Linear regression with np.polyfit() in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.polyfit() do in simple terms?

np.polyfit() finds the best straight line (or curve) that fits your data points. It helps you understand the relationship between two sets of numbers.

beginner

What are the main inputs to np.polyfit()?

You give it two lists or arrays: one for the x-values (input) and one for the y-values (output). You also tell it the degree of the polynomial (1 for a straight line).

beginner

What does the output of np.polyfit(x, y, 1) represent?

It returns two numbers: the slope (how steep the line is) and the intercept (where the line crosses the y-axis).

beginner

How can you use the result of np.polyfit() to predict new values?

Use the slope and intercept to make a formula: y = slope * x + intercept. Plug in new x-values to get predicted y-values.

beginner

Why is linear regression useful in real life?

It helps you find trends and make predictions, like estimating house prices based on size or predicting sales from advertising spend.

What degree should you use in np.polyfit() for a straight line?

A 1

B 2

C 0

D 3

Degree 1 means a straight line (linear). Higher degrees mean curves.

What does the first number returned by np.polyfit(x, y, 1) represent?

A Intercept

B Slope

C Correlation

D Residual

The first number is the slope, showing how steep the line is.

If you want to predict y for a new x, what formula do you use after np.polyfit()?

A y = slope * x + intercept

B y = intercept * x + slope

C y = x / slope + intercept

D y = slope + intercept * x

The correct formula is y = slope * x + intercept.

Which Python library provides polyfit()?

A pandas

B matplotlib

C scikit-learn

D numpy

polyfit() is a function in the numpy library.

What kind of relationship does linear regression model?

A Non-linear

B Random

C Linear

D Categorical

Linear regression models a straight-line (linear) relationship between variables.

Explain how to perform a simple linear regression using np.polyfit() with example data.

Think about giving x and y data, calling polyfit, then using the results.

Describe why linear regression is useful and how np.polyfit() helps in data analysis.

Consider real-life examples like predicting prices or sales.