210 PART 5 Looking for Relationships with Correlation and Regression

Examining the outcome variable’s type of data

Here are the different regressions we cover in this book by type of outcome

variable:»

» ^{Ordinary regression (also called linear regression) is used when the outcome}

is a continuous variable whose random fluctuations are governed by the

normal distribution (see Chapters 16 and 17).»

» ^{Logistic regression is used when the outcome variable is a two-level or}

dichotomous variable whose fluctuations are governed by the binomial

distribution (see Chapter 18).»

» ^{Poisson regression is used when the outcome variable is the number of}

occurrences of a sporadic event whose fluctuations are governed by the

Poisson distribution (see Chapter 19).»

» ^{Survival regression when the outcome is a time to event, often called a survival}

time. Part 6 covers the entire topic of survival analysis, and Chapter 23 focuses

on regression.

Figuring out what kind of function is being fitted

Another way to classify different types of regression analysis is according to

whether the mathematical formula for the model is linear or nonlinear in the

parameters.

In a linear function, you multiply each predictor variable by a parameter and then

add these products to give the predicted value. You can also have one more param-

eter that isn’t multiplied by anything — it’s called the constant term or the inter-

cept. Here are some linear functions:»

» ^Y

a

bX»

» ^Y

a

bX

cX

dX

2

3»

» ^Y

a

bX

cLog W

dX Cos Z

(

)

(

)

/

In these examples, Y is the dependent variable or the outcome, and X, W, and Z are

the independent variables or predictors. Also, a, b, c, and d are parameters.