# What are dependent variables and independent variables

Dependent and independent variables

# What are dependent variables and independent variables SAT / ACT Prep Online Guides and Tips

Dependent and Independent Variables. In analytical health research there are generally two types of variables. Independent variables are what we expect will influence dependent variables. A Dependent variable is what happens as a result of the independent variable. For example, if we want to explore whether high concentrations of vehicle exhaust impact incidence of asthma in . Oct 07,  · Independent vs Dependent Variable Key Takeaways The independent and dependent variables are the two key variables in a science experiment. The independent variable is the one the experimenter controls. The dependent variable is the variable that .

The two main variables in an experiment are the independent and dependent variable. An independent variable is the variable that is changed or controlled in a scientific experiment to test the effects on the dependent variable. A dependent variable is the variable being tested and measured in a scientific experiment.

Deepndent dependent variable is 'dependent' on the independent variable. As the experimenter changes the independent variablethe effect on the dependent variable is observed and recorded. For example, a scientist wants to see if the brightness of light has any effect on a moth being attracted to the light.

The brightness of the light is controlled by the scientist. This would be the independent variable. How the moth reacts to the different light levels distance to light how long is an appendectomy would be the dependent variable. The independent and dependent variables may be viewed in terms of cause and effect.

If the independent variable is what is a branched chain, then an effect is seen in the dependent variable.

Remember, the values of both variables may change in an experiment and are recorded. The difference is variiables the value of the independent variable is controlled by the experimenter, while the value of the dependent variable only changes in response to the independent variable.

When results are plotted in graphs, the convention is to use the independent variable as the x-axis and the dependent variable as the y-axis. D is the dependent variable R is the responding variable Y is the axis on which the dependent or responding variable is wyat the vertical axis. M is the manipulated variable or the one that is changed in an experiment I is the independent variable X is the axis on which the independent or manipulated variable is graphed the horizontal axis.

Share Flipboard Email. Todd Helmenstine. Todd Helmenstine is a science writer and illustrator who has varables physics and math at the college level. He holds bachelor's degrees in both physics and mathematics. Updated October 07, Cite this Article Format. Helmenstine, Todd. Independent Variagles Definition whxt Examples.

What Are Independent and Dependent Variables? Dependent Variable Definition and Examples. What Is an Experiment? Definition and Design. What Are the Elements of a Good Hypothesis? The Role of a Controlled Variable in an Vxriables.

Recognize and Graph Independent and Dependent Variables

Answer: Just like an independent variable, a dependent variable is exactly what it sounds like. It is something that depends on other factors. 4 rows · May 20,  · Independent and dependent variables in experiments. In experimental research, the independent. Dec 01,  · In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score. You want to compare brands of paper towels, to see which holds the most liquid. The independent variable in your experiment would be the brand of paper towel.

Dependent and independent variables are variables in mathematical modeling , statistical modeling and experimental sciences. Dependent variables receive this name because, in an experiment, their values are studied under the supposition or hypothesis that they depend, by some law or rule e.

Independent variables, in turn, are not seen as depending on any other variable in the scope of the experiment in question. Of the two, it is always the dependent variable whose variation is being studied, by altering inputs, also known as regressors in a statistical context.

In an experiment, any variable that the experimenter manipulates [ clarification needed ] can be called an independent variable. Models and experiments test the effects that the independent variables have on the dependent variables.

Sometimes, even if their influence is not of direct interest, independent variables may be included for other reasons, such as to account for their potential confounding effect. In mathematics, a function is a rule for taking an input in the simplest case, a number or set of numbers  and providing an output which may also be a number. It is possible to have multiple independent variables or multiple dependent variables. In mathematical modeling , the dependent variable is studied to see if and how much it varies as the independent variables vary.

The term e i is known as the "error" and contains the variability of the dependent variable not explained by the independent variable. The linear regression model is now discussed. To use linear regression, a scatter plot of data is generated with X as the independent variable and Y as the dependent variable. This is also called a bivariate dataset, x 1 , y 1 x 2 , y In this case, U i , This occurs when the measurements do not influence each other.

Through propagation of independence, the independence of U i implies independence of Y i , even though each Y i has a different expectation value. Expectation of Y i Proof: . In simulation , the dependent variable is changed in response to changes in the independent variables.

In an experiment , the variable manipulated by an experimenter is something that is proven to work called an independent variable. In data mining tools for multivariate statistics and machine learning , the dependent variable is assigned a role as target variable or in some tools as label attribute , while an independent variable may be assigned a role as regular variable. The target variable is used in supervised learning algorithms but not in unsupervised learning.

Depending on the context, an independent variable is sometimes called a "predictor variable", regressor , covariate , "manipulated variable", "explanatory variable", exposure variable see reliability theory , " risk factor " see medical statistics , " feature " in machine learning and pattern recognition or "input variable".

From the Economics community, the independent variables are also called exogenous. Depending on the context, a dependent variable is sometimes called a "response variable", "regressand", "criterion", "predicted variable", "measured variable", "explained variable", "experimental variable", "responding variable", "outcome variable", "output variable", "target" or "label".

An example is provided by the analysis of trend in sea level by Woodworth Here the dependent variable and variable of most interest was the annual mean sea level at a given location for which a series of yearly values were available.

The primary independent variable was time. Use was made of a covariate consisting of yearly values of annual mean atmospheric pressure at sea level. The results showed that inclusion of the covariate allowed improved estimates of the trend against time to be obtained, compared to analyses which omitted the covariate. A variable may be thought to alter the dependent or independent variables, but may not actually be the focus of the experiment.

So that the variable will be kept constant or monitored to try to minimize its effect on the experiment. Such variables may be designated as either a "controlled variable", " control variable ", or "fixed variable". Extraneous variables, if included in a regression analysis as independent variables, may aid a researcher with accurate response parameter estimation, prediction , and goodness of fit , but are not of substantive interest to the hypothesis under examination.

For example, in a study examining the effect of post-secondary education on lifetime earnings, some extraneous variables might be gender, ethnicity, social class, genetics, intelligence, age, and so forth. A variable is extraneous only when it can be assumed or shown to influence the dependent variable. If included in a regression, it can improve the fit of the model.

If it is excluded from the regression and if it has a non-zero covariance with one or more of the independent variables of interest, its omission will bias the regression's result for the effect of that independent variable of interest. From Wikipedia, the free encyclopedia. For dependent and independent random variables, see Independence probability theory. Concept in mathematical modeling, statistical modeling and experimental sciences.

Mathematical modelling techniques. Courier Corporation. DiPrima Elementary differential equations. Chaos an introduction to dynamical systems. Springer New York. Workshop calculus: guided exploration with review. A concrete introduction to real analysis. CRC Press, Cengage Learning, Section 1. Bivens, and Stephen Davis.

Calculus Single Variable. Section 0. Section Random House, Inc. Page , ISBN Basic Econometrics Fifth international ed. New York: McGraw-Hill. A Dictionary of Epidemiology Fourth ed. Oxford UP.

The Cambridge Dictionary of Statistics 2nd ed. Cambridge UP. ISBN X. Marine Geodesy. Differential equations. Difference discrete analogue Stochastic Stochastic partial Delay. Inspection Separation of variables Method of undetermined coefficients Variation of parameters Integrating factor Integral transforms Euler method Finite difference method Crank—Nicolson method Runge—Kutta methods Finite element method Finite volume method Galerkin method Perturbation theory.

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