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Temesgen Kifle
To address questions like these, the course is divided into three topics:
- In Topic 1, we examine the characteristics of point estimates – measurements that we take from samples to make inferences about a population.
- Topic 2 deals with confidence intervals , which help us to establish upper and lower limits about where we think the “true” population characteristic lies, at a certain level of confidence.
- In Topic 3, which is the largest and most important part of this course, we explore hypothesis testing. This includes one-, two- and many-sample forms of testing.
Register for this course and see more details by visiting:
OpenCourser.com/course/mpd806/statistics
What's inside
Learning objectives
- Upon successful completion of this course, you will be able to:
- Describe the characteristics of point estimates and how they can be used to make inferences about a population.
- Calculate upper and lower bounds of confidence intervals for given levels of confidence.
- Describe the general process for conducting a hypothesis test using the z-score or p-value methods.
- Conduct a hypothesis test and interpret the meaning of the result for: one mean or proportion against a predetermined standard; two means or proportions against one another; paired data from repeated measurements of the same sample or matched pairs of individuals; and many means using an analysis of variance (anova).
- Upon successful completion of this course, you will be able to:
- Describe the characteristics of point estimates and how they can be used to make inferences about a population.
- Calculate upper and lower bounds of confidence intervals for given levels of confidence.
- Describe the general process for conducting a hypothesis test using the z-score or p-value methods.
- Conduct a hypothesis test and interpret the meaning of the result for: one mean or proportion against a predetermined standard; two means or proportions against one another; paired data from repeated measurements of the same sample or matched pairs of individuals; and many means using an analysis of variance (anova).
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a solid foundation for learners who are interested in statistical analysis
Teaches concepts that are highly relevant in both industry and academia
Covers a comprehensive range of topics in statistical inference
Taught by instructors who are recognized for their expertise in statistics
Provides opportunities for hands-on practice through exercises and assignments
Requires learners to have a prerequisite understanding of probability and calculus
Save this course
Save Statistics for Business Analytics: Samples and Populations to your list so you can find it easily later:
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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 for Business Analytics: Samples and Populations with these
activities:
Review basic probability concepts
Show steps
Strengthens understanding of probability, which is a prerequisite for hypothesis testing.
Browse courses on
Probability
Show steps
-
Review notes from previous probability courses or textbooks.
-
Solve practice problems related to probability.
-
Watch online videos or tutorials on probability.
-
Discuss probability concepts with classmates or a tutor.
Review 'Introduction to the Practice of Statistics' by David S. Moore
Show steps
Provides a solid foundation in the concepts of inferential statistics and probability.
View
The Practice of Statistics in the Life Sciences
on Amazon
Show steps
-
Read the assigned chapters.
-
Take notes and highlight important concepts.
-
Complete the practice exercises at the end of each chapter.
-
Review the key terms and concepts before each lecture.
Create a course notebook
Show steps
Organizes course materials and promotes active learning by note-taking.
Show steps
-
Gather lecture notes, handouts, and other course materials.
-
Create a notebook or binder for the course.
-
Organize the materials into sections and subsections.
-
Take notes during lectures and add them to the notebook.
-
Review the notebook regularly and summarize the key concepts.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Watch YouTube videos on hypothesis testing
Show steps
Provides visual and interactive explanations of hypothesis testing.
Browse courses on
Hypothesis Testing
Show steps
-
Search for YouTube videos on hypothesis testing.
-
Watch the videos and take notes.
-
Pause the videos to summarize the key concepts.
-
Review the videos before each lecture or quiz.
Form a study group with classmates
Show steps
Provides opportunities for collaboration, discussion, and peer learning.
Show steps
-
Connect with classmates through the course discussion board or social media.
-
Schedule regular study sessions.
-
Review course materials together.
-
Work through practice problems as a group.
-
Quiz each other on key concepts.
Create a study guide
Show steps
Compiling your notes, assignments, quizzes, and exams will help you to synthesize the material
Browse courses on
Hypothesis Testing
Show steps
-
Organize and review notes
-
Complete all assignments
Solve practice problems using Z-scores
Show steps
Solidifies understanding of Z-scores and their use in hypothesis testing.
Show steps
-
Download practice problems from the course website or textbook.
-
Attempt to solve the practice problems using the Z-score formula.
-
Check your answers against the provided solutions.
-
Repeat steps 2-3 until you have a good understanding of Z-scores.
Attend a workshop on statistical software
Show steps
Provides hands-on training and practical experience with statistical software.
Browse courses on
Statistical Software
Show steps
-
Register for a workshop on statistical software.
-
Attend the workshop and complete the exercises.
-
Apply the skills learned in the workshop to your course assignments.
-
Share your knowledge with classmates.
