Linear regression|Legit essays

Posted: January 29th, 2023

sampling site more important than the other for conservation?

The data from the first 20 quadrats are:

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Site id Pinus banksiana Thuja occidentalis Dasiphora fruticosa A 1 1 1 A 0 0 1 A 1 1 1 A 0 0 1 A 0 0 1 A 1 1 1 A 1 1 1 A 0 1 1 A 1 1 1 A 0 0 1 B 0 0 1 B 0 1 1 B 1 1 1 B 0 1 1 B 1 1 1 B 0 1 1 B 1 1 1 B 1 0 1 B 1 1 1 B 1 1 1

3. One of the key species in this community, jack pine, may be impacted by climate change in Ontario. In his undergraduate thesis, Gary Whelan examined climate factors that may affect jack pine growth. I have provided digital versions of data from his thesis on tree volume (Appendix 1.1. Average provenance volumes (dmˆ3); saved as “Whelan2020volumes.csv”) and various climate factors (Appendix 1.11. Climatic variables for each provenance at Fraserdale from 1971-2000; saved as “Whelan2020Fraserdaleclimate1971-2000.txt”).

a. Using the data provided, plot the tree volume at the Fraserdale site vs the average minimum winter temperature (named “Tmin_wt”). Remember to check that your data import has been successful.

b. Using a linear regression, determine if a linear function adequately characterizes the relationship between tree volume and minimum winter temperature.

c. What evidence supports or fails to support your claim?

d. Superimpose the fitted regression line on your data plot.

e. Using your plot and regression, provide a description of the niche of jack pine with respect to minimum winter temperatures.


4. Your alvar community data was only provided to you as a .pdf. I converted the data from Whelan (2020) for you, but this was also provided only as a .pdf by the author. Describe any difficulties you experienced as a result of this data format.

Literature cited

Whelan, G. (2020). Jack pine fitness under current and future climate (Undergraduate Thesis). Faculty of Natural Resources Management, Lakehead University, Thunder Bay, Ontario.



Linear regression is a statistical method used to model the relationship between a dependent variable (often denoted as Y) and one or more independent variables (often denoted as X). The goal of linear regression is to find the line of best fit that minimizes the sum of the squared differences between the predicted values (based on the line) and the actual values.

The line of best fit is represented by an equation of the form Y = a + bX, where a and b are coefficients determined by the linear regression analysis. Linear regression is a simple and widely used method for modeling and predicting numerical data.

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