Variables and Graphing A good experiment attempts to control all variables except the variable(s) that are being manipulated to see if a change can be observed and a cause and effect relationship can be reasoned. Variable A property or condition that can change. A variable may or may not cause a significant change. Manipulated variable is the variable that the experimenter decides to change to see if there is or is not an effect. Responding variable is the variable that will change as a result of the change in the manipulated variable. It may also be observed and or measured to determine the quantity or quality of change. When we plot information on a graph the manipulated variable always is plotted on the X - axis and the responding variable is always plotted on the Y - axis . Independent variable is another name for manipulated variable. It is independently selected by the experimenter to be manipulated. Dependent variable is watched by the experimenter and will respond to the manipulated or independent variable if there is a relationship. These relationships can be represented on a chart or graph: Before deciding to use a chart or graph a person needs to decide if the data is continuous or categorical. Continuous is data that can be represented on a continum with infinite possibilities. Temperature for example can be represented by whole numbers, decimal numbers... Categorical is data that is represented by a limited number of examples. Temperature could be categorical if it were categorized into hot, medium, or cold. Kinds of vehicles, people's favorite food... One might argue that favorite foods could be infinite, however it can be represented by categories and a category labeled others. Therefore, limiting the possibilities. Continuous data is best shown on a line graph and categorical in bar, pie, or other kinds of charts. Extrapolation is a prediction that is made from outside (extra) the data points collected and represented on the graph. Interpolation is a prediction that is made between the data points (inter, like interstate, between or among states).
Graphs in science
Ch.1 Section 5 Pages 35-41 Graphs in Science
Graph <ul><li>A picture of your data, they tend to reveal patterns or trends words or data tables cannot. </li></ul>
Line Graphs <ul><li>Are used to display data to show how one variable (RESPONDING VARIABLE) changes in response to another variable (MANIPULATED VARIABLE). </li></ul>
Plotting a line Graph <ul><li>1. Draw the axes </li></ul><ul><ul><li>Horizontal axis (x-axis) Responding Variable </li></ul></ul><ul><ul><li>Vertical axis (y-axis) Manipulated variable </li></ul></ul><ul><li>2. Label the axes: </li></ul><ul><ul><li>Horizontal (manipulated variable) </li></ul></ul><ul><ul><li>Vertical (responding variable) </li></ul></ul><ul><ul><li>Include UNITS of Measure </li></ul></ul><ul><li>3 Create a scale </li></ul><ul><ul><li>Needs to cover the ranges of values from your data. </li></ul></ul><ul><ul><li>Origin (point where two axes cross) </li></ul></ul><ul><ul><li>Coordinate: is a pair of numbers used to determine the position of a point on a graph. </li></ul></ul>
Plotting a line Graph <ul><li>4. Plot the data </li></ul><ul><ul><li>Using coordinate pairs (data) </li></ul></ul><ul><ul><li>Data Point: the point showing the location of the coordinate pairs </li></ul></ul><ul><li>5. Draw a line of best fit </li></ul><ul><ul><li>A smooth line that reflects the general pattern of a graph </li></ul></ul><ul><ul><li>Linear graph: a line graph in which the data points yield a straight line. </li></ul></ul><ul><li>6. Add a title </li></ul><ul><li>Indentify the variables or relationship shown in the graph. </li></ul>
Line of best Fit Line of best fit emphasizes the overall trend shown by all the data taken as a whole
Slope <ul><li>The slope of a graph line tells you how much y changes for every change in x. </li></ul>
Nonlinear graph <ul><li>A graph in which the data points do not fall along a straight line. </li></ul><ul><li>Linear trends: linear graphs easily show how two variables are related. </li></ul><ul><li>Nonlinear trends: useful in understanding how the variables are related </li></ul><ul><li>No trend: show no recognizable pattern. (Why?) </li></ul>
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