ex. 1 data management
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Experiment 1
Data Management
Submitted by:
Group 3 - 4B7
Agarano, Jethro
Almonte, Kathleen
Bartolome, Arriane
Cruces, Mico
Hermoso, Abraham
Ramos, Al Christopher
Submitted to:
Prof. Susana F. Baldia, Ph.D.
Abigail Garcia M.Sc.
7 December 2010
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I. Materials:
Bond paper, scratch papers
Graphing paper
Pencil and eraser
Metric ruler
Scientific Calculator
Personal Computer/ Laptop
II. Solutions for the Practice Exercises
A. Practical exercise I.
A t- test was performed on the following data to see if there is a significant
difference in the growth of oat coleoptiles that was treated with IAA (indole
acetic acid or auxin) in comparison with the untreated controls. On the
worksheet, Variable I and Variable 2 was re-labeling into columns in the
output block as Control and IAA respectively. Short statements were
written below the output block on the worksheet indicating the difference in
the means if it is significant or not.
Figure 1 Coleoptiles Growth Comparison based on food intake with control and IAA
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HO: IAA has no substantial effect on the growth and development of the
coleoptiles
HA: IAA has substantial effect on the growth and development of the coleoptiles
Given the data for the comparison of coleoptiles length in control and IAA induced
samples, a graph was formed where t-test unpaired was used and a p-value of
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Excel was used to plot and print the XY graph of larval growth in Noctua
pronubausing the given data. Instar has been the independent variable (it is
related to time or age) and body length (mm) was the dependent variable.The X axis began at 1.
Ho: Growth rate is linear and does not change as the caterpillar grows
Ha: Growth rate is not linear and changes as the caterpillar grows.
Figure 2 Larval growth of Noctua pronuba showing the comparison of body length through
growth of the instars
Mean body length (mm)
Best-fit values
Slope 5.242 0.8139
Y-intercept when X=0.0 -6.941 3.640
X-intercept when Y=0.0 1.324
1/slope 0.1908
95% Confidence Intervals
Slope 3.150 to 7.335
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Y-intercept when X=0.0 -16.30 to 2.417
X-intercept when Y=0.0 -0.7233 to 2.357
Goodness of Fit
R square 0.8924Sy.x 4.307
Is slope significantly non-zero?
F 41.48
DFn, DFd 1.000, 5.000
P value 0.0013
Deviation from zero? Significant
Data
Number of X values 7
Maximum number of Y replicates 1
Total number of values 7
Number of missing values 0
C. Practice exercise III. XY graph; given X values and equation.
Excel was used to plot and print the graph of Growth in Pices hallucigenia
that was based in the equation. 10 values of L (Length) were used. L values
were chosen but space them more or less evenly between 50 and 200mm
and 50 and 200 was included as the first and last values. Length (mm) was
the independent variable and weight (g) as the independent variable. The X
axis began at 50.
Figure 3 Growth curve ofPices hallucigenia
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Growth (in g)Best-fit values
Slope 50.17 5.724
Y-intercept when X=0.0 -3492 751.4X-intercept when Y=0.0 69.61
1/slope 0.01993
95% Confidence Intervals
Slope 36.97 to 63.37
Y-intercept when X=0.0 -5225 to -1760
X-intercept when Y=0.0 45.97 to 85.37
Goodness of Fit
R square 0.9057
Sy.x 849.3
Is slope significantly non-zero?
F 76.84
DFn, DFd 1.000, 8.000P value < 0.0001
Deviation from zero? Significant
Data
Number of X values 10
Maximum number of Y replicates 1
Total number of values 10
Number of missing values 0
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D. Practical exercise IV: XY graph: logarithmic plots.
The length- weight was plot in the equation in problem 3 on a log-log plot. In
the given problem same data was used and equations were entered inproblem 3. The only thing that was changed was switch from Normal to Log
under the Scale on both the Y-axis and X-axis dialog boxes.
Figure 4 Log growth curve ofPices hallucigenia
Growth (in g)
Best-fit values
Slope 50.17 5.724
Y-intercept when X=0.0 -3492 751.4
X-intercept when Y=0.0 69.61
1/slope 0.01993
95% Confidence Intervals
Slope 36.97 to 63.37
Y-intercept when X=0.0 -5225 to -1760
X-intercept when Y=0.0 45.97 to 85.37
Goodness of Fit
R square 0.9057
Sy.x 849.3
Is slope significantly non-zero?
F 76.84
DFn, DFd 1.000, 8.000
P value < 0.0001
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Deviation from zero? Significant
Data
Number of X values 10
Maximum number of Y replicates 1Total number of values 10
Number of missing values 0
E. Practice exercise V: XY graph; given X and Y values.
Taking the samples was the technique involved. identifying and counting was
also used in each of the samples. The cumulative number of species in the
sample was plotted against the number of samples. Results were shown in
the curve that showed the number of species produced against the sampling
effort that was collected. The curve was usually steep near the origin and the
levels off at about the number of species were present in the community.
There were additional sampling yields for only a small number of very rare
species.
Figure 5 Species/ Effort curve for cumulative number of species
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Cumulative # of Species
Total number of values 12
Number of excluded values 0
Number of binned values 12
Minimum 6.0
25% Percentile 15.25
Median 24.5
75% Percentile 28.0
Maximum 29.0
Mean 21.5833
Std. Deviation 8.1515
Std. Error 2.35314
Lower 95% CI of mean 16.4042Upper 95% CI of mean 26.7625
F. Practice exercise VI. Histogram
For additional practice with histogram the following data for age distribution of
male perch in Lake Windermere, England was plotted. The data represented
the numbers of fish of different ages in the Lake in the year 1966.
Figure 6 Histogram for Age distribution of Male Perch Population
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Figure 7 Alternative Histogram for Ages of Male Perch population
Age % of Male Perch Population
Total number of values 11 11
Number of excluded values 0 0
Number of binned values 11 11
Minimum 2.0 0.0
25% Percentile 4.0 0.0
Median 7.0 3.0
75% Percentile 10.0 9.0
Maximum 12.0 60.0
Mean 7.0 9.09091Std. Deviation 3.31662 17.3347
Std. Error 1.0 5.2266
Lower 95% CI of mean 4.77188 -2.55458
Upper 95% CI of mean 9.22812 20.7364
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G. Practice exercise VII: Graphing exponential equations from X data and
an equation.
A laboratory experiment in population growth was prepared, the instructor thatwas found in the green alga, Selenastrum capricornutum, exhibited an
instantaneous per capita growth rate of 1.5. At day t0 the population density
was 5382 cells/ml. Excel was used to draw a graph of population growth of
this species in the equation using the exponential growth.
Time (days)
One phase decay
Best-fit values
Y0 -0.01199
Plateau 2.969
K 1.202e-005
Half Life 57677
Tau 83210
Span -2.981
Std. Error
Y0 0.3634
Plateau 0.3635
K 5.288e-006
Span 0.4722
95% Confidence Intervals
Y0 -4.629 to 4.605
Figure 8 Growth curve for Selenastrum capricornutum
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Plateau -1.649 to 7.588
K 0.0 to 7.921e-005
Half Life 8751 to +infinity
Tau 12625 to +infinitySpan -8.981 to 3.019
Goodness of Fit
Degrees of Freedom 1
R square 0.9755
Absolute Sum of Squares 0.1223
Sy.x 0.3498
Constraints
K K > 0.0
Number of points
Analyzed 4
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