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Distributions and Variability Type of Unit: Project Prior Knowledge Students should be ...
Distributions and Variability
Type of Unit: Project
Students should be able to:
Represent and interpret data using a line plot.
Understand other visual representations of data.
Students begin the unit by discussing what constitutes a statistical question. In order to answer statistical questions, data must be gathered in a consistent and accurate manner and then analyzed using appropriate tools.
Students learn different tools for analyzing data, including:
Measures of center: mean (average), median, mode
Measures of spread: mean absolute deviation, lower and upper extremes, lower and upper quartile, interquartile range
Visual representations: line plot, box plot, histogram
These tools are compared and contrasted to better understand the benefits and limitations of each. Analyzing different data sets using these tools will develop an understanding for which ones are the most appropriate to interpret the given data.
To demonstrate their understanding of the concepts, students will work on a project for the duration of the unit. The project will involve identifying an appropriate statistical question, collecting data, analyzing data, and presenting the results. It will serve as the final assessment.
Students will apply what they have learned in previous lessons to analyze ...
Students will apply what they have learned in previous lessons to analyze and draw conclusions about a set of data. They will also justify their thinking based on what they know about the measures (e.g., I know the mean is a good number to use to describe what is typical because the range is narrow and so the MAD is low.).
Students analyze one of the data sets about the characteristics of sixth grade students that was collected by the class in Lesson 2. Students construct line plots and calculate measures of center and spread in order to further their understanding of the characteristics of a typical sixth grade student.
No new mathematical ideas are introduced in this lesson. Instead, students apply the skills they have acquired in previous lessons to analyze a data set for one attribute of a sixth grade student. Students make a line plot of the data and find the mean, median, range, MAD, and outliers. They use these results to determine a typical value for their data.
Goals and Learning Objectives
Describe an attribute of a typical sixth grade student using line plots and measures of center (mean and median) and spread (range and MAD).
Justify thinking about which measures are good descriptors of the data set.
This module describes the process of selecting the best available climate projection ...
This module describes the process of selecting the best available climate projection information and using it to develop “climate-adjusted weather” inputs to be used for modeling climate change impacts. These modeled impacts can be used for planning of future water resources. Specific steps of this process include: 1) Recognizing the general science and terms associated with Atmosphere-ocean General Circulation Models (AOGCMs); 2) Making AOGCMs more regionally applicable through bias correction and downscaling; 3) Determining climate change scenarios based on climate projections and selecting specific projections to inform each scenario; and 4) Developing climate-adjusted weather inputs associated with each climate change scenario. See An Introduction to the Downscaled Climate and Hydrology Projections Website for two related videos on how to access downscaled climate and hydrology projections.
Descriptive and inferential statistics for the behavioral and neurological sciences are considered. ...
Descriptive and inferential statistics for the behavioral and neurological sciences are considered. Techniques such as t-tests, factorial analysis of (co)variance, correlation, multiple regression, and nonparametric tests are introduced. Subject provides an introductory overview of some advanced methods such as path analysis, factor analysis, discriminant analysis, and analysis of functional MRI data. Basic issues of research design and methodology intimately associated with data analysis are discussed.