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SNUx: Introductory Statistics : Analyzing Data Using Graphs and Statistics

This course teaches basic statistical concepts andexplores many compelling applications of statistical methodsusing real-life applications of Statistics.

Introductory Statistics : Analyzing Data Using Graphs and Statistics
5 weeks
1–2 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

6,369 already enrolled! After a course session ends, it will be archivedOpens in a new tab.
Starts Nov 22
Ends Dec 31

About this course

Skip About this course

Why do we study statistics? The field of statistics provides professionals and scientists withconceptual foundations and useful techniques for evaluating ideas, testing theories, and - ultimately -uncovering the truth in any situation.

This course will familiarize you with data and basic statistical concepts, enabling you to analyze data using graphs and statistics. We'll start with types of data, controlled experiments,and observational study. You'll learn touse ahistogram, a representation of the distribution of numerical data, to easily arrange data.

You will learn about basic concepts of statistics, such as average and standard deviation. Methods of using the normal approximation to solve a problem will be covered in this course. Inaddition, we'll discussthe correlation coefficient and the regression method in order to represent the relationship between two variables.

At a glance

  • Language: 한국어
  • Video Transcripts: English, 한국어
  • Associated skills:Data Analysis, Controlled Experiments, Standard Deviation

What you'll learn

Skip What you'll learn
  • Statistics and Data
  • The Histogram
  • The Average and the Standard Deviation
  • The Normal Approximation
  • Correlation
  • Regression
  • The Error for Regression
  • Regression Line

[Week 1]

  • Statistics and Data
  • The Histogram

[Week 2]

  • The Average and the Standard Deviation
  • The Normal Approximation

[Week 3]

  • Correlation
  • Regression

[Week 4]

  • The Error for Regression
  • Regression Line

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