Laboratory Manual for
Human Physiology
Biology 141L
8th Edition – 2019
Grossmont College
Compiled by
Sue Caldwell, Angela Didomenico,
Allison Shearer, Ruth Botten
1
Exercise 1:
Data Collection, Table and Graph Construction
Introduction-A characteristic that varies from one time to another time or from one
biological entity to another is known as a variable. A variable may be either observed
qualitatively or measured quantitatively: These observations or measurements are known
as data (a single observation is a datum or item).
The purpose of this exercise is to familiarize you with conventional ways of expressing data
in tables and graphs, and to give you some practice analyzing these.
I. Qualitative Data
Sometimes the variable under study is classified by some quality it possesses rather
than by a numerical measurement. There are two types of qualitative data :
A. Nominal scale data
Example: Genetic phenotypes are commonly encountered biological
attributes; for instance, eye color or hair color.
Subject
Eye Color
A
blue
B
brown
C
brown
D
blue
B. Ordinal scale data
Example: Results of Benedict’s test performed on different solutions may be
classified as (-), (+), (++), (+++), (++++), meaning various intensities of color
are observed.
Test
Serum Benedict’s Test
A
_
B
++
C
+
2
C. Simple Tables -For a limited number of observations, data may be organized into
simple tables:
Serum Tests (Relative Amounts of Organic Molecules)
Sample
Glucose
Lipid
Urea
Protein
A
+
–
++
+
B
++
+
C
–
++
+++
–
D
+
+
–
++
–
+
II. Quantitative Data – When actual numerical values are determined for a variable, the
data are quantitative. Most of the data you will analyze this semester will be quantitative
data.
A. Frequency table: An easy way to represent data collected is a frequency table.
This method is used to make data more understandable or “analyzable.”
Example: Frequency Table: Eye Color in Physiology Students
Eye Color
Number
Percent of Total
Blue
126
21
Brown
483
79
B. Measurement table: This type of table may be used for any quantity that is
measured, such as weight, height, or length.
Table: Body Weights (g) of Male Wistar Rats
Rat #
Body Weight (g)
1
72
2
85
3
64
4
73
5
82
3
III. Graphs – Graphs are found in many forms, but are all pictorial representations of data
contained in tables.
Example 1 – Bar Graph: The table data from the previous page for “Eye Color in
Physiology Students” has been used to construct the bar graph below. Each axis
(vertical, horizontal line) should be labeled clearly, including units used, if
applicable. A space should be left between bars and vertical axis. Vertical axis
enumeration should begin at zero, if possible.
Eye Color in Physiology Students
Frequency (numbers)
500
400
300
200
100
0
Blue
Brown
Eye Color
Example 2: Line graphs: Independent and Dependent Variables
The relationship between two variables may be one of functional dependence of one on the
other.
Dependence means that the value of one of the variables (the dependent variable) is
assumed to be determined by the second variable (independent). For example, blood
pressure may be dependent on age in humans.
In experimental physiology, the independent variable is often a physical factor (e.g., age,
temperature, pH) or a treatment (e.g., a drug dosage), while the dependent variable is
some biological variable observed in the organism or system treated.
Table (example): Oxygen consumption of sparrows at different temperatures
4
O2 consumed
(ml/g/hr)
Temperature (°C)
5.2
-18
4.5
-10
3.4
0
3.1
5
2.7
10
In graphing these data, one always places the independent variable on the horizontal (x)
axis.
Data may also be analyzed along a single line using linear regression analysis. Linear
regressions are typically performed by a computer program such as MS Excel. (Refer to
Excel instruction page) The power of a linear regression analysis is that the data has been
statistically analyzed and can be assessed for the quality of data presented. The data
interpretation is usually in the form of the equation: y= mx + b. This should include an R2
value. The closer an R2 value is to 1, the higher the quality of data presented. Linear
regression data does extrapolate the data line plotted on the graph. The graph on the next
page represents the temperature of dark chocolate heated over boiling water (you don’t
want to scorch the chocolate) over time. Cooking schools could use this representation in
order to carefully “time” the preparation of a dessert. The linear regression for this graph
can be expressed with the equation: Y = X + 20
(Hint) If you need to show an extrapolation (projected data that lies beyond the range of
data), or interpolation (projected data that lies within the range of data) on your graph, use
a dotted line.
