Statistics 101

Unlock the Power of Numbers: Dive into Statistics 101!

Statistics 101

Unlock the Power of Numbers: Dive into Statistics 101!

Embark on a journey to demystify the world around you with our transformative Statistics 101 course. Imagine possessing the power to decode complex data, revealing truths hidden within numbers. This isn't just about calculations; it's about sharpening your perception, making informed decisions, and gaining a dynamic edge in any field--be it business, science, or social research. Experience learning as you never have before, through engaging insights and real-world applications that will forever change how you view data. Embrace this opportunity to become the master of your narrative, armed with the skills to challenge misleading claims and empower your vision. Enroll today and transform your analytical capabilities, ensuring that every statistic speaks not just to you, but for you.

Self Paced • Online Class • Accredited CEUs

$120

4.5 ★ ★ ★ ★ ★

"Extraordinarily Helpful"

4,825 Students have taken this course

Add to Cart

OVERVIEW

CURRICULUM

Statistics 101

Name: Statistics 101
Price: 95.00 USD

CRN: 9770453

Lesson 1. Unveiling the World of Statistics: A Gateway to Understanding and Application

Statistics is a versatile tool that aids in compiling, interpreting, and applying numerical data across multiple disciplines. This overview establishes the groundwork by introducing key statistical concepts and the importance of differentiating between population and sample data.

Lesson 2. Data Organization and Visualization: Building Tables and Graphs

Organizing data for statistical analysis is crucial for efficient calculations and inference; this lesson covers constructing tables and understanding graphical data presentations. Emphasizing terms like frequency, cumulative frequency, and relative frequency, it guides their application through examples.

Lesson 3. Measuring the Center: Understanding Mean, Median, and Mode

Measures of central tendency, including the mean, median, and mode, describe the 'center' of a dataset, each uniquely suited to different data distributions. This lesson emphasizes that while the mean is popular, skewed distributions may require the median or mode for accurate representations.

Lesson 4. Choosing Your Measures: Understanding Central Tendency

This lesson teaches how data set nuances can make selecting the right measure of central tendency a non-trivial task. By illustrating with examples, it discusses the balance between the mean, median, and mode, and the importance of context to avoid misleading interpretations.

Lesson 5. Characterizing the Spread: A Guide to Variance and Standard Deviation

This lesson on measures of dispersion explains variance and standard deviation as tools to gauge data spread around a mean. It distinguishes between broad population analyses and specific sample estimates, with adjustments for freedom degrees to improve accuracy.

Lesson 6. The Role of Skewness in Statistical Analysis

This lesson delves into skewness, a statistical measure that explains the asymmetry of a data set, enriching our understanding beyond just central tendency and dispersion. By quantifying skewness, we can determine both the degree and direction of data asymmetry relative to the mean.

Lesson 7. Understand Percentiles and Quartiles

The lesson explores statistical measures like percentiles and quartiles, which help describe data set positions. Understanding these concepts enhances data interpretation beyond just central tendency and dispersion.

Lesson 8. Understanding Probability: The Backbone of Statistical Analysis

The connection between chance events in experiments and statistics highlights the importance of terms like outcome, event, sample space, and random variable in probability theory. Using statistical measures like mean and standard deviation helps in comprehending and describing these random variations.

Lesson 9. Counting Outcomes for Probability

This lesson delves into solving probability problems by counting the outcomes of random experiments, utilizing key concepts such as permutations and combinations. By understanding the influence of replacement and ordering, one can effectively calculate probabilities by determining relative frequencies.

Lesson 10. Fundamentals of Discrete and Continuous Distributions

This lesson blends statistics and probability to explore statistical distributions, highlighting the distinction between discrete and continuous types, and showing how to calculate mean and standard deviation. With practice problems, it demonstrates practical applications like rolling a die or estimating measurement expectations.

Lesson 11. Normal Distribution and Z-Scores

This lesson deciphers the normal distribution and its statistical applications, emphasizing how to use Z-scores for standardizing data to estimate probabilities with ease. Probabilities are often computed using tables, which provide a user-friendly method to navigate complex probability calculations.

Lesson 12. Exploring Bivariate Data Distributions: A Foundation for Advanced Statistical Concepts

By transitioning from univariate to bivariate data analyses, the lesson underscores the importance of scatterplots and marginal distributions, equipping learners to evaluate statistical dependencies. It serves as groundwork for exploring how random variable interactions inform advanced statistical methods like regression.

Lesson 13. Introduction to Least-Squares Regression

This lesson delves into least-squares linear regression, discussing its formulation and the correlation measure for bivariate data. It offers a robust algebraic approach to fitting linear trends to datasets while addressing the 'goodness of fit.'

Lesson 14. Crunch Time: Hypothesis Testing in Practice

Through understanding hypothesis testing, students will grasp concepts like null and alternative hypotheses, significance levels, and test statistics. The lesson applies these principles to real-world scenarios, enhancing analytical skills and decision-making capabilities.

Lesson 15. Significance Levels and Error Analysis in Hypothesis Testing

By focusing on error types in hypothesis testing, the lesson underscores the critical nature of choosing an appropriate significance level, influencing the probability of errors like Type I or Type II. Practice questions enable students to apply these concepts and strengthen their analytical skills.

