how to furthur analyze data

[elvisrocks]

My science research project is the effect of different amounts of gas and oil on the height and the health of the bean plants. The independent variable is the amount/type of liquid given to the plant. The levels of the independent variable are 1/2 tsp gas, 1 tsp gas, 1 tbsp gas, 1 1/2 tbsp gas, 1/2 tsp oil, 1 tsp oil, 1 tbsp oil, 1 1/2 tbsp oil and water(control). There are 6 trials for each level. There are two dependent variables, the height of the bean plants and the health of the bean plants. For recording the health of the plants I simply record "Healthy" or "Unhealthy".
The end data was as follows. The plants receiving gas all died by the end of the experiment. The experiment lasted 21 days. The plants receiving water had the highest mean height with 37.5 cm. The mean height for 1/2 tsp of oil was 29.5 cm. The mean height for 1 tsp of oil was 14.8 cm. The mean height for 1 tbsp of oil was 25.1 cm. The mean height for 1 1/2 tbsp of oil was 19.7 cm.
I am working on analyzing the data. Here is what I have so far. The height of the plant is quantitative ratio data. I am using mean as the measure of central tendency and range as the measure of variation. The health of the plant is qualitative nominal data. I am using mode as the measure of central tendency and frequency distribution as the measure of variation. Please tell me other ways I can furthur analyze the data. Should I use statistical graphs, stem and leaf plots,box plots, standard deviation or another way to analyze data?Thank you for your help.

[deleted-71495]

Hi elvisrocks,

what you're doing sounds OK to me. I can just give you some general remarks. Keep in mind that a measurement without error is not very meaningful. When you average numbers, this error is usually the standard deviation (square root of the variance). Whatever you do with those measurements, their errors need to be propagated correctly into the final result.
You mention that you look at spread/variance so you do have an error evaluation of some kind and that's good.

In terms of presenting your data, go for the graphics. A good plot tells you the whole story in just one single picture. When choosing the type of plot, think of your variables. For example, you've measured growth over time for different samples. So one plot that has to be in there is growth over time.. don't forget the error bars on your data points.