Temperature of Dark Chocolate Heated at 100°C Over Time
5
F. Nonlinear Relationships
Often when we measure two variables at a time a straight-line or linear relationship is not
indicated. The data do not appear to fit a straight line.
If the relationship between the variables is thought to be a continuous one and if the points
seem to fall along a smooth curve, it may or may not be appropriate to draw a line of best
fit – alternatively, a more conservative presentation of the data is to simply draw a line
directly connecting the points.
TABLE: Metabolic rate of mice over time
Time (Day)
Metabolic
rate
(cal/hr/g)
1
2
3
4
5
6
50
35
10
25
10
25
6
Metabolic rate mice
(calories/hr/gram)
50
•
•
•
25
•
•
•
0
1
2
3
4
5
6
Time (Days)
III. Comparing Two or More Dependent Variables
Many line graphs compare two or more dependent variables over a single independent
variable. One example might be the average heart rate of two groups of individuals over a
period of time (athletes vs. couch potatoes). In this case it is customary to include two
differently colored or differently shaped lines to represent the two groups. The two different
groups can be identified in a legend that can be imbedded in your graph.
Control Group
XXXXXXXXXX
Experimental Group ●●●●●●●●●●●●
Often this data is analyzed usingAthlete
an unpaired t test. Fortunately, this statistical analysis is
usually performed using a computer program such as MS Excel. (Refer to Excel instruction
pages). An unpaired t test determines if a difference in data across two groups is due to an
experimental variable or due to random chance. The data gleaned from this analysis is
called a p value. P values represent the chance that differences in data are due to random
chance (technically speaking, this is used to “reject the null hypothesis”). P values most
often have a cut off of 0.05. For example, if data between two groups have a P value of
0.10 that means that there is a 10% chance that the differences were due to random
chance and not experimental design. The cut-off value of 0.05 means that there is a 5%
chance that the difference is due to random chance (or a 95% chance that the difference
was due to an experimental variable).
7
Here are a few straight-forward videos on:
standard deviation: (https://www.youtube.com/watch?v=09kiX3p5Vek ),
standard error of the mean:
(https://www.youtube.com/watch?v=BwYj69LAQOI&list=PLllVwaZQkS2omBpLjQm_BAQK
sQ7lq86ku&index=3 ),
confidence intervals (https://www.youtube.com/watch?v=w3tM-PMThXk )
t-test (https://www.youtube.com/watch?v=pTmLQvMM-1M ), and
Chi-squared (https://www.youtube.com/watch?v=WXPBoFDqNVk )
General Rules for ALL Tables and Graphs (Required)
1. Tables and graphs should always have titles that give a complete summary of the data
presented.
2. All variables should be identified with labels and the units of measurement.
3. When graphs are constructed, be sure that the scales show each interval (box on the graph
paper) to be equal in value. This is called the scale factor. The scale factors on your graph
should be easy for the reader to determine. While the scale factor must be the same for the
entire x or y axis, the x and y scale factors do not have to equal each other.
4. Graphs should be as large as possible without resorting to awkward scale factors. Fill the
entire page if possible.
5. All graphs must include a 2-3 sentence caption summarizing the data and including any
statistical analyses.
8
Instructions for Using Microsoft Excel 2013 (Courtesy of Bonnie Ripley)
OPEN THE PROGRAM MICROSOFT OFFICE EXCEL AND THE .XLS or .XLSX FILE OF YOUR DATA
I.
Making a HISTOGRAM
a.
Have your (quantitative) data typed in one column (not shown)
b.
On the Data tab, click on Data Analysis
c.
Click on the Histogram option and click OK.
d.
Click in the “Input Range” box and highlight the data, then click in “Output Range”
and select an empty cell to the right of your data;
e.
Click the “Chart Output” box then click OK.
f.
Fix the histogram’s formatting:
i.
Right-click on some part of the graph and select the bottom choice in the pop-up list
which will probably be “Format Chart Area”. The chart dialog panel will appear on
the right. As you click on different parts of the graph your options in the panel will
change.
ii.
Click on the bars of the histogram to get the “Format Data Series” panel.
iii.
Click on the paint can icon and change the color scheme to black and white or grey
using the Border and Fill options.
iv.