In This Course

35 Hours average completion time
3.5 CEUs
15 Lessons
31 Exams & Assignments
77 Discussions
16 Videos
16 Reference Files
6 Articles
Mobile Friendly
Last Updated February 2024

Description

Statistics are used in a variety of contexts ranging from scientific experiments to political advertisements and beyond. Because statistics can be used to mislead, an understanding of the topic can be helpful for more than just the mathematical skills that it imparts: knowledge of statistics theory can also be a strong defense against attempts by the unscrupulous to mislead others.

This statistics course introduces the basic concepts of statistical analysis, with a focus on both univariate (single-variable) and bivariate (two-variable) data. The course starts with an introduction to statistics terms and then moves on to organization and display of data. Analysis of univariate data by way of measures of central tendency (such as the mean or average), dispersion (such as the variance), and asymmetry ("skewness") is presented next, followed by an introduction to probability theory.

The relationship of probability to statistics is also discussed, providing students with the tools they need to understand how "chance" plays a role in statistical analysis. Statistical distributions, with a focus on the normal distribution and its uses, are also considered, along with a discussion of bivariate data and linear (least-squares) regression. Finally, the course culminates with a low-level introduction to hypothesis testing. Although this last topic could be a course of its own, the student is provided with enough theory and sufficient practice to conduct analyses of simple statistical hypotheses.

The course assumes a minimal understanding of and proficiency in algebra, but many of the concepts can be understood, and even many of the calculations performed, without an extensive mathematical background. This course is designed for anyone who wants a little more than just a cursory overview of statistics, but who doesn't want to get bogged down in the mathematical theory that underlies it.

Statistics is a subject that has earned a certain amount of notoriety because of its misuse in various contexts. Nevertheless, statistics is a tool that, if used properly, can be of tremendous help in math, science, engineering, history, politics, and numerous other fields. As you study this subject, always keep in mind that statistics is more than just math: it is not simply manipulation of numbers through addition, subtraction, multiplication, division, and other mathematical operations. Statistics also involves language and units: when a statistician (or layman) provides a statistic, it involves a number and a label of some sort. For instance, the number 5.3 is not in and of itself a statistical value; "an average age of 5.3 years," however, is a statistical value. This linguistic aspect of statistics sometimes allows a certain amount of ambiguity that can be misleading. By studying statistics, you will equip yourself to identify and understand both uses and abuses of this tool.

What is Statistics?

Statistics is used for quantifying sets of data such as attributes of a group of people and measurements taken in a laboratory. Consider, for instance, the population of a particular country. The people who reside in that country have varying heights: some are short, some are tall, some are in between. If we wanted to compare the height of this population with that of some other population in a convenient manner, we would not want to compare individual people. Such a task would be burdensome (the number of people in a country might be in the millions or billions) and would not necessarily be particularly helpful as a means of comparing populations as a whole. Instead, we can use an average or median height as the basis for our comparison. These statistical values are single numbers that quantify the data (the heights of a country's population) and that provide a convenient way to express and compare certain characteristics of those data. Part of the goal of this course is to teach you how to select and use statistical tools like averages and medians, as well as a host of others, in assessing and comparing data.

Simply defined, statistics (sometimes colloquially termed "stats") is the study of collecting, analyzing, interpreting, and representing of sets of numerical data. Thus, virtually any field of study that uses numbers can, at least occasionally, involve statistics. Statistics, because it makes extensive use of numbers, is math-intensive, and a decent grasp of basic arithmetic and algebra is required to study this field.

Skills You'll Develop

Analyzing skewness in data
Utilizing percentiles and quartiles
Data visualization techniques
Probability concepts application
Statistical data interpretation
Linear regression formulation skills
Central tendency measures understanding
Bivariate data analysis
Conducting hypothesis testing
Dispersion measures comprehension

More About This Course

Explore Probability Concepts: Grasp outcome, event, and random variables.
Practice Hypothesis Testing: Conduct real-world statistical hypothesis analyses.
Gain Statistical Literacy: Understand and interpret statistical data confidently.
Understand Data Distributions: Distinguish between discrete and continuous types.
Defend Against Misuse: Identify and critique misleading statistics.
Learn Regression Basics: Analyze data with least-squares linear regression.
Strengthen Decision Making: Utilize statistics in diverse fields and contexts.
Master Data Visualization: Create clear, informative tables and graphs.
Enhance Analytical Skills: Employ mean, median, and mode effectively.

What You'll Achieve

Recognize and define key statistical terms such as univariate data, bivariate data, and population by the end of the lesson
Explain the importance of statistics in various fields and identify examples of its misuse for improved decision-making skills
Recognize and construct frequency tables, relative frequency tables, and cumulative frequency tables to organize discrete data sets.
Demonstrate the ability to discern between discrete and continuous data sets and select appropriate graphical methods for their representation.
Define and calculate the mean, median, and mode for a given data set.
Demonstrate the ability to select the appropriate measure of central tendency for non-symmetrical data distributions.
Demonstrate the ability to select the appropriate measure of central tendency based on data distribution and context.
Identify potential biases and ambiguity when presenting statistical data using different measures of central tendency.
Identify the differences between population and sample variance calculations, emphasizing the use of degrees of freedom for sample data.
Calculate the standard deviation of a given data set, distinguishing between population and sample data to apply appropriate formulas.
Recognize and define skewness as a measure of asymmetry in a data distribution, identifying its role in quantifying data skew in either direction.
Calculate the skewness of a given data set, determining its directionality and impact on the distribution compared to measures of central tendency and dispersion.
Define and calculate the range of a given data set to effectively measure data dispersion.