Gain insights into data manipulation, analysis, and visualization with R. Learn statistical concepts, create insightful visualizations, and empower decision-making to excel in data-centric roles.

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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

We use the Student's t-test to compare the means of two normal populations. It is useful when we have a small sample (typically less than 30), and the population standard deviation is unknown. 

The t-test takes the difference and variability of sample means into account and then makes inferences about the test issue.
There are two types of Student's t-tests: the independent samples t-test and the paired samples t-test.



# Independent samples t-test

The independent samples t-test compares the means of two independent groups. We assume that the groups are unrelated, meaning that there is no relationship between the observations. It calculates the p-value based on the difference between the sample means and standard deviations. We use the independent samples t-test to determine if the null value should be rejected. For example, we can decide if two distinct learning methods have distinguishable effects on comprehension. The formula of the independent t-test is as follows:

t = \frac{\bar{x}_1 - \bar{x}_2 - (\mu_1 - \mu_2)}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}}


The notation is as follows:

- $\bar{x}_1$: The mean of the first sample
- $\bar{x}_2$: The mean of the second sample
- $\mu_1$: The mean of the first population

- $\mu_2$: The mean of the second population

- $s_1$: The standard deviation of the first sample

- $s_2$: The standard deviation of the second sample

- $n_1$: The number of units in the first sample

- $n_2$: The number of units in the second sample


R allows us to make this calculation in one line of code.
We use the `t.test()` function for the t-test. It outputs the t-value, p-value, degrees of freedom, 95% confidence interval, and so on. The critical output is the p-value because we compare it to the alpha to determine if the difference is statistically significant.

On the other hand, the t-value is the measure used in hypothesis testing to assess the difference between a sample mean and a population mean. The t-value is used to determine the level of significance of a result, with larger t-values indicating stronger evidence against the null hypothesis.

Learn the different variations of Student's t-tests, such as independent samples t-test and paired samples t-test.

Getting Started

File Management

Data Structures

Data Cleaning

Statistical Analysis

Data Transformation

Data Visualization

Uber Data Analysis Using the R Language

Conclusion

Evaluation

Netflix Shows

Student's T-test

Independent samples t-test