Statistics and Data Analysis with Excel, Part 2 from Coursera

This course is meant to be a direct continuation of "Statistics and Data Analysis with Excel, Part 1." Therefore, it is not recommended to take Part 2 unless you've also taken Part 1. Building on the topics learned in Part 1 of the course (probability, probability mass and density functions, the normal and standard normal distributions), this course dives into a more applied side of statistics. Topics in Part 2 include sampling distributions; one-sample hypothesis tests on the mean, variance, and binomial proportion; two-sample hypothesis tests (comparison of means, variances, and binomial proportions of samples drawn from two populations); simple (straight-line) regression; multilinear regression; and analysis of variance (ANOVA).

Statistical techniques are taught with the help of Microsoft Excel, which is an intuitive software package that has many built-in functions and tools for statistical analysis. This course is the second course out of three that comprise the specialization "Statistics and Applied Data Analysis." Course 3 will focus on statistical analysis in the statistical software package RStudio.

What's inside

Syllabus

Introduction and Review

Week 1 of the course is an introduction to Part 2 of "Statistics and Data Analysis with Excel." You will have several short, orientation-type reading assignments and you will have the opportunity to review some important concepts from Part 1 of the course. Finally, you'll be introduced to some of the main concepts and goals of the course.

Sampling Distributions and the Central Limit Theorem

In Week 2 of the course, you will learn all about sampling distributions and how they are different from population distributions, which you learned about in Part 1 of the course. You will also learn about the "variance known" and "variance unknown" cases and the differences between them. You'll learn all about the T distribution and how to create confidence intervals on the population mean when variance is known and unknown. Finally, you will learn about the chi-squared distribution and how to create confidence intervals on the population variance.

One-Sample Hypothesis Tests

Week 3 will introduce you to hypothesis testing. You will perform hypothesis tests on single-sample parameters (mean and variance). You will then learn about Type I and Type II errors, how to calculate beta and power, and how to determine sample size for a specified power of the test. Finally, you will learn how to perform hypothesis tests on a binomial proportion.

Two-Sample Hypothesis Tests

Week 4 is all about hypothesis tests related to comparision of means, variances, and binomial proportions of two populations. You will also learn how to perform paired T-tests and you will learn how to use the F distribution.

Linear Regression

Week 5 introduces you to linear regression models. You will learn how to create simple linear regression models, perform hypothesis tests on the slope and intercept, and calculate the coefficient of determination and adjusted R-squared value. You will also learn how to use Excel's Regression tool to create linear regression models.

Multilinear Regression

Building off of concepts you learned in Week 5 of the course, Week 6 will introduce you to multiple linear regression models. You will learn how to perform hypothesis tests on model parameters and how to create confidence and prediction intervals. Finally, you will be introduced to nonlinear regression (logistic regression).

ANOVA

In Week 7, you will learn the basics of one-way and two-way analysis of variance (ANOVA). You will learn how to do this "by hand" and also using a built-in tool in Excel.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops intermediate-level skills in statistics and data analysis

Builds a strong foundation for more advanced data analysis and statistical modeling

Taught by Charlie Nuttelman, a highly regarded instructor in statistics and data analysis

Covers a wide range of topics, providing a comprehensive overview of key concepts in statistics and data analysis

Utilizes Microsoft Excel as a tool for statistical analysis, making it accessible to learners with varying backgrounds in programming

Requires strong foundational knowledge in probability, probability mass and density functions, the normal and standard normal distributions, as covered in Part 1 of the course, so it may not be suitable for complete beginners

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Statistics and Data Analysis with Excel, Part 2 with these activities:

Organize and review course materials

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Build a strong foundation by organizing and reviewing course materials before the course begins.

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Gather all relevant course materials.
Organize the materials in a logical order.
Review the materials periodically.

Review probability and distributions

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Solidify your understanding of probability and distributions, which are essential foundations for the course.

Browse courses on Probability

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Review the basics of probability theory.
Study the different types of probability distributions.
Practice solving problems involving probability distributions.

Review probability and statistics concepts

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Doing a quick review of probability and statistics concepts will help you refresh your memory and get you up to speed on the material covered in this course.

Browse courses on Probability

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Review your notes from a previous probability and statistics course.
Go through some online tutorials on probability and statistics.
Take a practice quiz on probability and statistics.

Seven other activities

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Show all ten activities

Practice solving problems on sampling distributions

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Practicing solving problems on sampling distributions will help you understand the concepts and how to apply them to real-world problems.

Browse courses on Sampling Distributions

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Find some practice problems on sampling distributions online.
Solve the problems and check your answers.
Repeat steps 1 and 2 until you feel confident in your ability to solve problems on sampling distributions.

Attend a workshop on data analysis with Excel

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Gain hands-on experience with Excel's data analysis features through a workshop.

Browse courses on Data Analysis

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Find a workshop on data analysis with Excel.
Register for the workshop.
Attend the workshop and participate actively.

Solve practice problems on hypothesis testing

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Reinforce your understanding of hypothesis testing techniques through practice.

Browse courses on Hypothesis Testing

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Find practice problems on hypothesis testing.
Solve the problems and check your answers.

Practice ANOVA calculations using real-world datasets

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Enhance your understanding of ANOVA by applying it to real data.

Browse courses on ANOVA

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Find real-world datasets that are suitable for ANOVA analysis.
Perform ANOVA calculations on the datasets.
Interpret the results of the ANOVA analysis.

