data collection -
data processing/analysis -
You are required to complete statistical measures. At this level of research, it
is simply not an option. The good news is that there is help for you. Below is a
Brief Course in Statistics written by Dru Martin at Ramstein High School. This
will assist you with your data analysis.
Data gathering + measurement:
Ultimately the analysis you can do, the graphs you can make and the conclusions
you can come to depend on the data that you gather. The richer in information
your measurements are, the better the rest of your project can be. To
illustrate:
1.
The bacteria grew yes/no
2.
The bacteria grew (circle one) none some lots
3.
The bacteria covered ____% of the surface.
Number
one is an example of a categorical measurement; your observation fits into one
of two categories. Number two is also a categorical measurement, but the
categories have an inherent order. Number three is a quantitative measurement.
As we move from 1 to 2 to 3, the amount of information contained in the data
increases, and the analysis possibilities increase as well. When possible, take
quantitative measurements of your dependent and independent
variables.
Graphing data: Your experiment will most likely be comparing a
control setup with an experimental setup. Therefore, your graphs should clearly
compare these two results, preferably on the same graph.
The type of graph you can draw depends on the type of data that you took. For
categorical data, a pie chart or histogram is best. For one quantitative measurement,
a bar graph, boxplot, stem-and-leaf diagram or dotplot are all good alternatives. If
you have a quantitative measurement for each of your experimental groups, you
will need to display all the groups on one graph. Finally, if you are looking at
the relationship between two quantitative measurements, a scatterplot is most
useful, and a least squares regression line may be
appropriate.
Graphing
data: Your experiment will most likely be comparing a control setup with an
experimental setup. Therefore, your graphs should clearly compare these two
results, preferably on the same graph.The
type of graph you can draw depends on the type of data that you took. For
categorical data, a pie chart or histogram is best. For one quantitative
measurement, a bar graph, boxplot, stem-and-leaf diagram or dotplot are all good
alternatives. If you have a quantitative measurement for each of your
experimental groups, you will need to display all the groups on one graph.
Finally, if you are looking at the relationship between two quantitative
measurements, a scatterplot is most useful, and a least squares regression line
may be appropriate.
Video - Introduction to Statistical Analysis
OPTIONS FOR STATISTICAL ANALYSIS
T-Test PowerPoint Explanation
is simply not an option. The good news is that there is help for you. Below is a
Brief Course in Statistics written by Dru Martin at Ramstein High School. This
will assist you with your data analysis.
Data gathering + measurement:
Ultimately the analysis you can do, the graphs you can make and the conclusions
you can come to depend on the data that you gather. The richer in information
your measurements are, the better the rest of your project can be. To
illustrate:
1.
The bacteria grew yes/no
2.
The bacteria grew (circle one) none some lots
3.
The bacteria covered ____% of the surface.
Number
one is an example of a categorical measurement; your observation fits into one
of two categories. Number two is also a categorical measurement, but the
categories have an inherent order. Number three is a quantitative measurement.
As we move from 1 to 2 to 3, the amount of information contained in the data
increases, and the analysis possibilities increase as well. When possible, take
quantitative measurements of your dependent and independent
variables.
Graphing data: Your experiment will most likely be comparing a
control setup with an experimental setup. Therefore, your graphs should clearly
compare these two results, preferably on the same graph.
The type of graph you can draw depends on the type of data that you took. For
categorical data, a pie chart or histogram is best. For one quantitative measurement,
a bar graph, boxplot, stem-and-leaf diagram or dotplot are all good alternatives. If
you have a quantitative measurement for each of your experimental groups, you
will need to display all the groups on one graph. Finally, if you are looking at
the relationship between two quantitative measurements, a scatterplot is most
useful, and a least squares regression line may be
appropriate.
Graphing
data: Your experiment will most likely be comparing a control setup with an
experimental setup. Therefore, your graphs should clearly compare these two
results, preferably on the same graph.The
type of graph you can draw depends on the type of data that you took. For
categorical data, a pie chart or histogram is best. For one quantitative
measurement, a bar graph, boxplot, stem-and-leaf diagram or dotplot are all good
alternatives. If you have a quantitative measurement for each of your
experimental groups, you will need to display all the groups on one graph.
Finally, if you are looking at the relationship between two quantitative
measurements, a scatterplot is most useful, and a least squares regression line
may be appropriate.
Video - Introduction to Statistical Analysis
OPTIONS FOR STATISTICAL ANALYSIS
T-Test PowerPoint Explanation