Description

Activity 3: Metabolism Laboratory Report

Laboratory Report

You must complete the report using Excel and/or statistics software. Analysis is performed on the entire MEDI211 class data set (No class this time because of the restrictions and use video!

⦁ Enter the data from the subject in the video into the table below. (2 decimal points where appropriate). (It is attached)

Table 2.1: Raw Data Table

Subject Initials Gender Height (m) Mass (kg) BSA

(m2) VXO2

(L.min-1) RMR

(kJ.hr-1) RMR

(kJ.m-2.hr-1) RMR

(kJ.kg-1.hr-1)

FL F 1.69 61.0

⦁ Perform the following statistical computations on the numerical variables (height, mass, surface area, oxygen consumption, RMR (kJ.hr-1), RMR (kJ.m-2.hr-1), and RMR (kJ.kg-1.hr-1):

⦁ mean (males and females separately)

⦁ standard deviation (males and females separately)

⦁ standard error of the mean (males and females separately)

⦁ student T-test to obtain a p value for males versus females.

Watch the excel tutorial video

(https://www.youtube.com/watch?v=c4ugzVwsHdg) for 17’15” and see the Appendix 1 for detailed instructions on using Excel to perform the above statistical calculations.

⦁ Produce the following graphs, using different symbols for males and females:

⦁ mass (x axis) versus oxygen consumption (y axis)

⦁ surface area (x axis) versus resting metabolic rate (RMR: kJ.hr-1: y axis) See the tutorial video above and Appendix 2 for detailed instructions on using Excel to make graphs. Appendix 3 contains examples of what your graphs should look like (your data points will be different).

⦁ Write a 3-paragraph discussion (with complete scholarly reference citations) to interpret your data above, demonstrate your understanding of your graphs, and place your findings in the context of the literature. Consider why some of the variables may or may not have been different between males and females. Appendix 4 contains detailed instructions on the layout of your discussion.

As a general guide, the first page should contain your name etc., Table 1 showing the data you collected on the day of your class and your statistics table copied from Excel (with logical titles for tables) ( in the video). The 2nd page contains two graphs each with figure legends. The 3rd page contains discussion and references. Be mindful of plagiarism rules. Please see Appendix 5 for Marking Criteria.

Appendix 1: Metabolism Prac Report - Instructions for Statistical Analysis

Statistical analysis should be conducted on the whole class data file using Excel. (from video) The excel data file is attached. Student reports should not include a copy of the table/spreadsheet (i.e. the raw class data), but only the statistical analysis that was derived from the data.

⦁ Download excel file containing class data from Moodle (from video)

⦁ Sort data by gender. We will use male height as an example, then repeat for all other variables

⦁ Highlight all data in columns A-K, select Data tab, Sort, sort by: column C (the column corresponding to gender), OK

⦁ Mean (average) height:

⦁ Scroll across to column O, notice the template. This is where your statistics will be entered. This table forms a component of your lab report.

⦁ Click the field corresponding to: average male height

⦁ Perform the following command

=average(highlight all of the male height data), press Enter

⦁ N: number of male subjects:

⦁ Perform the following command

=count(highlight all male height data) (or count the number of males manually)

⦁ SD: standard deviation of the male height data:

⦁ Click the field corresponding to: Standard Deviation male height

⦁ Perform the following command

=stdev(highlight all of the male height data), press Enter

⦁ SEM: standard error of mean of the male height data:

⦁ SEM uses the following formula: SD/square root of N

⦁ Click the field corresponding to: Standard Error of Mean for male height

⦁ Modify the following command

=(click on the male height SD field/(sqrt(click on the male height N field))

⦁ Repeat steps 1-5 for female data

⦁ p: the probability that any significant difference was achieved purely by chance alone. We will only accept that there is a difference between two data sets if the p value is less than 0.05 (p<0.05)

⦁ Click the field corresponding to: height P

Instruction Files