Click on the bar chart icon and eliminate gaps between bars sliding the “gap width”
bar to 0.
v.
Fix the “More” on the x-axis by editing the More box in the data table (Bin column)
by calculating the correct value for it in the data table
vi.
Correct digits shown on x-axis by highlighting cells in data table, right-clicking,
selecting Format Cells, going to the Number tab and the Number Category. Set
appropriate decimal places.
9
II.
Making a BAR CHART
a.
Highlighting the data table including Titles
b.
Go to the Insert tab, and click the first Column graph button (clustered).
i.
Right click on the graph and click on Format Plot Area so the options bar
appears at the right of window
ii.
Click on a bar of the graph to get the Series formatting options. Change the
color scheme to black & white or grey by clicking on the paint bucket and changing
the Fill and Border characteristics.
iii.
Make sure you are clicked on the graph and go to Chart Tools in the top row
of tabs. Select the Design tab and go to Add Chart Element to add x-axis and y-axis
titles.
iv.
Move the Legend to the right side of the graph by right-clicking it and
selecting the appropriate option in the right-side bar.
Birth
Female
Male
0.48
0.52
Make sure to leave the cell in the top left of your data table empty!!!
Age > 65
0.58
0.42
Frequency
v.
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Female
Male
Birth
Age > 65
Life Stage
10
III.
Instructions for making a LINE GRAPH with ORDINAL X-Axis
a.
This graph is appropriate for an x-axis label that is ordinal (in numbers like years)
b.
Highlight the two columns of data (excluding titles), with the x-axis data on the left.
c.
Go to the Insert tab, and click the Scatter Plot graph button (Do not use Line Chart!).
d.
Select the Scatter with Straight Lines and Markers option
e.
Make sure you are clicked on the graph and go to Chart Tools in the top row of tabs.
On the Design tab, select Add Chart Element to add x-axis and y-axis titles.
Year
1980
1981
1982
1983
1984
1985
Ring Width
20.8
13.2
15.1
12.8
14.4
10.2
Tree Ring Width (dm)
f.
Click on a bar of the graph to get the Series formatting options. Change the color
scheme to black & white or grey by clicking on the paint bucket and changing the Fill and
Border characteristics.
25
20
15
10
5
0
1979
1980
1981
1982
1983
1984
1985
1986
Year
V.
Instructions for making a SCATTER PLOT
a.
This graph is appropriate for two quantitate variables
b.
Highlight the two columns of data (not titles), with the x-axis variable on the left.
c.
Go to the Insert tab, and click the Scatter Plot graph button.
d.
Select the Markers Only
i.
Delete legend and change the color scheme to Black & White by rightclicking on the graph, selecting Format Data Series, then changing series
characteristics under the paint icon menu in the right-side panel
ii.
Add Axis Titles (Horizontal and Vertical) from the Design tab on the Chart
toolbar.
e.
To add a regression line to the graph
11
i.
Click on graph then select the Design tab on the Chart toolbar. Under Add
Chart Element, select Add Trendline from the dropdown menu
Length
Width
10.7
5.8
11
6
9.5
5
11.1
6
10.3
5.3
10.7
5.8
Select Linear, and click in boxes to display Equation and display R2
6.5
Chiton Width (cm)
ii.
