Page 43 - Statistics II for Dummies

Chapter 2: Finding the Right Analysis for the Job 27

In order for you to compare these complex relationships, you must build a
model to evaluate each group’s impact on political affiliation (or some other
categorical variable). This kind of model-building is explored in-depth in
Chapter 8, where I discuss the topic of logistic regression.

Logistic regression builds models to predict the outcome of a categorical
variable, such as political affiliation. If you want to make predictions about a
quantitative variable, such as income, you need to use the standard type of
regression (check out Chapters 4 and 5).

Statistics for Quantitative Variables

Quantitative variables, unlike categorical variables, have a wider range of
statistics that you can do, depending on what questions you want to ask. The
main reason for this wider range is that quantitative data are numbers that
represent measurements or counts, so it makes sense that you can order,
add or subtract, and multiply or divide them — and the results all have
numerical meaning. In this section, I present the major data-analysis tech-
niques for quantitative data. I expand on each technique in later chapters of
this book.

Making estimates

Quantitative variables take on numerical values that involve counts or mea-
surements, so they have means, medians, standard deviations, and all those
good things that categorical variables don’t have. Researchers often want to
know what the average or median value is for a population (these are called
parameters). To do this requires taking a sample and making a good guess,
also known as an estimate, of that parameter.

To find an estimate for any population parameter requires a confidence
interval. For categorical variables, you would find a confidence interval to
estimate the population mean, median, or standard deviation, but by far the
most common parameter of interest is the population mean.

A confidence interval for the population mean is the sample mean plus or
minus a margin of error. (To calculate the margin of error in this case, see
Chapter 3.) The result will be a range of likely values you have produced for
the real population mean. Because the variable is quantitative, the confi-
dence interval will take on the same units as the variable does. For example,
household incomes will be in thousands of dollars.

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