Fitting a model

A university has provided you with data that shows a relationship between the hours of study and the scores that the students get on a given test.

You have access to the data through the variables hours_of_study and scores. Use a linear model to learn from the data.

This exercise is part of the course

Foundations of Probability in Python

Exercise instructions

Import the linregress() function from scipy.stats.
Fit a linear model using the provided data in the hours_of_study and scores variables.
Print the parameters.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import the linregress() function
from scipy.stats import ____

# Get the model parameters
slope, intercept, r_value, p_value, std_err = ____(____, ____)

# Print the linear model parameters
print('slope:', ____)
print('intercept:', ____)

Edit and Run Code

This exercise is part of the course

Foundations of Probability in Python

IntermediateSkill Level

4.8+

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A coin flip is the classic example of a random experiment. The possible outcomes are heads or tails. This type of experiment, known as a Bernoulli or binomial trial, allows us to study problems with two possible outcomes, like “yes” or “no” and “vote” or “no vote.” This chapter introduces Bernoulli experiments, binomial distributions to model multiple Bernoulli trials, and probability simulations with the scipy library.

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Exercise 1: Normal distributions Exercise 2: Range of values Exercise 3: Plotting normal distributions Exercise 4: Within three standard deviations Exercise 5: Normal probabilities Exercise 6: Restaurant spending example Exercise 7: Smartphone battery example Exercise 8: Adults' heights example Exercise 9: Poisson distributions Exercise 10: ATM example Exercise 11: Highway accidents example Exercise 12: Generating and plotting Poisson distributions Exercise 13: Geometric distributions Exercise 14: Catching salmon example Exercise 15: Free throws example Exercise 16: Generating and plotting geometric distributions

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Exercise 1: From sample mean to population mean Exercise 2: Generating a sample Exercise 3: Calculating the sample mean Exercise 4: Plotting the sample mean Exercise 5: Adding random variables Exercise 6: Sample means Exercise 7: Sample means follow a normal distribution Exercise 8: Adding dice rolls Exercise 9: Linear regression Exercise 10: Fitting a model

Current Exercise

Exercise 11: Predicting test scores Exercise 12: Studying residuals Exercise 13: Logistic regression Exercise 14: Fitting a logistic model Exercise 15: Predicting if students will pass Exercise 16: Passing two tests Exercise 17: Wrapping up