6
5.5
5
y = 0.6793x – 1.5166
R² = 0.9478
4.5
4
9
9.5
10
10.5
Chiton Length (cm)
12
11
11.5
M/F Age (Decade)
F
M
F
M
M
F
F
F
F
F
F
F
M
m
m
F
f
f
f
f
F
F
f
Sitting
Right (Sitting)
Left (Sitting)
Supine
Right (Supine)
HR
Systolic Diastolic Systolic Diastolic
HR
Systolic
20
85
112
84
112
86
70
112
40
89
142
89
135
80
89
138
20
58
118
52
118
65
50
128
20
76
124
87
115
78
69
124
20
86
126
92
124
81
83
118
20
66
102
67
99
66
56
101
20
75
121
84
118
76
63
122
20
90
98
68
96
74
80
102
20
76
101
55
99
62
62
111
20
79
92
67
92
69
75
106
20
90
114
78
106
72
79
102
20
92
110
70
102
78
80
110
20
58
113
59
109
68
59
115
20
73
110
72
115
79
68
116
30
73
129
83
130
79
65
126
21
57
107
68
104
68
58
115
20
74
100
74
101
70
69
108
10
74
78
56
80
51
64
86
30
45
120
73
128
80
45
136
10
95
114
82
107
83
91
110
20
76
109
70
84
51
69
146
40
63
113
79.5
114.5
79
53.5
123
20
74
92.5
65
86
63
74
90
Means=
74.95652 110.6739 72.80435 107.587 72.08696 68.326087
115
Standard dev= 12.53336 13.5435 10.95122 14.34713 9.226206 11.7358691 14.18818
Right (Supine)
Left (Supine)
Diastolic Systolic Diastolic
HR
78
112
80
79
140
82
0
64
120
54
86
120
88
83
133
86
59
101
67
79
111
64
70
93
67
55
106
64
69
96
65
68
108
84
72
112
76
60
111
65
+
72
110
77
71
118
67
66
112
66
+
68
101
73
57
86
56
77
134
75
0
71
113
79
98
139
85
79
116
75.5
62
92
62
0
71.43478 112.3478 72.06522 0.002213
9.92943 14.1563 9.459645
Blood Pressure Lab Write-up:
Produce a lab write up for the body position activity of the Blood Pressure lab. Your
report should be in nonnumeric, paragraph form. The data you need for the report is
found in an Excel file posted on Canvas. The data includes subjects reported sex and age
(decade, not specific year) as well as collected heart rates and data for sitting (resting)
and supine blood pressure.
1. Write an Introduction Section (3 or 4 paragraphs). A brief overview of blood pressure
should be included in this section. Don’t forget to include a citation when you provide
information which is not common knowledge. For example, your text (Silverthorn,
2019), lab manual (Caldwell, Didomenico, Shearer, & Botten, 2019), or another
reasonable source. Make sure to include brief explanations of the physiology underlying
the measurement of blood pressure and how to interpret the blood pressure reading.
2. Write a Methods Section. State the hypotheses or predictions.
Write 2-3 paragraphs which give an overview of how you did your experiment. Cite our
lab manual (Caldwell, Didomenico, Shearer, & Botten, 2019), and then concisely
summarize how the data was collected. Write a paragraph describing your test subjects.
Write 1-2 paragraphs which gives a general overview of how you used the methods and
baseline data to study the effects of changes in body position on blood pressure and heart
rate. What is the independent variable of this experiment? The dependent variable?
How are you analyzing the data?
3. Complete a Results Section. Include at least one graph and a summary table or graph.
You may need more than one. Any table or graph should have a descriptive title. Make
sure you submit a summary table, not a restatement of raw data. I smile when I see
calculated measures of central tendency (mean and median) and some measure of spread.
Excel can do most of those calculations for you.
Your graph should show class averages of the blood pressure component in your
hypothesis for each body position. You should include a short narrative highlighting
your data in this section.
4. Write a Summary Section. (4-5 paragraphs) Explain your data by using information
you introduced in your introduction. Make sure to demonstrate you understand the
underlying physiology explaining why blood pressure changes in response to changes in
body position. Refer to your table and graph and point out any important data. If your
results did not match your expected results, write a paragraph suggesting one reason your
results might have been different from the expected results.
5. Prepare a References Cited section. Make sure to include all sources you cite in your
write up. Do not include sources you do not cite. References Cited. You need to provide
you reader with enough information so they can find your sources. You should be
consistent and thorough. You need to use your textbook and the lab manual. You may use
other sources such as websites if you feel the need. Provide enough detail so your reader
can find the information if they desire.
Below are examples for single authors, multiple authors, and websites:
Silverthorn, DU. 2019. Human Physiology: An Integrated Approach (8th Edition).
Pearson Publishing.
S. Caldwell, A. Didomenico, A. Shearer, and R. Botten. 2019. Laboratory Manual for
Human Physiology Grossmont College (8th Edition).
Baroreflex Regulation of Blood Pressure Animation. Online. Alila Medical Media.
