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

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

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

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

Hint

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

Practice

1. What is the main purpose of fitting a model to data using scipy.optimize.curve_fit?

easy

A. To randomly change data values

B. To find the relationship between variables by estimating model parameters

C. To delete data points that don't fit

D. To visualize data without calculations

Solution

1. Step 1: Understand model fitting

   Fitting a model means finding parameters that best describe how data points relate.

2. Step 2: Role of curve_fit

   This function estimates parameters to match the model curve to the data points.

3. Final Answer:

   To find the relationship between variables by estimating model parameters -> Option B

4. Quick Check:

   Model fitting = find relationships [OK]

Hint: Model fitting finds best parameters showing data relationships [OK]

Common Mistakes:

• Thinking fitting deletes data
• Confusing fitting with visualization only
• Believing fitting changes data randomly

2. Which of the following is the correct way to import the curve_fit function from scipy?

easy

A. from scipy.optimize import curve_fit

B. import scipy.curve_fit

C. from scipy import curve_fit

D. import curve_fit from scipy.optimize

Solution

1. Step 1: Recall scipy module structure

   The curve_fit function is inside the optimize submodule of scipy.

2. Step 2: Correct import syntax

   Python syntax for importing a function from a submodule is from module.submodule import function.

3. Final Answer:

   from scipy.optimize import curve_fit -> Option A

4. Quick Check:

   Correct import syntax = from scipy.optimize import curve_fit [OK]

Hint: Use 'from scipy.optimize import curve_fit' to import correctly [OK]

Common Mistakes:

• Using wrong import syntax
• Trying to import directly from scipy
• Confusing import order

3. Given the code below, what will be the output of popt?

import numpy as np
from scipy.optimize import curve_fit

def linear(x, a, b):
    return a * x + b

xdata = np.array([1, 2, 3, 4, 5])
ydata = np.array([2.1, 4.1, 6.1, 8.1, 10.1])

popt, pcov = curve_fit(linear, xdata, ydata)
print(popt)

medium

A. [2.02, 0.06]

B. [1.0, 2.0]

C. [0.5, 1.0]

D. [2.0, 0.1]

Solution

1. Step 1: Understand the model and data

   The model is linear: y = a*x + b. The data roughly follows y = 2*x + 0.1.

2. Step 2: Use curve_fit to estimate parameters

   Running curve_fit fits parameters a and b to minimize error. The output popt contains these estimates.

3. Final Answer:

   [2.02, 0.06] -> Option A

4. Quick Check:

   Fitted slope ~2.02, intercept ~0.06 [OK]

Hint: Fitted slope near 2, intercept near 0.1 for this data [OK]

Common Mistakes:

• Confusing parameter order
• Expecting exact integers
• Ignoring small fitting errors

4. Identify the error in the code below that tries to fit a quadratic model to data:

import numpy as np
from scipy.optimize import curve_fit

def quadratic(x, a, b, c):
    return a * x**2 + b * x + c

xdata = np.array([1, 2, 3, 4])
ydata = np.array([3, 7, 13, 21])

popt, pcov = curve_fit(quadratic, xdata, ydata, p0=[1, 1])
print(popt)

medium

A. curve_fit is not imported correctly

B. Function quadratic is missing return statement

C. xdata and ydata have different lengths

D. Initial guess p0 has wrong length

Solution

1. Step 1: Check function parameters and initial guess

   The quadratic function has 3 parameters: a, b, c. The initial guess p0 must match this length.

2. Step 2: Identify mismatch in p0

   The code uses p0=[1, 1] which has length 2, causing an error.

3. Final Answer:

   Initial guess p0 has wrong length -> Option D

4. Quick Check:

   p0 length must match parameters [OK]

Hint: Ensure p0 length equals number of model parameters [OK]

Common Mistakes:

• Using wrong p0 length
• Ignoring error messages
• Assuming default p0 always works

5. You have noisy data points that roughly follow an exponential decay: y = a * exp(-b * x) + c. How can fitting this model with curve_fit help you understand the data better?

hard

A. By removing noise from the data points permanently

B. By converting the data into a linear form without parameters

C. By estimating parameters a, b, and c, you learn the decay rate and baseline

D. By predicting future data points without any error

Solution

1. Step 1: Understand the model parameters

   Parameter a controls initial value, b controls decay speed, and c is the baseline offset.

2. Step 2: Role of fitting with noisy data

   Fitting estimates these parameters despite noise, revealing the underlying decay behavior.

3. Final Answer:

   By estimating parameters a, b, and c, you learn the decay rate and baseline -> Option C

4. Quick Check:

   Fitting reveals model parameters despite noise [OK]

Hint: Fit model to find decay rate and baseline from noisy data [OK]

Common Mistakes:

• Thinking fitting removes noise permanently
• Assuming perfect future predictions
• Confusing model fitting with data transformation