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DEPENDENT: A variable that is changed based on another variable. Y-variable. FUNCTION: A relationship where every input has one and only one output. INDEPENDENT: A variable that is changed by choice. X-Variable. INPUT: A term or number that is plugged into a relationship; the x-variable in an ordered pair

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Jan 08, 2003 · For each of the problem statements listed below, identify the independent (IV) and dependent (DV) variable, and determine whether each variable is qualitative or quantitative. Answers follow the problem statements. 1. Will students' scores on an achievement test differ between directive teachers and non-directive teachers? 2.

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If x and y are two variable in an algebraic equation and every value of x is linked with any other value of y, then ‘y’ value is said to be a function of x value known as an independent variable, and ‘y’ value is known as a dependent variable. Example: In the expression y = x 2, x is an independent variable and y is a dependent variable.

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b) Your variables (identify if you have an independent or subject variable and your dependent variable) c) Your method (how you obtained your observations) d) The answer to your research question from your observations e) Any limitations you found using this method to answer your question

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Make directional hypotheses that specify what happens to a dependent variable when an independent variable is manipulated. Manipulate variables and collect data about a complex model of a proposed process or system to identify failure points or improve performance relative to criteria for success or other variables.

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(a) Which variable is independent and which variable is dependent in this scenario? (b) Fill in the table below for a variety of time values. Time, t, in minutes 0 1 5 10 Distance from home, D, in blocks REASONING 4. In one of the following tables, the variable y is a function of the variable x. Explain which relationship is a

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The basic linear regression model assumes that the contributions of the different independent variables to the prediction of the dependent variable are additive. For example, if X 1 and X 2 are assumed to contribute additively to Y, the prediction equation of the regression model is: Ŷ t = b 0 + b 1 X 1t + b 2 X 2t