**Introduction**

Logistic regression is conducted when an outcome or dependent variable can have only two values, typically 0 or 1. This type of statistical procedure is often utilized in academic essays, research papers, and theses that are quantitative and focused on the analyses of binary outcomes (for example, wins or losses, heads or tails, etc.).

**Example Scenario and Stata Code**

Let’s imagine a scenario in which, in the context of hospital research, you want to study the relationship between the age of a patient and their odds of experiencing a fall in the hospital. You can code falling, as the dependent variable, as 0 = did not fall, 1 = fell. Age can be a whole number. The following code generates a mock dataset in Stata that lets us work with these data in a way that can build your intuition around logistic regression. Note that the very last line of code below (ciplot falls) generates a handy **95% confidence interval chart** to add visual support.

set obs 400

gen age1 = runiform(73,98)

gen age2 = runiform(7,75)

replace age1 = . in 260/400

replace age2 = . in 1/259

egen age3 = rowmax (age1 age2)

drop age1 age2

gen age = round(age3)

drop age3

label variable age "Patient Age"

gen falls1 = runiform(0,1)

gen falls = round(falls1)

drop falls1

replace falls = 1 in 1/129

label variable falls "Patient Falls"

label define falls 0 "Did Not Fall" 1 "Fell"

label value falls falls

logistic falls age, or

ciplot falls

**Logistic Regression Model**

The model is statistically significant, *p *= .004. In order to interpret the odds ratio (*OR*) associated with age, begin by recalling that a fall is 1, whereas the absence of a fall is 0. Given this coding approach, if the *OR* were exactly 1, age would not alter the odds of falling. If the *OR* had been below 1, and statistically significant, age would reduce the odds of falling. However, what we observe is that the *OR* is over 1 (*p* = .004), meaning that age increases the odds of falling.

To interpret the OR,

1.011943 – 1.000000 = 0.011943

0.011943 * 100 = 1.1943%

Therefore, every added year of patient age increases the odds of falling by 1.1943%.

**95% CI Reporting **

You will want to conclude that every added year of patient age increases the odds of falling by 1.1943%, *p * = .004, but it would be even better to add the 95% confidence intervals (*CI*s). Stata gives you the *CI* in its readout. Note that the 95% *CI* range is [1.003821, 1.02013]. You could therefore write that every added year of age patient age increases the odds of falling by 1.1943%, *p * = .004, 95% *CI* = 0.3821%, 2.013%.

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