Adaptive Perseverance:
Application: Students will encounter challenges in performing statistical tests correctly and interpreting results. They will learn to persist and find multiple ways to approach problems.
Learner’s Mindset:
Application: Students will cultivate a positive attitude towards learning statistical methods and continuously seek to improve their understanding and skills through practice and feedback.
Communication:
Application: Students will practice clearly articulating their findings from statistical tests, both in written reports and oral presentations, ensuring their conclusions are understood.
Responsibility:
Application: Students will be responsible for the integrity of their data collection and analysis, ensuring that their conclusions are based on accurate and ethical practices.
Global Citizenship:
Application: Students will recognize the importance of statistical testing in making informed decisions that can impact local and global communities, such as public health studies or market research.
Critical Thinking:
Application: Students will develop critical thinking skills by analyzing data, questioning assumptions, and evaluating the validity of their statistical tests and conclusions.
Collaboration:
Application: Students will work in teams to collect data, perform statistical analyses, and share their findings, leveraging each other's strengths and perspectives to enhance their understanding.
What are the steps involved in performing a hypothesis test?
How do z-tests and t-tests differ, and when should each be used?
What does a P-value indicate about the significance of a test result?
Apply Hypothesis Testing: Students will be able to perform hypothesis testing using z-tests and t-tests to evaluate claims about population parameters.
Interpret P-values: Students will demonstrate the ability to calculate and interpret P-values to determine the statistical significance of their test results.
Compare Means: Students will compare sample means using appropriate statistical tests and draw conclusions about the population based on their findings.
Data Analysis: Learn to collect, organize, and analyze data using statistical software (e.g., Excel, SPSS, R).
Hypothesis Testing: Master the steps of hypothesis testing, including setting up null and alternative hypotheses, selecting the right test, and interpreting results.
Statistical Reasoning: Develop the ability to reason statistically, understand probability concepts, and make informed decisions based on data.
HSS-IC.A.1: Understand statistics as a process for making inferences about population parameters based on a random sample from that population.
HSS-IC.B.4: Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling.
HSS-IC.B.5: Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.
Textbooks:
The Practice of Statistics by Starnes, Tabor, Yates, and Moore
Introduction to the Practice of Statistics by Moore, McCabe, and Craig
Online Tutorials and Courses:
Khan Academy: Statistics and Probability
Coursera: Statistics with R
Software Tools:
Excel: For basic data analysis and visualization.
R: For more advanced statistical analysis.
SPSS: For comprehensive statistical analysis
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