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GRAPHING CHECKLIST
For a more humorous approach, check out  The Evil Tutor's Guide: Graph Formats

Graphs provide a way to look at and see trends in data. They are an excellent way to analyze and show the results of an experiment, because they show clearly the relationship between the manipulated or independent variable and the responding or dependent variable.
DESIGNING THE GRAPH:  Selecting the data to graph is the most critical step of the graph
  1. CHOICE OF DATA:  Relates to hypothesis; uses averages when appropriate
  2. TYPE OF GRAPH: Based on data. If the independent variable is qualitative, you must draw a bar graph; if the independent variable is quantitative, you may use a bar, line, or scatter graph.

LAYING OUT THE GRAPH

  1. USE OF SPACE: Appropriate for data 
  2. RULER: Clearly used! 
  3. ZERO:  The intersection of the two axes is 0 
  4. INTERVALS: Equal for each axis and numbered
  5. TITLE: Describes the data on the graph and is placed near the top of the graph
  6. X-AXIS: Labeled with the name of the manipulated or independent variable (horizontal axis)
  7. Y-AXIS: Labeled with the name of the responding or dependent variable (vertical axis)
  8. UNITS: Both axis labeled with the units of measurements
PLOTTING AND FINISHING THE GRAPH
  1. ACCURACY: Points or bars plotted accurately; plotted points can be identified 
  2. TRENDS/LINE: If points are connected or a line of best fit is drawn, the line ends at or just past the data points; line does not include 0 unless that is one of the measured points: neatly drawn 
  3. OUTLIERS: Generally not plotted  (include them in the data and discuss them in the analysis section of the lab report)

USING THE GRAPH TO ANALYZE THE DATA: Observe the completed graph for patterns, predictions, and interpretations.

  1. PATTERNS: Note how the dependent variable (Y axis) changes as a function of the independent variable (X axis) 
  2. INTERPOLATIONS (quantitative data): Look at the plotted coordinates and make interpretations based on the data that fall between the plotted coordinates, called "interpolations." For example, no data has been plotted on the X axis at 3.5, but one can interpolate that 5 responds on the Y axis 
  3. EXTRAPOLATIONS (quantitative data): Look at the plotted coordinates for patterns and, if possible, make inferences or predictions that fall outside the plotted data called "extrapolations." For example, no data has been plotted on the X axis at 7, but one can extrapolate the 1 responds on the Y axis.