Why fitting models to data reveals relationships in SciPy - See It in Action

Why fitting models to data reveals relationships

📖 Scenario: Imagine you are a scientist studying how temperature affects the growth of plants. You collected data on temperature and plant height. You want to find a simple rule that connects temperature to height so you can predict how tall plants will grow at different temperatures.

🎯 Goal: In this project, you will fit a straight line model to the temperature and plant height data. This will help you see the relationship between temperature and growth clearly.

📋 What You'll Learn

Create a dictionary called data with temperature and height lists

Create a variable called degree and set it to 1 for linear fitting

Use numpy.polyfit with degree to fit a line to the data

Print the slope and intercept of the fitted line

💡 Why This Matters

🌍 Real World

Scientists and analysts often collect data and fit models to understand how one thing affects another, like temperature affecting plant growth.

💼 Career

Data scientists and researchers use model fitting to find patterns and make predictions in many fields such as biology, economics, and engineering.

1

DATA SETUP: Create the data dictionary

Create a dictionary called data with two keys: 'temperature' and 'height'. Set data['temperature'] to the list [15, 18, 21, 24, 27, 30] and data['height'] to the list [10, 12, 15, 18, 20, 22].

SciPy

# Create the data dictionary with temperature and height lists
# Your code here

Need a hint?

Use a dictionary with keys 'temperature' and 'height'. Assign the exact lists given.

2

CONFIGURATION: Set the degree for linear fitting

Create a variable called degree and set it to 1 to indicate a linear model.

SciPy

data = {'temperature': [15, 18, 21, 24, 27, 30], 'height': [10, 12, 15, 18, 20, 22]}
# Set degree to 1 for linear fitting
# Your code here

Need a hint?

Set degree to 1 because we want a straight line fit.

3

CORE LOGIC: Fit a linear model to the data

Import numpy as np. Use np.polyfit with data['temperature'], data['height'], and degree to fit a line. Store the result in a variable called coefficients.

SciPy

import numpy as np

data = {'temperature': [15, 18, 21, 24, 27, 30], 'height': [10, 12, 15, 18, 20, 22]}
degree = 1
# Fit a linear model using np.polyfit and store in coefficients
# Your code here

Need a hint?

Use np.polyfit(x, y, degree) where x is temperature and y is height.

4

OUTPUT: Print the slope and intercept

Print the slope and intercept from coefficients using print(f"Slope: {coefficients[0]:.2f}, Intercept: {coefficients[1]:.2f}").

SciPy

import numpy as np

data = {'temperature': [15, 18, 21, 24, 27, 30], 'height': [10, 12, 15, 18, 20, 22]}
degree = 1
coefficients = np.polyfit(data['temperature'], data['height'], degree)
# Print slope and intercept
# Your code here

Need a hint?

Use an f-string to format the slope and intercept to two decimal places.