Create a summary of linear regression

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Enhance your comprehension of linear regression concepts by creating a concise summary.

Browse courses on Linear Regression

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Review the concepts of linear regression.
Summarize the key points in your own words.

Build a data visualization tool in Excel

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Develop your skills in creating data visualizations by building a tool in Excel.

Browse courses on Data Visualization

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Determine the purpose and audience for your data visualization tool.
Gather the data you will use to create the tool.
Design and build the tool using Excel's charting and graphing features.

Volunteer as a data analyst

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Gain practical experience in data analysis by volunteering.

Browse courses on Data Analysis

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Find a volunteer opportunity where you can assist with data analysis tasks.
Contact the organization and inquire about volunteer opportunities.
Attend training provided by the organization.
Perform data analysis tasks under the supervision of experienced professionals.

Career center

Learners who complete Statistics and Data Analysis with Excel, Part 2 will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts use their skills in data mining, data visualization, and statistical analysis to turn raw data into useful insights. They work closely with business stakeholders to identify the most important questions to ask of the data and then develop and implement solutions to help answer those questions. This course can help you build a foundation in statistical analysis, which is essential for success in data analytics. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists are responsible for developing and implementing data-driven solutions to business problems. They use their skills in machine learning, artificial intelligence, and statistical analysis to build models that can predict future outcomes and help businesses make better decisions. This course can help you build a foundation in statistical analysis, which is essential for success in data science. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

See salaries and explore the career path for Data Scientist

Statistician

Statisticians use their skills in probability, statistical theory, and data analysis to solve problems in a variety of fields, including medicine, finance, and engineering. They design and conduct studies, collect and analyze data, and interpret results. This course can help you build a foundation in statistical analysis, which is essential for success as a statistician. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

See salaries and explore the career path for Statistician

Business Analyst

Business Analysts use their skills in data analysis and business process improvement to help organizations improve their performance. They identify problems and opportunities, develop solutions, and implement changes. This course can help you build a foundation in statistical analysis, which is essential for success as a business analyst. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

See salaries and explore the career path for Business Analyst

Financial Analyst

Financial Analysts use their skills in financial modeling and data analysis to evaluate investments and make recommendations to clients. They use financial data to develop models that can predict future performance and help clients make informed decisions. This course can help you build a foundation in statistical analysis, which is essential for success as a financial analyst. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use their skills in mathematics, statistics, and finance to assess risk and develop insurance products. They use data analysis to evaluate the likelihood of events and then develop models to calculate premiums and benefits. This course can help you build a foundation in statistical analysis, which is essential for success as an actuary. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

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Biostatistician

Biostatisticians use their skills in statistics and biology to design and analyze studies on human health. They use data analysis to identify risk factors for disease, evaluate the effectiveness of treatments, and develop new methods for preventing and treating disease. This course can help you build a foundation in statistical analysis, which is essential for success as a biostatistician. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

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Market Researcher

Market Researchers use their skills in data analysis and market research to understand consumer behavior and trends. They conduct surveys, focus groups, and other research studies to collect data on consumer preferences, attitudes, and behaviors. This course can help you build a foundation in statistical analysis, which is essential for success as a market researcher. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

See salaries and explore the career path for Market Researcher

Economist

Economists use their skills in economic theory and data analysis to study the economy and make recommendations on economic policy. They use data analysis to identify trends and patterns in the economy and then develop models to predict future outcomes. This course can help you build a foundation in statistical analysis, which is essential for success as an economist. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

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Quantitative Analyst

Quantitative Analysts use their skills in mathematics, statistics, and financial modeling to develop and implement trading strategies. They use data analysis to identify trends and patterns in the financial markets and then develop models to predict future prices. This course can help you build a foundation in statistical analysis, which is essential for success as a quantitative analyst. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

See salaries and explore the career path for Quantitative Analyst

Data Engineer

Data Engineers are responsible for designing, building, and maintaining the data infrastructure that supports data analysis. They work with data scientists and other data professionals to ensure that data is available, accurate, and secure. This course can help you build a foundation in statistical analysis, which is essential for success as a data engineer. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

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Public Health Analyst

Public Health Analysts use their skills in statistics and epidemiology to investigate and prevent public health problems. They use data analysis to identify risk factors for disease, evaluate the effectiveness of public health interventions, and develop new strategies to promote health. This course may be useful for public health analysts who want to learn more about statistical analysis. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

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Operations Research Analyst

Operations Research Analysts use their skills in mathematics, statistics, and computer science to solve problems in a variety of industries, including manufacturing, healthcare, and transportation. They use data analysis to identify inefficiencies and develop solutions to improve performance. This course may be useful for operations research analysts who want to learn more about statistical analysis. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They use their skills in programming, mathematics, and statistics to create software that meets the needs of users. This course may be useful for software engineers who want to learn more about statistical analysis. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

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Risk Analyst

Risk Analysts use their skills in statistics and finance to assess risk and develop strategies to mitigate risk. They use data analysis to identify potential risks and develop models to calculate the likelihood and impact of those risks. This course may be useful for risk analysts who want to learn more about statistical analysis. You will learn how to use Microsoft Excel to perform a variety of statistical tasks, including hypothesis testing, regression analysis, and analysis of variance.

See salaries and explore the career path for Risk Analyst