Basswood linear fit Notebook

Use the chunk of text below to get started on a linear fit to your data. This example loads data from the Leslie method worksheet for fish population estimation by depletion, then seeks a linear fit to that data and reports the results.

#data <- read.csv("Basswood_data_2024_clean1.csv", header = TRUE)
library(readxl)

#data <- read.csv("Basswood_data_2024_clean1.csv", header = TRUE)

#data <- data[, c("TreeID", "Diam_cm", "Height_m")]

colnames(data) <- c("TreeID", "dbh_cm", "total_height_m")

#linfit <- lm(height ~ dbh, data = clean_data)

summary(linfit)

Call:
lm(formula = height ~ dbh, data = clean_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9150 -3.0651  0.2877  2.4665  5.0298 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   9.3048     1.7679   5.263 0.000153 ***
dbh           0.2559     0.0389   6.578 1.78e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.47 on 13 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.769, Adjusted R-squared:  0.7512 
F-statistic: 43.27 on 1 and 13 DF,  p-value: 1.775e-05

plot(height ~ dbh, data = clean_data)

abline(linfit, col = "blue")

abline(linfit, col = "blue", lwd = 2)
summary(linfit)

Call:
lm(formula = height ~ dbh, data = clean_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9150 -3.0651  0.2877  2.4665  5.0298 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   9.3048     1.7679   5.263 0.000153 ***
dbh           0.2559     0.0389   6.578 1.78e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.47 on 13 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.769, Adjusted R-squared:  0.7512 
F-statistic: 43.27 on 1 and 13 DF,  p-value: 1.775e-05

abline(linfit )