Available: https://www.youtube.com/watch?v=X3BCFOlk1oQ. Updated October 1, 2020
[Accessed October 30, 2022]
Mean
Standard
Deviation
SE =
Standard Error
ME =
Margin of Error
2 SE (Stand Error)
Purchase answer to see full
attachment
Human Physiology
Biology 141L
8th Edition – 2019
Grossmont College
Compiled by
Sue Caldwell, Angela Didomenico,
Allison Shearer, Ruth Botten
1
Exercise 1:
Data Collection, Table and Graph Construction
Introduction-A characteristic that varies from one time to another time or from one
biological entity to another is known as a variable. A variable may be either observed
qualitatively or measured quantitatively: These observations or measurements are known
as data (a single observation is a datum or item).
The purpose of this exercise is to familiarize you with conventional ways of expressing data
in tables and graphs, and to give you some practice analyzing these.
I. Qualitative Data
Sometimes the variable under study is classified by some quality it possesses rather
than by a numerical measurement. There are two types of qualitative data :
A. Nominal scale data
Example: Genetic phenotypes are commonly encountered biological
attributes; for instance, eye color or hair color.
Subject
Eye Color
A
blue
B
brown
C
brown
D
blue
B. Ordinal scale data
Example: Results of Benedict’s test performed on different solutions may be
classified as (-), (+), (++), (+++), (++++), meaning various intensities of color
are observed.
Test
Serum Benedict’s Test
A
_
B
++
C
+
2
C. Simple Tables -For a limited number of observations, data may be organized into
simple tables:
Serum Tests (Relative Amounts of Organic Molecules)
Sample
Glucose
Lipid
Urea
Protein
A
+
–
++
+
B
++
+
C
–
++
+++
–
D
+
+
–
++
–
+
II. Quantitative Data – When actual numerical values are determined for a variable, the
data are quantitative. Most of the data you will analyze this semester will be quantitative
data.
A. Frequency table: An easy way to represent data collected is a frequency table.
This method is used to make data more understandable or “analyzable.”
Example: Frequency Table: Eye Color in Physiology Students
Eye Color
Number
Percent of Total
Blue
126
21
Brown
483
79
B. Measurement table: This type of table may be used for any quantity that is
measured, such as weight, height, or length.
Table: Body Weights (g) of Male Wistar Rats
Rat #
Body Weight (g)
1
72
2
85
3
64
4
73
5
82
3
III. Graphs – Graphs are found in many forms, but are all pictorial representations of data
contained in tables.
Example 1 – Bar Graph: The table data from the previous page for “Eye Color in
Physiology Students” has been used to construct the bar graph below. Each axis
(vertical, horizontal line) should be labeled clearly, including units used, if
applicable. A space should be left between bars and vertical axis. Vertical axis
enumeration should begin at zero, if possible.
Eye Color in Physiology Students
Frequency (numbers)
500
400
300
200
100
0
Blue
Brown
Eye Color
Example 2: Line graphs: Independent and Dependent Variables
The relationship between two variables may be one of functional dependence of one on the
other.
Dependence means that the value of one of the variables (the dependent variable) is
assumed to be determined by the second variable (independent). For example, blood
pressure may be dependent on age in humans.
In experimental physiology, the independent variable is often a physical factor (e.g., age,
temperature, pH) or a treatment (e.g., a drug dosage), while the dependent variable is
some biological variable observed in the organism or system treated.
Table (example): Oxygen consumption of sparrows at different temperatures
4
O2 consumed
(ml/g/hr)
Temperature (°C)
5.2
-18
4.5
-10
3.4
0
3.1
5
2.7
10
In graphing these data, one always places the independent variable on the horizontal (x)
axis.
Data may also be analyzed along a single line using linear regression analysis. Linear
regressions are typically performed by a computer program such as MS Excel. (Refer to
Excel instruction page) The power of a linear regression analysis is that the data has been
statistically analyzed and can be assessed for the quality of data presented. The data
interpretation is usually in the form of the equation: y= mx + b. This should include an R2
value. The closer an R2 value is to 1, the higher the quality of data presented. Linear
regression data does extrapolate the data line plotted on the graph. The graph on the next
page represents the temperature of dark chocolate heated over boiling water (you don’t
want to scorch the chocolate) over time. Cooking schools could use this representation in
order to carefully “time” the preparation of a dessert. The linear regression for this graph
can be expressed with the equation: Y = X + 20
(Hint) If you need to show an extrapolation (projected data that lies beyond the range of
data), or interpolation (projected data that lies within the range of data) on your graph, use
a dotted line.