Conduct a hypothesis test using a p-value
Show steps
Provides hands-on experience in conducting hypothesis tests and interpreting the results.
Show steps
-
Choose a research question and formulate a hypothesis.
-
Collect data and calculate the test statistic.
-
Determine the p-value using a statistical software package.
-
Make a decision about the null hypothesis based on the p-value.
-
Write a report summarizing the results of your hypothesis test.
Review basic probability concepts
Show steps
Strengthens understanding of probability, which is a prerequisite for hypothesis testing.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review notes from previous probability courses or textbooks.
- Solve practice problems related to probability.
- Watch online videos or tutorials on probability.
- Discuss probability concepts with classmates or a tutor.
Review 'Introduction to the Practice of Statistics' by David S. Moore
Show steps
Provides a solid foundation in the concepts of inferential statistics and probability.
View
The Practice of Statistics in the Life Sciences
on Amazon
Show steps
Show steps
Show steps
- Read the assigned chapters.
- Take notes and highlight important concepts.
- Complete the practice exercises at the end of each chapter.
- Review the key terms and concepts before each lecture.
Create a course notebook
Show steps
Organizes course materials and promotes active learning by note-taking.
Show steps
Show steps
Show steps
- Gather lecture notes, handouts, and other course materials.
- Create a notebook or binder for the course.
- Organize the materials into sections and subsections.
- Take notes during lectures and add them to the notebook.
- Review the notebook regularly and summarize the key concepts.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Watch YouTube videos on hypothesis testing
Show steps
Provides visual and interactive explanations of hypothesis testing.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Search for YouTube videos on hypothesis testing.
- Watch the videos and take notes.
- Pause the videos to summarize the key concepts.
- Review the videos before each lecture or quiz.
Form a study group with classmates
Show steps
Provides opportunities for collaboration, discussion, and peer learning.
Show steps
Show steps
Show steps
- Connect with classmates through the course discussion board or social media.
- Schedule regular study sessions.
- Review course materials together.
- Work through practice problems as a group.
- Quiz each other on key concepts.
Create a study guide
Show steps
Compiling your notes, assignments, quizzes, and exams will help you to synthesize the material
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Organize and review notes
- Complete all assignments
Solve practice problems using Z-scores
Show steps
Solidifies understanding of Z-scores and their use in hypothesis testing.
Show steps
Show steps
Show steps
- Download practice problems from the course website or textbook.
- Attempt to solve the practice problems using the Z-score formula.
- Check your answers against the provided solutions.
- Repeat steps 2-3 until you have a good understanding of Z-scores.
Attend a workshop on statistical software
Show steps
Provides hands-on training and practical experience with statistical software.
Browse courses on
Statistical Software
Show steps
Show steps
Show steps
- Register for a workshop on statistical software.
- Attend the workshop and complete the exercises.
- Apply the skills learned in the workshop to your course assignments.
- Share your knowledge with classmates.
Conduct a hypothesis test using a p-value
Show steps
Provides hands-on experience in conducting hypothesis tests and interpreting the results.
Show steps
Show steps
Show steps
- Choose a research question and formulate a hypothesis.
- Collect data and calculate the test statistic.
- Determine the p-value using a statistical software package.
- Make a decision about the null hypothesis based on the p-value.
- Write a report summarizing the results of your hypothesis test.
Career center
Learners who complete Statistics for Business Analytics: Samples and Populations will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use data to solve problems and make informed decisions. The Statistics for Business Analytics: Samples and Populations course would be a valuable asset for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Data Analysts who are responsible for using data to identify trends and patterns, and to make recommendations on how to improve business performance.
Statistician
Statistician
Statisticians use data to solve problems and make informed decisions. The Statistics for Business Analytics: Samples and Populations course would be a valuable asset for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Statisticians who are responsible for using data to make recommendations on how to improve business performance.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make informed decisions. The Statistics for Business Analytics: Samples and Populations course would be a valuable asset for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Data Scientists who are responsible for using data to make recommendations on how to improve business performance.
Market Research Analyst
Market Research Analyst
Market Research Analysts study market conditions and consumer behavior to help businesses make informed decisions. The Statistics for Business Analytics: Samples and Populations course would be a valuable asset for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Market Research Analysts who are responsible for conducting surveys and analyzing data to identify trends and patterns in consumer behavior.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use data to build and train machine learning models. The Statistics for Business Analytics: Samples and Populations course would be a valuable asset for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Machine Learning Engineers who are responsible for using data to build and train machine learning models.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make predictions and develop trading strategies. The Statistics for Business Analytics: Samples and Populations course would be a valuable asset for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Quantitative Analysts who are responsible for using data to make predictions about future market trends.
