Interpreting Your Data Plots
Although basic trends in your data can sometimes be estimated by simply looking at the data points on your scatter plots, quantitative measures of the effects you are studying can only be determined by fitting a curve to your data. Curve fitting involves producing a statistically derived best-fit line of data points on the graph; not a hand-drawn or estimated line connecting data points. Once you have plotted your data, a Plot # tab will appear at the top of the Plot Data screen. Clicking on this tab will take you to the curve-fitting functions of LeafLab and allow you to switch between plots that you generate. 1.
Click on the Plot 1 tab to enter the curve-fitting view.
An enlarged view of the plot should now appear with a series of curve-fitting controls to the left of the plot.
The purpose and instructions for manipulating each control are described in following steps:
Curve: generates a best-fit curve based on the data points selected. You will be generating a best-fit curve by following the steps listed next. •
y–Intercept: indicates the rate of dark respiration (light compensation point)
To input the y-intercept: return to the data table by clicking on the Data tab. Look at the zero light measurement in the table and use the P value for this measurement as the initial measurement of the intercept. o
Return to the curve-fitting view and enter this P value directly into the intercept box.
Slope (of the line): photochemical efficiency; indicates the rate at which photosynthesis increases as light intensity increases.
To manipulate the slope: click on the up arrow next to the slope function (you will see the line rise up and begin to form a curve). o
Increase the slope of the line until the curve looks like it is matching (fitting) the data points.
Asymptote (where the curve forms a straight line indicating that the data has leveled off): indicates photosynthetic saturation (maximum...
Please join StudyMode to read the full document