Temperature of Dark Chocolate Heated at 100°C Over Time
5
F. Nonlinear Relationships
Often when we measure two variables at a time a straight-line or linear relationship is not
indicated. The data do not appear to fit a straight line.
If the relationship between the variables is thought to be a continuous one and if the points
seem to fall along a smooth curve, it may or may not be appropriate to draw a line of best
fit – alternatively, a more conservative presentation of the data is to simply draw a line
directly connecting the points.
TABLE: Metabolic rate of mice over time
Time (Day)
Metabolic
rate
(cal/hr/g)
1
2
3
4
5
6
50
35
10
25
10
25
6
Metabolic rate mice
(calories/hr/gram)
50
•
•
•
25
•
•
•
0
1
2
3
4
5
6
Time (Days)
III. Comparing Two or More Dependent Variables
Many line graphs compare two or more dependent variables over a single independent
variable. One example might be the average heart rate of two groups of individuals over a
period of time (athletes vs. couch potatoes). In this case it is customary to include two
differently colored or differently shaped lines to represent the two groups. The two different
groups can be identified in a legend that can be imbedded in your graph.
Control Group
XXXXXXXXXX
Experimental Group ●●●●●●●●●●●●
Often this data is analyzed usingAthlete
an unpaired t test. Fortunately, this statistical analysis is
usually performed using a computer program such as MS Excel. (Refer to Excel instruction
pages). An unpaired t test determines if a difference in data across two groups is due to an
experimental variable or due to random chance. The data gleaned from this analysis is
called a p value. P values represent the chance that differences in data are due to random
chance (technically speaking, this is used to “reject the null hypothesis”). P values most
often have a cut off of 0.05. For example, if data between two groups have a P value of
0.10 that means that there is a 10% chance that the differences were due to random
chance and not experimental design. The cut-off value of 0.05 means that there is a 5%
chance that the difference is due to random chance (or a 95% chance that the difference
was due to an experimental variable).
7
Here are a few straight-forward videos on:
standard deviation: (https://www.youtube.com/watch?v=09kiX3p5Vek ),
standard error of the mean:
(https://www.youtube.com/watch?v=BwYj69LAQOI&list=PLllVwaZQkS2omBpLjQm_BAQK
sQ7lq86ku&index=3 ),
confidence intervals (https://www.youtube.com/watch?v=w3tM-PMThXk )
t-test (https://www.youtube.com/watch?v=pTmLQvMM-1M ), and
Chi-squared (https://www.youtube.com/watch?v=WXPBoFDqNVk )
General Rules for ALL Tables and Graphs (Required)
1. Tables and graphs should always have titles that give a complete summary of the data
presented.
2. All variables should be identified with labels and the units of measurement.
3. When graphs are constructed, be sure that the scales show each interval (box on the graph
paper) to be equal in value. This is called the scale factor. The scale factors on your graph
should be easy for the reader to determine. While the scale factor must be the same for the
entire x or y axis, the x and y scale factors do not have to equal each other.
4. Graphs should be as large as possible without resorting to awkward scale factors. Fill the
entire page if possible.
5. All graphs must include a 2-3 sentence caption summarizing the data and including any
statistical analyses.
8
Instructions for Using Microsoft Excel 2013 (Courtesy of Bonnie Ripley)
OPEN THE PROGRAM MICROSOFT OFFICE EXCEL AND THE .XLS or .XLSX FILE OF YOUR DATA
I.
Making a HISTOGRAM
a.
Have your (quantitative) data typed in one column (not shown)
b.
On the Data tab, click on Data Analysis
c.
Click on the Histogram option and click OK.
d.
Click in the “Input Range” box and highlight the data, then click in “Output Range”
and select an empty cell to the right of your data;
e.
Click the “Chart Output” box then click OK.
f.
Fix the histogram’s formatting:
i.
Right-click on some part of the graph and select the bottom choice in the pop-up list
which will probably be “Format Chart Area”. The chart dialog panel will appear on
the right. As you click on different parts of the graph your options in the panel will
change.
ii.
Click on the bars of the histogram to get the “Format Data Series” panel.
iii.
Click on the paint can icon and change the color scheme to black and white or grey
using the Border and Fill options.
iv.
