lundquist-ecology-lab

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

# Load data
url = 'https://gist.githubusercontent.com/ZeccaLehn/4e06d2575eb9589dbe8c365d61cb056c/raw/898a40b035f7c951579041aecbfb2149331fa9f6/mtcars.csv'
data = pd.read_csv(url, index_col=0)
print(data.head(5))

lundquist-ecology-lab

from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load data
iris = load_iris()
X = iris.data
y = iris.target

# Split data into training and testing sets

lundquist-ecology-lab

# Load data
data <- iris

# Split data into training and testing sets
set.seed(123)
index <- sample(1:nrow(data), 0.8 * nrow(data))
train <- data[index,]
test <- data[-index,]

# Build ANN model

lundquist-ecology-lab

# Load data
data <- mtcars

# Build linear regression model
fit <- lm(mpg ~ wt + hp, data = data)

# Summarize the model
summary(fit)

lundquist-ecology-lab

library(rpart)

# Load data
data <- iris

# Build decision tree
fit <- rpart(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data = data, method = "class")

# Plot the decision tree
library(rpart.plot)

lundquist-ecology-lab

# Load data
import pandas as pd
data = pd.read_csv("data.csv")

# Run logistic regression analysis
from sklearn.linear_model import LogisticRegression
X = data[['age', 'income']]
y = data['default']
clf = LogisticRegression(random_state=0).fit(X, y)

lundquist-ecology-lab

library(tidyverse)

# Load data
data <- mtcars

# Run linear regression analysis
fit <- lm(mpg ~ wt, data = data)

# Provide ChatGPT with the summary of the model
summary_model <- summary(fit)

lundquist-ecology-lab

import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm

from sklearn.datasets import load_iris
from bioinfokit.analys import stat
from statsmodels.formula.api import ols

lundquist-ecology-lab

import scipy.stats as stats
import numpy as np

## One sample t
data=[13, 14, 13, 12, 14, 15, 16, 13, 14, 12]

x=stats.ttest_1samp(a=data, popmean=15)
print(x)

# Two sample t

lundquist-ecology-lab

# Binomial test in Python

from scipy.stats import binom_test

# Six-sided die flipped 24 times and lands on three exactly 6 times
x = binom_test(x=6, n=24, p=1/6, alternative='greater')
print(x)

# Flip coin 30 times and it lands on heads exactly 19 times
y = binom_test(x=19, n=30, p=1/2, alternative='greater')