Actuary
Actuary
Actuaries use data to assess risk and uncertainty. The Statistics for Business Analytics: Samples and Populations course would be a valuable asset for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Actuaries who are responsible for using data to make recommendations on how to manage risk.
Financial Analyst
Financial Analyst
Financial Analysts use data to make recommendations on investments and financial decisions. The Statistics for Business Analytics: Samples and Populations course would be beneficial for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret financial data. This course would be particularly helpful for Financial Analysts who are responsible for making recommendations on investments and financial decisions.
Business Analyst
Business Analyst
Business Analysts use data to identify problems and opportunities for businesses. The Statistics for Business Analytics: Samples and Populations course would be beneficial for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Business Analysts who are responsible for using data to make recommendations on how to improve business performance.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency and effectiveness of business operations. The Statistics for Business Analytics: Samples and Populations course would be beneficial for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Operations Research Analysts who are responsible for using data to identify inefficiencies and to make recommendations on how to improve operations.
IT Manager
IT Manager
IT Managers plan and direct the activities of an organization's IT department. The Statistics for Business Analytics: Samples and Populations course may be helpful for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for IT Managers who are responsible for using data to improve the performance of the IT department.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. The Statistics for Business Analytics: Samples and Populations course may be helpful for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Software Engineers who are responsible for using data to improve the performance of software applications.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. The Statistics for Business Analytics: Samples and Populations course may be helpful for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Database Administrators who are responsible for using data to improve the performance of databases.
Web Developer
Web Developer
Web Developers design and develop websites. The Statistics for Business Analytics: Samples and Populations course may be helpful for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Web Developers who are responsible for using data to improve the performance of websites.
Project Manager
Project Manager
Project Managers plan and execute projects. The Statistics for Business Analytics: Samples and Populations course may be helpful for someone in this role, as it would provide them with the skills and knowledge necessary to collect, analyze, and interpret data. This course would be particularly helpful for Project Managers who are responsible for using data to improve the performance of projects.
For more career information including salaries, visit:
OpenCourser.com/course/mpd806/statistics
Reading list
We've selected 13 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
Statistics for Business Analytics: Samples and Populations.
Provides a comprehensive overview of statistics, from the basics to advanced topics. It valuable resource for those who want to gain a deeper understanding of the field of statistics.
Covers a wide range of topics in statistics for business and economics. It is commonly used as a textbook at the undergraduate level. It provides more depth in hypothesis testing and experimental design than the course covers.
Provides an introduction to modern statistical learning methods, such as supervised and unsupervised learning, and data mining techniques. It valuable resource for those interested in exploring the intersection of statistics and data science.
Provides a comprehensive overview of statistical inference, including point estimation, hypothesis testing, and confidence intervals. It useful reference for those interested in a deeper understanding of the theoretical foundations of statistics.
This open-source textbook provides an introduction to statistics using the Python programming language. It covers the basics of probability, sampling, and inference. It useful resource for those who want to learn statistics in a hands-on, interactive way.
Covers econometrics methods and their applications in economics, finance, and other fields. It useful reference for those interested in understanding and applying statistical methods in economic contexts.
Covers statistical methods commonly used in psychology. It provides a good overview of the topics covered in the course, with a focus on applications in the field of psychology.
Provides an introduction to Bayesian statistics using the R and Stan programming languages. It covers the basics of Bayesian modeling and inference. It valuable resource for those interested in exploring Bayesian approaches to statistical problems.
Covers statistical methods for designing and analyzing biological experiments. It useful reference for those working in the field of biology or ecology.
Provides a strong foundation in mathematical statistics, including probability theory, estimation, and hypothesis testing. It useful reference for those who want to delve deeper into the mathematical underpinnings of statistics. It is more technically challenging than the course's scope.
Save
This open-source textbook provides an introductory overview of statistics. It covers the basics of probability, sampling, and inference. It useful resource for those who want to supplement the course with additional introductory material.
Provides a non-technical introduction to statistics. It is written in a clear and accessible style, making it suitable for those with little or no background in statistics.
Provides an introduction to Bayesian data analysis, a statistical approach that uses probability distributions to represent uncertainty. It valuable resource for those interested in exploring alternative statistical paradigms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mpd806/statistics
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Effort
4 to 8 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
The University of Queensland
Instructor
Temesgen Kifle
Language
English
Transcript
English
Good to know
Provides a solid foundation for learners who are interested in statistical analysis
Teaches concepts that are highly relevant in both industry and academia
Covers a comprehensive range of topics in statistical inference
Taught by instructors who are recognized for their expertise in statistics
Provides opportunities for hands-on practice through exercises and assignments
Requires learners to have a prerequisite understanding of probability and calculus
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