interested in finding the average diameter of breast height of all of the Ponderosa Pine trees in the Coconino National Forest? This would be impossible to actually collect. What do you do in these cases? Instead of collecting the entire population, you take a smaller group of the population, kind of a snap shot of the population. This smaller group is called a sample.

How you collect your sample can determine how accurate the results of your study are. There are many ways to collect samples. Some of them create better samples than others. No sampling method is perfect, but some are better than others. Sampling techniques will be discussed later. For now, realize that every time you take a sample you will find different data values. The sample is a snapshot of the population, and there is more information than is in the picture. The idea is to try to collect a sample that gives you an accurate picture, but you will never know for sure if your picture is the correct picture. Unlike previous mathematics classes where there was always one right answer, in statistics there can be many answers, and you don’t know which are right.

Once you have your data, either from a population or a sample, you need to know how you want to summarize the data. As an example, suppose you are interested in finding the proportion of people who like a candidate, the average height a plant grows to using a new fertilizer, or the variability of the test scores. Understanding how you want to summarize the data helps to determine the type of data you want to collect. Since the population is what we are interested in, then you want to calculate a number from the population. This is known as a parameter. As mentioned already, you can’t really collect the entire population. Even though this is the number you are interested in, you can’t really calculate it. Instead you use the number calculated from the sample, called a statistic, to estimate the parameter. Since no sample is exactly the same, the statistic values are going to be different from sample to sample. They estimate the value of the parameter, but again, you do not know for sure if your answer is correct.

One last concept to mention is that there are two different types of variables – qualitative and quantitative. Each type of variable has different parameters and statistics that you find. It is important to know the difference between them.

There are different types of quantitative variables, called discrete or continuous. The difference is in how many values can the data have. If you can actually count the number of data values (even if you are counting to infinity), then the variable is called discrete. If it is not possible to count the number of data values, then the variable is called continuous.

There are also are four measurement scales for different types of data with each building on the ones below it. They are:

Measurement Scales:

Nominal and ordinal data come from qualitative variables. Interval and ratio data come from quantitative variables.

Most people have a hard time deciding if the data are nominal, ordinal, interval, or ratio. First, if the variable is qualitative (words instead of numbers) then it is either nominal or ordinal. Now ask yourself if you can put the data in a particular order. If you can it is ordinal. Otherwise, it is nominal. If the variable is quantitative (numbers), then it is either interval or ratio. For ratio data, a value of $0$ means there is no measurement. This is known as the absolute zero. If there is an absolute zero in the data, then it means it is ratio. If there is no absolute zero, then the data are interval. An example of an absolute zero is if you have $$0$ in your bank account, then you are without money. The amount of money in your bank account is ratio data. Word of caution, sometimes ordinal data is displayed using numbers, such as $5$ being strongly agree, and $1$ being strongly disagree. These numbers are not really numbers. Instead they are used to assign numerical values to ordinal data. In reality you should not perform any computations on this data, though many people do. If there are numbers, make sure the numbers are inherent numbers, and not numbers that were assigned.

Homework

Exercise $1$

1. Suppose you want to know how Arizona workers age $16$ or older travel to work. To estimate the percentage of people who use the different modes of travel, you take a sample containing $500$ Arizona workers age $16$ or older. State the individual, variable, population, sample, parameter, and statistic.
2. You wish to estimate the mean cholesterol levels of patients two days after they had a heart attack. To estimate the mean you collect data from $28$ heart patients. State the individual, variable, population, sample, parameter, and statistic.
3. Print-O-Matic would like to estimate their mean salary of all employees. To accomplish this they collect the salary of $19$ employees. State the individual, variable, population, sample, parameter, and statistic.
4. To estimate the percentage of households in Connecticut which use fuel oil as a heating source, a researcher collects information from $1000$ Connecticut households about what fuel is their heating source. State the individual, variable, population, sample, parameter, and statistic.
5. The U.S. Census Bureau needs to estimate the median income of males in the U.S., they collect incomes from $2500$ males. State the individual, variable, population, sample, parameter, and statistic.
6. The U.S. Census Bureau needs to estimate the median income of females in the U.S., they collect incomes from $3500$ females. State the individual, variable, population, sample, parameter, and statistic.
7. Eyeglassmatic manufactures eyeglasses and they would like to know the percentage of each defect type made. They review $\mathrm{25,891}$ defects and classify each defect that is made. State the individual, variable, population, sample, parameter, and statistic.
8. The World Health Organization wishes to estimate the mean density of people per square kilometer, they collect data on $56$ countries. State the individual, variable, population, sample, parameter, and statistic
9. State the measurement scale for each.
   1. Cholesterol level
   2. Defect type
   3. Time of first class
   4. Opinion on a 5 point scale, with 5 being strongly agree and 1 being strongly disagree
10. State the measurement scale for each.
    1. Temperature in degrees Celsius
    2. Ice cream flavors available
    3. Pain levels on a scale from 1 to 10, 10 being the worst pain ever
    4. Salary of employees

Answer

1. See solutions

3. See solutions

5. See solutions

7. See solutions

9.

1. ratio
2. nominal
3. interval
4. ordinal