Summary statistics gives you the tools you need to boil down massive datasets to reveal the highlights. In this chapter, you'll explore summary statistics including mean, median, and standard deviation, and learn how to accurately interpret them. You'll also develop your critical thinking skills, allowing you to choose the best summary statistics for your data.

What is statistics?

Descriptive and inferential statistics

Data type classification

Measures of center

Mean and median

Mean vs. median

Measures of spread

Variance and standard deviation

Quartiles, quantiles, and quintiles

Finding outliers using IQR

Summary Statistics

In this chapter, you'll learn how to generate random samples and measure chance using probability. You'll work with real-world sales data to calculate the probability of a salesperson being successful. Finally, you’ll use the binomial distribution to model events with binary outcomes.

What are the chances?

Options

With replacement

Without replacement

With or without replacement?

Calculating probabilities

Sampling deals

Discrete distributions

Creating a probability distribution

Identifying distributions

Expected value vs. sample mean

Continuous distributions

Discrete uniform

Continuous uniform

Other

Which distribution?

Data back-ups

Simulating wait times

The binomial distribution

Simulating sales deals

Calculating binomial probabilities

How many sales will be won?

Random Numbers and Probability

It’s time to explore one of the most important probability distributions in statistics, normal distribution. You’ll create histograms to plot normal distributions and gain an understanding of the central limit theorem, before expanding your knowledge of statistical functions by adding the Poisson, exponential, and t-distributions to your repertoire. 

The normal distribution

Distribution of Amir's sales

Probabilities from the normal distribution

Simulating sales under new market conditions

Which market is better?

The central limit theorem

Visualizing sampling distributions

The CLT in action

The mean of means

The Poisson distribution

Identifying lambda

Tracking lead responses

More probability distributions

Too many distributions

Modeling time between leads

The t-distribution

More Distributions and the Central Limit Theorem

In this chapter, you'll learn how to quantify the strength of a linear relationship between two variables, and explore how confounding variables can affect the relationship between two other variables. You'll also see how a study’s design can influence its results, change how the data should be analyzed, and potentially affect the reliability of your conclusions.

Correlation

Guess the correlation

Relationships between variables

Correlation caveats

What can't correlation measure?

Transforming variables

Does sugar improve happiness?

Confounders

Design of experiments

Study types

Longitudinal vs. cross-sectional studies

Congratulations!

Correlation and Experimental Design

Food Consumption & Carbon Footprint

Amir's Sales

World Happiness Report

Statistics is the study of how to collect, analyze, and draw conclusions from data. It’s a hugely valuable tool that you can use to bring the future into focus and infer the answer to tons of questions. For example, what is the likelihood of someone purchasing your product, how many calls will your support team receive, and how many jeans sizes should you manufacture to fit 95% of the population? In this course, you'll use sales data to discover how to answer questions like these as you grow your statistical skills and learn how to calculate averages, use scatterplots to show the relationship between numeric values, and calculate correlation. You'll also tackle probability, the backbone of statistical reasoning, and learn how to conduct a well-designed study to draw your own conclusions from data.

Data Manipulation with dplyr 

Intermediate R

Learn how to work with variables, plotting, and standard deviation in R with this free statistics course. It covers histograms, distributions and more.

Introduction to Statistics in R

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Scienziato dei dati associato in R

Analista dati  in R

Statistico in R

Fondamenti di statistica in R

Content Developer, DataCamp

Measuring chance

Assigning salespeople

Sampling from a data frame

Setting a random seed

A second meeting

Sampling twice in R

Sampling with replacement

Sampling with replacement in R

Independent events

Dependent events

Let's practice!

https://s3.amazonaws.com/assets.datacamp.com/production/course_24852/subtitles/course_24852_e3779fcd06a54abb133d03a6cc0d4c5a.vtt

Random Numbers and Probability - What are the chances?

What are the chances?

Create Your Free Account