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R_lang

This course delves into data manipulation, analysis, and visualization with R. It demonstrates the power of R as open-source statistical software, unlocking valuable insights for decision-making across diverse fields.

You'll begin by mastering the basics of R programming, fundamental data structures in R, and file management. You will learn fundamental statistical concepts such as hypothesis testing and regression analysis. You will also learn to leverage well-structured data for creating insightful visualizations using R's robust graphics libraries.

After completing this course, you'll confidently approach data-driven challenges. With proficiency in R, you can efficiently manipulate and analyze data, positioning yourself as a sought-after asset in data-centric roles. Your ability to interpret statistical results and create compelling data visualizations will empower improved decision-making, paving the way for new opportunities for career growth and success across various industries.

Introduction to Data Analysis and Visualization with R

## Understanding the concept
**Confidence interval** is a critical concept in inferential statistics.
It estimates the range of plausible values for a population parameter based on a data sample. Confidence intervals provide a way to quantify the uncertainty associated with point estimates. We use standard deviation to define confidence intervals. 

Suppose we gather the math test scores of high school students in a country, and we wish to determine the 95% confidence interval of the population mean. We collect a random sample of 200 high school students from across the country and use their scores in the calculation.

The calculation result means that we can be 95% confident that the average score on the math exam for all high school students in the country falls between the lower and upper range of the confidence interval.


# Understanding the concept
**Confidence interval** is a critical concept in inferential statistics.
It estimates the range of plausible values for a population parameter based on a data sample. Confidence intervals provide a way to quantify the uncertainty associated with point estimates. We use standard deviation to define confidence intervals. 

Suppose we gather the math test scores of high school students in a country, and we wish to determine the 95% confidence interval of the population mean. We collect a random sample of 200 high school students from across the country and use their scores in the calculation.

The calculation result means that we can be 95% confident that the average score on the math exam for all high school students in the country falls between the lower and upper range of the confidence interval.


Learn about confidence interval, the central limit theorem, and their relationships with standard deviation. 

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Conclusion

Evaluation

Netflix Shows

Confidence Interval

Understanding the concept