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Tricia Bagley
Data has the power to inform decision-making to move us toward our personal, professional, and organizational goals. However, there is a risk involved in data-driven decision making when we are not confident in the interpretation of analytical test findings....
Read more
Data has the power to inform decision-making to move us toward our personal, professional, and organizational goals. However, there is a risk involved in data-driven decision making when we are not confident in the interpretation of analytical test findings....
Read more
Data has the power to inform decision-making to move us toward our personal, professional, and organizational goals. However, there is a risk involved in data-driven decision making when we are not confident in the interpretation of analytical test findings. Gaining that confidence in the data we use for decision-making requires us to be able to recognize Type 1 and Type 2 analysis errors. In this project you will gain hands-on experience with the principles of developing a hypothesis, conducting a t-test, interpreting test results, and recognizing Type 1 and Type 2 errors. To do this you will work in the free-to-use spreadsheet software Google Sheets.
By the end of this project, you will be able to recognize Type 1 and Type 2 errors to improve confidence using data for decision-making.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/1reoxe/type
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Teaches tools and skills that are relevant to industry's decision making process
Provides a strong foundation for learners new to interpreting analytical test findings
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Reviews summary
Simple and easy to learn data analysis
With a perfect 5-star rating from 1 review, learners who took the course titled "Type 1 and Type 2 Error Analysis in Google Sheets" say that this course provides a simple and easy to learn approach to data analysis.
Course presents simple approach to data analysis concepts.
"Very informative... keeping it simple and easy to learn."
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 Type 1 and Type 2 Error Analysis in Google Sheets with these
activities:
Review descriptive statistics
Show steps
Sharpen your understanding of descriptive statistics, which will be essential for understanding analytical test findings.
Browse courses on
Descriptive Statistics
Show steps
-
Review your notes and textbook chapters on descriptive statistics.
-
Take practice quizzes or exams to test your understanding.
Conduct t-tests with Google Sheets
Show steps
Gain practical experience with conducting t-tests using Google Sheets, which will enhance your confidence in interpreting test results.
Browse courses on
t-Test
Show steps
-
Create a dataset in Google Sheets that includes variables relevant to your research question.
-
Use the TTEST function to perform a t-test on your dataset.
-
Interpret the test results and draw conclusions based on the p-value.
Write a report on your t-test findings
Show steps
Contribute to your own learning by documenting your t-test findings in a report, which will solidify your understanding and strengthen your ability to communicate data-driven insights.
Show steps
-
Summarize the research question and hypothesis you tested.
-
Describe the dataset you used and the t-test procedure you followed.
-
Present the test results, including the p-value and your interpretation.
-
Discuss the implications and limitations of your findings.
One other activity
Expand to see all activities and additional details
Show all four activities
Design a study to test a hypothesis
Show steps
Solidify your understanding of hypothesis testing by designing a study that involves collecting and analyzing data to test a hypothesis, which will enhance your ability to make data-driven decisions.
Browse courses on
Hypothesis Testing
Show steps
-
Identify a research question and formulate a hypothesis.
-
Determine the appropriate data collection method and sample size.
-
Collect and clean the data.
-
Analyze the data and interpret the results.
-
Draw conclusions and discuss the implications of your findings.
Review descriptive statistics
Show steps
Sharpen your understanding of descriptive statistics, which will be essential for understanding analytical test findings.
Browse courses on
Descriptive Statistics
Show steps
Show steps
Show steps
- Review your notes and textbook chapters on descriptive statistics.
- Take practice quizzes or exams to test your understanding.
Conduct t-tests with Google Sheets
Show steps
Gain practical experience with conducting t-tests using Google Sheets, which will enhance your confidence in interpreting test results.
Browse courses on
t-Test
Show steps
Show steps
Show steps
- Create a dataset in Google Sheets that includes variables relevant to your research question.
- Use the TTEST function to perform a t-test on your dataset.
- Interpret the test results and draw conclusions based on the p-value.
Write a report on your t-test findings
Show steps
Contribute to your own learning by documenting your t-test findings in a report, which will solidify your understanding and strengthen your ability to communicate data-driven insights.
Show steps
Show steps
Show steps
- Summarize the research question and hypothesis you tested.
- Describe the dataset you used and the t-test procedure you followed.
- Present the test results, including the p-value and your interpretation.
- Discuss the implications and limitations of your findings.
One other activity
Expand to see all activities and additional details
Show all four activities
Design a study to test a hypothesis
Show steps
Solidify your understanding of hypothesis testing by designing a study that involves collecting and analyzing data to test a hypothesis, which will enhance your ability to make data-driven decisions.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Identify a research question and formulate a hypothesis.
- Determine the appropriate data collection method and sample size.
- Collect and clean the data.
- Analyze the data and interpret the results.
- Draw conclusions and discuss the implications of your findings.
Career center
Learners who complete Type 1 and Type 2 Error Analysis in Google Sheets will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, analyze, interpret, and present data in order to help businesses make informed decisions. They use statistical techniques to identify trends and patterns in data, and they communicate their findings in clear and concise reports. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Data Analysts develop the skills they need to accurately analyze data and draw meaningful conclusions from their findings.
