Traditionally, this would be a scatter plot. Creating an Initial Scatter Plotīefore you can create a regression line, a graph must be produced from the data. This is done by fitting a linear regression line to the collected data. This fact can be used to calculate the concentration of unknown solutions, given their absorption readings. Beer's Law states that there is a linear relationship between concentration of a colored compound in solution and the light absorption of the solution. The data below was first introduced in the basic graphing module and is from a chemistry lab investigating light absorption by solutions. This too can be calculated and displayed in the graph. The closer R^2 is to 1.00, the better the fit. How well this equation describes the data (the 'fit'), is expressed as a correlation coefficient, R^2 (R-squared). This equation can either be seen in a dialogue box and/or shown on your graph. In addition to visually depicting the trend in the data with a regression line, you can also calculate the equation of the regression line. That is, the theory underlying your lab should indicate whether the relationship of the independent and dependent variables should be linear or non-linear. It is important that you are able to defend your use of either a straight or curved regression line. A curved line represents a trend described by a higher order equation (e.g., y = 2x^2 + 5x - 8). There are no squared or cubed variables in this equation).
A straight line depicts a linear trend in the data (i.e., the equation describing the line is of first order.
Regression lines can be used as a way of visually depicting the relationship between the independent (x) and dependent (y) variables in the graph.
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