Resampling, Selection and Splines

Resampling, Selection and Splines

Resampling, Selection and Splines This course is part of Statistical Learning for Data Science Specialization Taught in English Instructor: Osita Onyejekwe Enroll for Free Starts Mar 21 Financial aid available Included with • Learn more Course Gain insight into a topic and learn the fundamentals Intermediate level Recommended experience Close Recommended experience Intermediate level Completion

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Description

Resampling, Selection and Splines: A Comprehensive Guide

Resampling, selection, and splines are three essential concepts in statistics and data analysis that are widely used in various fields. These techniques are crucial for making accurate predictions, identifying patterns in data, and drawing meaningful conclusions. In this article, we will provide a comprehensive overview of resampling, selection, and splines, including their applications and importance in statistical analysis.

Resampling is a statistical method used to estimate the sampling distribution of a statistic by repeatedly sampling data from a population. This technique is particularly useful when the true distribution of the data is unknown or when the sample size is small. Common resampling methods include bootstrapping and cross-validation. Bootstrapping involves randomly sampling data points with replacement to estimate the uncertainty of a statistic

Resampling, Selection and Splines

This course is part of Statistical Learning for Data Science Specialization

Taught in English

Osita Onyejekwe

Instructor: Osita Onyejekwe

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

15 hours (approximately)
Flexible schedule
Learn at your own pace

What you’ll learn

  • Apply resampling methods in order to obtain additional information about fitted models.

  • Optimize fitting procedures to improve prediction accuracy and interpretability.

  • Identify the benefits and approach of non-linear models.

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