Click on the bar chart icon and eliminate gaps between bars sliding the “gap width”
bar to 0.
v.
Fix the “More” on the x-axis by editing the More box in the data table (Bin column)
by calculating the correct value for it in the data table
vi.
Correct digits shown on x-axis by highlighting cells in data table, right-clicking,
selecting Format Cells, going to the Number tab and the Number Category. Set
appropriate decimal places.
9
II.
Making a BAR CHART
a.
Highlighting the data table including Titles
b.
Go to the Insert tab, and click the first Column graph button (clustered).
i.
Right click on the graph and click on Format Plot Area so the options bar
appears at the right of window
ii.
Click on a bar of the graph to get the Series formatting options. Change the
color scheme to black & white or grey by clicking on the paint bucket and changing
the Fill and Border characteristics.
iii.
Make sure you are clicked on the graph and go to Chart Tools in the top row
of tabs. Select the Design tab and go to Add Chart Element to add x-axis and y-axis
titles.
iv.
Move the Legend to the right side of the graph by right-clicking it and
selecting the appropriate option in the right-side bar.
Birth
Female
Male
0.48
0.52
Make sure to leave the cell in the top left of your data table empty!!!
Age > 65
0.58
0.42
Frequency
v.
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Female
Male
Birth
Age > 65
Life Stage
10
III.
Instructions for making a LINE GRAPH with ORDINAL X-Axis
a.
This graph is appropriate for an x-axis label that is ordinal (in numbers like years)
b.
Highlight the two columns of data (excluding titles), with the x-axis data on the left.
c.
Go to the Insert tab, and click the Scatter Plot graph button (Do not use Line Chart!).
d.
Select the Scatter with Straight Lines and Markers option
e.
Make sure you are clicked on the graph and go to Chart Tools in the top row of tabs.
On the Design tab, select Add Chart Element to add x-axis and y-axis titles.
Year
1980
1981
1982
1983
1984
1985
Ring Width
20.8
13.2
15.1
12.8
14.4
10.2
Tree Ring Width (dm)
f.
Click on a bar of the graph to get the Series formatting options. Change the color
scheme to black & white or grey by clicking on the paint bucket and changing the Fill and
Border characteristics.
25
20
15
10
5
0
1979
1980
1981
1982
1983
1984
1985
1986
Year
V.
Instructions for making a SCATTER PLOT
a.
This graph is appropriate for two quantitate variables
b.
Highlight the two columns of data (not titles), with the x-axis variable on the left.
c.
Go to the Insert tab, and click the Scatter Plot graph button.
d.
Select the Markers Only
i.
Delete legend and change the color scheme to Black & White by rightclicking on the graph, selecting Format Data Series, then changing series
characteristics under the paint icon menu in the right-side panel
ii.
Add Axis Titles (Horizontal and Vertical) from the Design tab on the Chart
toolbar.
e.
To add a regression line to the graph
11
i.
Click on graph then select the Design tab on the Chart toolbar. Under Add
Chart Element, select Add Trendline from the dropdown menu
Length
Width
10.7
5.8
11
6
9.5
5
11.1
6
10.3
5.3
10.7
5.8
Select Linear, and click in boxes to display Equation and display R2
6.5
Chiton Width (cm)
ii.
