Introduction to Statistics
Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!
Statistics are all around us, from marketing to sales to healthcare. The ability to collect, analyze, and draw conclusions from data is not only extremely valuable, but it is also becoming commonplace to expect roles that are not traditionally analytical to understand the fundamental concepts of statistics. This course will equip you with the necessary skills to feel confident in working with analyzing data to draw insights. You’ll be introduced to common methods used for summarizing and describing data, learn how probability can be applied to commercial scenarios, and discover how experiments are conducted to understand relationships and patterns. You’ll work with real-world datasets including crime data in London, England, and sales data from an online retail company!
What You’ll Learn
Summary statistics gives you the tools you need to describe your data. 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.
More Distributions and the Central Limit Theorem
It’s time to explore more probability distributions. You’ll learn about the binomial distribution for visualizing the probability of binary outcomes, and one of the most important distributions in statistics, the normal distribution. You’ll see how distributions can be described by their shape, along with discovering the Poisson distribution and its role in calculating the probabilities of events occuring over time. You’ll also gain an understanding of the central limit theorem!
Probability and distributions
Probability underpins a large part of statistics, where it is used to calculate the chance of events occurring. You’ll work with real-world sales data and learn how data with different values can be interpreted as a probability distribution. You’ll find out about discrete and continuous probability distributions, including the discovery of the normal distribution and how it occurs frequently in natural events!
Correlation and Hypothesis Testing
In the final chapter, you’ll be introduced to hypothesis testing and how it can be used to accurately draw conclusions about a population. You’ll discover correlation and how it can be used to quantify a linear relationship between two variables. You’ll find out about experimental design techniques such as randomization and blinding. You’ll also learn about concepts used to minimize the risk of drawing the wrong conclusion about the results of hypothesis tests!
Introduction to Statistics
In this course, students will look at the properties behind the basic concepts of probability and statistics and focus on applications of statistical knowledge. Students will learn about how statistics and probability work together. The subject of statistics involves the study of methods for collecting, summarizing, and interpreting data. After finishing this course, students should be comfortable evaluating an authorâ€™s use of data and be able to extract information from articles and display that information effectively. Students will also be able to understand the basics of how to draw statistical conclusions. This course will begin with descriptive statistics and the foundation of statistics, move onto probability and random distributions, the latter of which enables statisticians to work with several aspects of random events and their applications. Finally, students will examine a number of ways to investigate the relationships between various characteristics of data.
Upon successful completion of this course, you will be able to:
define the meaning of descriptive statisticsÂ and statistical inference, describe the importance of statistics, and interpret examples of statistics in a professional context;distinguish between a population and a sample;explain the purpose of measures of location, variability, and skewness;apply simple principles of probability;compute probabilities related to both discrete and continuous random variables;identify and analyze sampling distributions for statistical inferences;identify and analyze confidence intervals for means and proportions;compare and analyze data sets using descriptive statistics, parameter estimation, hypothesis testing;explain how the central limit theorem applies in inference;calculate and interpret confidence intervals for one population average and one population proportion;differentiate between type I and type II errors;conduct and interpret hypothesis tests;identify and evaluate relationships between two variables using simple linear regression; anduse regression equations to make predictions.