A response variable is a value in the dependent variable side of an equation.
If you hear about how “a high level of X causes decreased Y” but don’t know what the variables are, ask for them to clarify and see if it’s a linear or exponential relationship (if they respond). They may give information about slope and intercept which will help figure out what the equation is.
Practically, if somebody says something like “I’m 20 pounds overweight because my parents were always on food stamps,” this would be a conclusion statement rather than a hypothesis so there would be no independent or dependent variables–just their observation.
A Response variable is the variable in a statistical model that has response values which are typically unknown to the researcher. In other words, they represent what we want to discover based on our research.
Non-response variables are those variables we know based on previous experience or research; for example, age in a study of human mortality rates.
In cases where researchers have access to both data sets (the “model” and the “data”), their manner of presentation may be adjusted as such: The left half is intended for use with response data and refers only to predictive residuals, while the right half contains Fourier analysis coefficients calculated from both data sets simultaneously using interpolation techniques. These coefficients represent important information but not useful in determining the response values.
The “response variable” is the one we’re measuring.
It’s common for people who eat a lot of sugar, junk food, or processed food to need a lot of sugar or salt to enjoy food. In other words, it’s the norm for most Americans, and it’s the reason people from other countries often find our food too sweet. The good news is that by eating natural foods, the taste buds can slowly be awakened to a whole array of real flavors, and everything begins to taste much sweeter naturally without all the added sugar.
This can refer to different things. A response variable is a variable in a statistical model that corresponds to the outcome of an experiment that you’re trying to measure.
This is typically when you test something like drugs or workouts and the statistic determines whether they help, whether they are safe, how they affect the body, etc.
Many estimates of requirements for foods and minerals come from this type of experimentation, analysis of what happens when a certain lab animal (such as a mouse) is fed with certain amounts of various substances (such as protein) for so many days on average (say 100). Some responses are average weight gained in grams per day while other responses can be more complicated measurements such as number of tumors on each side where one group is given substances A and B versus another group that receives substance B alone.
This is a variable that we typically know before conducting the experiment and can be used to predict what will happen in the experiment.
A response variable is either an outcome or measurement, like height. In a lab setting, you might make a hypothesis that “tall people weigh more than short people”, so your independent (predictor) variable is height and your dependent (response) variable would be weight. Although the predictor has all of the experimental power in this scenario, it’s actually treating data from two different things at once: measuring weight and predicting height.
In statistics, a response variable is the variable being measured and analyzed.
Quote from textbook: The response variable is often referred to as the “dependent” or “outcome” variable, because it describes an outcome that only depends on the levels of one or several predictor variables.
In statistics, a response variable is the dependent variable to which one wants to find a relationship with another variable.
A response variable in statistics is also called the object of analysis. It is an observed quantity whose behavior or value depends on the values of (or relationships among) other variables, sometimes called explanatory variables. For example, average rainfall might be used as a response variable to compare with temperature and water availability at different locations and times. The term “response” refers not only to these statistical analyses but also reflects that people are most interested in knowing what they might do if they cannot change their outcome or situation (the “response”).
A response variable in statistics is a variable that represents the outcome of an experiment or some other study.
A response variable is a measurement recorded during an experimental procedure. It usually means any variable whose values are being examined under different conditions, and can be thought of as something measured directly by the researcher, such as perceived pain levels with each shock level on a device designed to measure pain sensitivity. This measurement can also be obtained through indirectly observing variables such as heart rate, protein levels in blood cells, etc. In fact it could even refer to something that’s not easily measurable by someone other than the researcher like how pleasant researchers perceive their day-to-day actions to be on average while conducting research work; for this situation it would simply be a subjective self-report of the researcher.
In regression analysis, the response variable is the variable whose values are predicted by the model.
The response variable or outcome can refer to a dependent variable, the quantity being studied in a research study.
The response is measured as a change in whatever parameter you are measuring, like number of people who die from lung cancer caused by smoking cigarettes, how many cars own per household, etc.
In statistics, a response variable is a variable used in the response of an experiment or survey and ordinarily denoted by Y. It may be a measurement that reflects the effect or outcome of the experimental stimulus (X), such as profit or height, but can also refer to any dependent measure recorded for each unit in an observational study group.
During analysis for quantitative data, computer software assigns at least one entry to each category of the independent variables. The uncoded values are designated as missing values and are dealt with according to user specifications of how missing values are handled.