6
5.5
5
y = 0.6793x – 1.5166
R² = 0.9478
4.5
4
9
9.5
10
10.5
Chiton Length (cm)
12
11
11.5
M/F Age (Decade)
F
M
F
M
M
F
F
F
F
F
F
F
M
m
m
F
f
f
f
f
F
F
f
Sitting
Right (Sitting)
Left (Sitting)
Supine
Right (Supine)
HR
Systolic Diastolic Systolic Diastolic
HR
Systolic
20
85
112
84
112
86
70
112
40
89
142
89
135
80
89
138
20
58
118
52
118
65
50
128
20
76
124
87
115
78
69
124
20
86
126
92
124
81
83
118
20
66
102
67
99
66
56
101
20
75
121
84
118
76
63
122
20
90
98
68
96
74
80
102
20
76
101
55
99
62
62
111
20
79
92
67
92
69
75
106
20
90
114
78
106
72
79
102
20
92
110
70
102
78
80
110
20
58
113
59
109
68
59
115
20
73
110
72
115
79
68
116
30
73
129
83
130
79
65
126
21
57
107
68
104
68
58
115
20
74
100
74
101
70
69
108
10
74
78
56
80
51
64
86
30
45
120
73
128
80
45
136
10
95
114
82
107
83
91
110
20
76
109
70
84
51
69
146
40
63
113
79.5
114.5
79
53.5
123
20
74
92.5
65
86
63
74
90
Means=
74.95652 110.6739 72.80435 107.587 72.08696 68.326087
115
Standard dev= 12.53336 13.5435 10.95122 14.34713 9.226206 11.7358691 14.18818
Right (Supine)
Left (Supine)
Diastolic Systolic Diastolic
HR
78
112
80
79
140
82
0
64
120
54
86
120
88
83
133
86
59
101
67
79
111
64
70
93
67
55
106
64
69
96
65
68
108
84
72
112
76
60
111
65
+
72
110
77
71
118
67
66
112
66
+
68
101
73
57
86
56
77
134
75
0
71
113
79
98
139
85
79
116
75.5
62
92
62
0
71.43478 112.3478 72.06522 0.002213
9.92943 14.1563 9.459645
Blood Pressure Lab Write-up:
Produce a lab write up for the body position activity of the Blood Pressure lab. Your
report should be in nonnumeric, paragraph form. The data you need for the report is
found in an Excel file posted on Canvas. The data includes subjects reported sex and age
(decade, not specific year) as well as collected heart rates and data for sitting (resting)
and supine blood pressure.
1. Write an Introduction Section (3 or 4 paragraphs). A brief overview of blood pressure
should be included in this section. Don’t forget to include a citation when you provide
information which is not common knowledge. For example, your text (Silverthorn,
2019), lab manual (Caldwell, Didomenico, Shearer, & Botten, 2019), or another
reasonable source. Make sure to include brief explanations of the physiology underlying
the measurement of blood pressure and how to interpret the blood pressure reading.
2. Write a Methods Section. State the hypotheses or predictions.
Write 2-3 paragraphs which give an overview of how you did your experiment. Cite our
lab manual (Caldwell, Didomenico, Shearer, & Botten, 2019), and then concisely
summarize how the data was collected. Write a paragraph describing your test subjects.
Write 1-2 paragraphs which gives a general overview of how you used the methods and
baseline data to study the effects of changes in body position on blood pressure and heart
rate. What is the independent variable of this experiment? The dependent variable?
How are you analyzing the data?
3. Complete a Results Section. Include at least one graph and a summary table or graph.
You may need more than one. Any table or graph should have a descriptive title. Make
sure you submit a summary table, not a restatement of raw data. I smile when I see
calculated measures of central tendency (mean and median) and some measure of spread.
Excel can do most of those calculations for you.
Your graph should show class averages of the blood pressure component in your
hypothesis for each body position. You should include a short narrative highlighting
your data in this section.
4. Write a Summary Section. (4-5 paragraphs) Explain your data by using information
you introduced in your introduction. Make sure to demonstrate you understand the
underlying physiology explaining why blood pressure changes in response to changes in
body position. Refer to your table and graph and point out any important data. If your
results did not match your expected results, write a paragraph suggesting one reason your
results might have been different from the expected results.
5. Prepare a References Cited section. Make sure to include all sources you cite in your
write up. Do not include sources you do not cite. References Cited. You need to provide
you reader with enough information so they can find your sources. You should be
consistent and thorough. You need to use your textbook and the lab manual. You may use
other sources such as websites if you feel the need. Provide enough detail so your reader
can find the information if they desire.
Below are examples for single authors, multiple authors, and websites:
Silverthorn, DU. 2019. Human Physiology: An Integrated Approach (8th Edition).
Pearson Publishing.
S. Caldwell, A. Didomenico, A. Shearer, and R. Botten. 2019. Laboratory Manual for
Human Physiology Grossmont College (8th Edition).
Baroreflex Regulation of Blood Pressure Animation. Online. Alila Medical Media.
Available: https://www.youtube.com/watch?v=X3BCFOlk1oQ. Updated October 1, 2020
[Accessed October 30, 2022]
Mean
Standard
Deviation
SE =
Standard Error
ME =
Margin of Error
2 SE (Stand Error)
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