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Project 22: Experimenting with the Current Voltage Characteristics of a Solar Cell Project 22: Current Voltage Characteristics

0.83 0.78 0.83 0.81

16.35 42.55 53.71 2173.42

3.71 4.67 4.75 4.04

Each organization, division, or department is an integrated and interrelated part of a larger system. Every part of the system affects the performance of other organizations in a larger system, such as in society or even the universe. Each level of a natural system interfaces or collides with other systems at the same level. Individuals work with other individuals, groups or departments interact with the whole organization, and the organization interrelates with the outside world. Managing these interactions is critical in an organization encompassing a new vision, if it is to lead to dramatic improvement and higher profitability. Organizations naturally exist in a continuum where the environment, the organization, and feedback are interconnected.

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Light source Photovoltaic cell Voltmeter Ammeter Variable resistance Graph paper and pencil Figure 10-21 Circuit to determine the current voltage curve of a single solar cell.

Figure 16.5 quantities).

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Stepwise regression results for the 20 waste groups (signi cant variables that in uence solid waste

1.00 0.90 0.80 0.70 0.60 R2 0.50 0.40 0.30 0.20 0.10 0.00 AUT HTL RTL FDS MED CON ARG REC WDM MLM ELM PPM TRM FBM CHM FDM GOV MIN EDU RST

According to Stephen Haines, the author of The Manager s Pocket Guide to Systems Thinking and Learning (1998), systems thinking involves thinking backward. Define first the vision, then the goals that will let the organization realize that vision, while simultaneously considering the environment as it will exist in the future. By establishing processes to realize vision and goals, we can then create measurement criteria that describe the specific factors defining success. These clear and consistent measurement criteria are used to evaluate the strategies effectiveness.

in uential in predicting annual solid waste at the 95 percent con dence level and the full stepwise regression results for each of the 20 waste groups. The remainder of this chapter discusses the regression results from the previous table and compares the 20 waste groups to gain insights on waste generation in the United States. As shown in the preceding table, 50 to 85 percent of the total variation (R2) in annual waste generation is attributed to the number of employees for each waste group. The chart in Fig. 16.6 provides a visual comparison. The chart in Fig. 16.7 provides a comparison of the total and partial coef cient of determination for each of the 20 waste groups. This chart indicates that 79 percent to 90 percent of the variation in annual solid waste generation was accounted for by the three signi cant independent variables identi ed for the 20 waste groups (at the 95 percent con dence level). The magnitudes of the regression coef cients were compared for each of the 20 waste groups. The charts in Figs. 16.8 through 16.10 display this comparison. The previous procedure determined the signi cant variables that in uence annual solid waste quantities for each group. The next step involved interpreting the results, identifying trends, and creating new knowledge. Two categories of analyses were conducted (from the regression analyses):

We can learn a lot about solar cells electrical characteristics by plotting the current voltage curve of the device. To carry out the experiment, we will need to ensure that the solar cell receives constant illumination all the time. Use a bright lamp, and position it a fixed distance above the solar cell. Set up the circuit as shown in Figure 10-21. We are now going to adjust the variable resistor from one extreme to the other, noting how the readings on the voltmeter and ammeter change as we do so. At this point you need to make careful notes as to the current and the voltage at each stage. You can do this on paper, or, if you have a PC handy, on a spreadsheet. Try and take at least 15 or so different readings to help you plot an accurate curve.

Analysis of the variables that entered the prediction equations Analysis of the variables that did not enter the prediction equations

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