Can you interact continuous variables?
Can you interact continuous variables?
It means that the slope of one continuous variable on the response variable changes as the values on a second continuous change. Multiple regression models often contain interaction terms. In other words, a regression model that has a significant two-way interaction of continuous variables.
Can you have an interaction between a continuous and categorical variable?
An interaction can occur between independent variables that are categorical or continuous and across multiple independent variables. This example will focus on interactions between one pair of variables that are categorical and continuous in nature. This is called a two-way interaction.
What is an interaction term in Stata?
Stata: Data Analysis and Statistical Software You can put a # between two variables to create an interaction–indicators for each combination of the categories of the variables. You can put ## instead to specify a full factorial of the variables—main effects for each variable and an interaction.
What is considered a continuous variable?
Continuous variables can take on an unlimited number of values between the lowest and highest points of measurement. Continuous variables include such things as speed and distance. Gender or rank are examples of discrete variables because there are a limited number of mutually exclusive options.
What is categorical and continuous variables?
Categorical variables contain a finite number of categories or distinct groups. Continuous variables are numeric variables that have an infinite number of values between any two values. A continuous variable can be numeric or date/time. For example, the length of a part or the date and time a payment is received.
What is an interaction variable?
An interaction variable or interaction feature is a variable constructed from an original set of variables to try to represent either all of the interaction present or some part of it.
What is a categorical variable in Stata?
Stata handles categorical variables as factor variables; see [U] 11.4. 3 Factor variables. Categorical variables refer to the variables in your data that take on categorical values, variables such as sex, group, and region. Factor variables refer to Stata’s treatment of categorical variables.
What does != Mean in Stata?
In Stata, these expressions use one or more various relational and logical operators. The operators ==, ~=, != , >, >=, <, and <= are used to test equality or inequality. The operators & | ~ and ! are used to indicate “and”, “or”, and “not”. It is a matter of taste whether you use ~ or ! to indicate negation.
What are two examples of continuous variables?
You often measure a continuous variable on a scale. For example, when you measure height, weight, and temperature, you have continuous data. With continuous variables, you can calculate and assess the mean, median, standard deviation, or variance.
What are examples of discrete and continuous variables?
Difference between Discrete and Continuous Variable
| Discrete Variable | Continuous Variable |
|---|---|
| Examples: Number of planets around the Sun Number of students in a class | Examples: Number of stars in the space Height or weight of the students in a particular class |
What are examples of categorical variables?
Categorical variables represent types of data which may be divided into groups. Examples of categorical variables are race, sex, age group, and educational level.
When to use C or I in Stata?
First, when you specify an interaction in Stata, it’s preferable to also specify whether the predictor is continuous or categorical (by default Stata assumes interaction variables are categorical). A c. precedes a continuous variable and an i. precedes a categorical one.
What are the components of an interaction in Stata?
Let’s define the essential elements of the interaction in a regression: DV: dependent variable (Y), the outcome of your study (e.g., weight loss) IV: independent variable (X), the predictor of your outcome (e.g., time exercising)
What are the variables in the Stata exercise program?
Variables include loss: weight loss (continuous), positive = weight loss, negative scores = weight gain hours: hours spent exercising (continuous) effort: effort during exercise (continuous), 0 = minimal physical effort and 50 = maximum effort prog: exercise program (categorical) jogging=1 swimming=2 reading=3 gender: participant gender (binary)
How can I explain a continuous by continuous interaction?
In the formula, Y is the response variable, X the predictor (independent) variable with Z being the moderator variable. The term XZ is the interaction of the predictor with the moderator. We will illustrate the simple slopes process using the hsbdemo dataset that has a statistically significant continuous by continuous interaction.