Financial Analyst
Financial Analyst
Financial Analysts use data to evaluate the financial health of companies and make recommendations on investments. They use statistical techniques to analyze financial data and identify trends and patterns. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Financial Analysts develop the skills they need to accurately analyze financial data and make sound investment decisions.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about consumers and markets in order to help businesses understand their customers and make informed decisions about product development and marketing campaigns. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Market Researchers develop the skills they need to accurately analyze market data and draw meaningful conclusions from their findings.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to analyze and improve the efficiency of business operations. They use statistical techniques to identify inefficiencies and develop solutions to improve processes. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Operations Research Analysts develop the skills they need to accurately analyze data and make recommendations for process improvement.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data in order to help businesses and organizations make informed decisions. They use statistical techniques to identify trends and patterns in data, and they communicate their findings in clear and concise reports. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Statisticians develop the skills they need to accurately analyze data and draw meaningful conclusions from their findings.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems and develop new products and services. They use statistical techniques to analyze data and identify trends and patterns. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Data Scientists develop the skills they need to accurately analyze data and draw meaningful conclusions from their findings.
Business Analyst
Business Analyst
Business Analysts use data to analyze and improve the performance of businesses. They use statistical techniques to identify trends and patterns in data, and they make recommendations for improvement. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Business Analysts develop the skills they need to accurately analyze data and make sound recommendations for business improvement.
Management Consultant
Management Consultant
Management Consultants use data to help businesses solve problems and improve performance. They use statistical techniques to analyze data and identify trends and patterns. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Management Consultants develop the skills they need to accurately analyze data and make sound recommendations for business improvement.
Product Manager
Product Manager
Product Managers use data to develop and market new products. They use statistical techniques to analyze market data and identify customer needs. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Product Managers develop the skills they need to accurately analyze market data and make sound decisions about product development and marketing.
Marketing Manager
Marketing Manager
Marketing Managers use data to develop and implement marketing campaigns. They use statistical techniques to analyze market data and identify customer needs. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Marketing Managers develop the skills they need to accurately analyze market data and make sound decisions about marketing campaigns.
Sales Manager
Sales Manager
Sales Managers use data to analyze sales trends and identify growth opportunities. They use statistical techniques to analyze sales data and make predictions about future sales. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Sales Managers develop the skills they need to accurately analyze sales data and make sound decisions about sales strategies.
Customer Success Manager
Customer Success Manager
Customer Success Managers use data to analyze customer behavior and identify opportunities for improvement. They use statistical techniques to analyze customer data and make predictions about customer behavior. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Customer Success Managers develop the skills they need to accurately analyze customer data and make sound decisions about customer service and support strategies.
Operations Manager
Operations Manager
Operations Managers use data to analyze and improve business operations. They use statistical techniques to identify inefficiencies and develop solutions to improve processes. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Operations Managers develop the skills they need to accurately analyze data and make sound decisions about operational improvements.
Project Manager
Project Manager
Project Managers use data to plan and track projects. They use statistical techniques to identify risks and develop contingencies. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Project Managers develop the skills they need to accurately analyze data and make sound decisions about project planning and execution.
Human Resources Manager
Human Resources Manager
Human Resources Managers use data to analyze employee performance and identify areas for improvement. They use statistical techniques to analyze employee data and make predictions about employee behavior. The Type 1 and Type 2 Error Analysis in Google Sheets course can help Human Resources Managers develop the skills they need to accurately analyze employee data and make sound decisions about human resources policies and practices.
For more career information including salaries, visit:
OpenCourser.com/course/1reoxe/type
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Type 1 and Type 2 Error Analysis in Google Sheets.
Comprehensive and up-to-date introduction to statistical methods used in psychology research. It provides a strong foundation for understanding the principles of hypothesis testing, including Type 1 and Type 2 errors.
Provides a comprehensive guide to the principles of statistical power analysis. It covers a wide range of topics, including the different types of power analysis, the calculation of power, and the interpretation of results.
Provides a hands-on introduction to the use of IBM SPSS Statistics for hypothesis testing. It includes a variety of examples and exercises that help students to develop their skills in using IBM SPSS Statistics.
Save
Provides a conceptual introduction to the principles of statistical thinking. It emphasizes the importance of understanding the underlying concepts of statistics, rather than simply memorizing formulas.
Provides a comprehensive overview of statistical methods used in machine learning. It covers a wide range of topics, including the different types of machine learning algorithms, the different types of data, and the different types of statistical tests.
Provides a gentle introduction to the principles of statistical learning. It is specifically designed for students with no prior knowledge of statistics, and it covers a wide range of topics, including the different types of machine learning algorithms, the different types of data, and the different types of statistical tests.
Provides a comprehensive and up-to-date introduction to the principles of statistical inference. It covers a wide range of topics, including the different types of statistical inference, the different types of data, and the different types of statistical tests.
Provides a comprehensive and up-to-date introduction to the principles of Bayesian data analysis. It covers a wide range of topics, including the different types of Bayesian models, the different types of data, and the different types of statistical tests.
Provides a practical introduction to the principles of machine learning. It is specifically designed for students with no prior knowledge of machine learning, and it covers a wide range of topics, including the different types of machine learning algorithms, the different types of data, and the different types of statistical tests.
Provides a practical introduction to the principles of data science. It is specifically designed for students with no prior knowledge of data science, and it covers a wide range of topics, including the different types of data, the different types of data analysis techniques, and the different types of data visualization techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1reoxe/type
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Tricia Bagley
Good to know
Teaches tools and skills that are relevant to industry's decision making process
Provides a strong foundation for learners new to interpreting analytical test findings
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