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Linear Regression (Excel) and Cellular Respiration for Biology, Chemistry and Mathematics

Author(s): Irene Corriette1, Beatriz Gonzalez1, Daniela Kitanska2, Henriette Mozsolits2, Sheela Vemu3

1. Santa Fe College 2. Passaic County Community College 3. Waubonsee Community College

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Summary:
Students typically find linear regression analysis of data sets in a biology classroom challenging. These activities could be used in a Biology, Chemistry, Mathematics, or Statistics course. The collection provides student activity files with Excel…

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Students typically find linear regression analysis of data sets in a biology classroom challenging. These activities could be used in a Biology, Chemistry, Mathematics, or Statistics course. The collection provides student activity files with Excel instructions and Instructor Activity files with Excel instructions and solutions to problems. Students will be able to perform linear regression analysis, find correlation coefficient, create a scatter plot and find the r-square using MS Excel 365. Students will be able to interpret data sets, describe the relationship between biological variables, and predict the value of an output variable based on the input of an predictor variable.

Licensed under CC Attribution-ShareAlike 4.0 International according to these terms

Version 1.0 - published on 03 Dec 2021 doi:10.25334/5PX5-H796 - cite this

Keywords

  1. statistics
  2. mathematics
  3. data analysis
  4. data
  5. linear regression
  6. biology
  7. online
  8. graphing
  9. linear equations
  10. Quantitative reasoning
  11. assessment
  12. college algebra
  13. cell
  14. statistical analysis
  15. regression
  16. OER
  17. Algebra
  18. chemistry
  19. Models
  20. correlation
  21. Simple Linear Regression
  22. Microsoft Excel
  23. lab activity
  24. experiment
  25. Cellular Processes
  26. ATP
  27. qbcc
  28. Using Excel
  29. manipulate equations
  30. Cellular and Molecular Biology
  31. Concepts in Statistics
  32. oxidation
  33. line of best fit
  34. explaining statistics
  35. using statistics
  36. Cellular Respiration
  37. Predictions
  38. Respiration
  39. graphing in Excel
  40. Linear Correlation
  41. Exothermic Reaction
  42. create scatter plot
  43. Adenosine Triphosphate (ATP)
  44. germinating peas
  45. Pre-lab activity
  46. Student activity
  47. Hybrid
  48. Collecting Data
  49. R-square
  50. Regression Model
  51. Function
  52. Dependent Variables
  53. Independent Variables
  54. Linear Equation
  55. Linear Equation in two variables
  56. create graph
  57. create equation
  58. create regression
  59. create plot
  60. interpret regression
  61. interpret data

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  1. Resource Type
  2. Audience Level
  3. Open Science and Education Practices Ontology
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Description

Students typically find linear regression analysis of data sets in a biology classroom challenging. These activities could be used in a Biology, Chemistry, Mathematics, or Statistics course. The collection provides student activity files with Excel instructions and Instructor Activity files with Excel instructions and solutions to problems. 

Students will be able to perform linear regression analysis, find correlation coefficient, create a scatter plot and find the r-square using MS Excel 365. Students will be able to interpret data sets, describe the relationship between biological variables, and predict the value of an output variable based on the input of an predictor variable.

Upon completion of the activities students will be able to: 

  • Describe the function of cellular respiration

  • Identify the type of reaction of cellular respiration

  • Identify the reactants and products of the cellular respiration reaction

  • Interpret data sets to show the presence of CO2 as a product in cellular respiration 

  • Perform linear regression analysis using MS Excel 365

    • Interpret the regression output using the correlation coefficient

    • Construct a scatter plot using MS Excel 365

    • Use R-squared to judge the fit of the regression line

    • Describe the relationship between the biological variables presented in this experiment. 

    • Predict the value of an output variable (response) based on the input of an input (predictor) variable. 

 

This material is based upon work supported by the National Science Foundation under Grant No. 1919613. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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