ex. 1 data management

8/7/2019 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


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


F 76.84

DFn, DFd 1.000, 8.000P value < 0.0001


Data


Maximum number of Y replicates 1




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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


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


F 76.84

DFn, DFd 1.000, 8.000

P value < 0.0001


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Data


Maximum number of Y replicates 1Total number of values 10


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


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


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

ex. 1 data management

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