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Excel Data Analysis Project
Excel Data Analysis Project
Excel Data Analysis Project
Excel Data Analysis Project
Excel Data Analysis Project
Excel Data Analysis Project
Excel Data Analysis Project
Excel Data Analysis Project
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Description

This mini Project covers the concepts of scatterplots and linear regression lines using data analysis in Excel. The project introduces the concepts of scatterplots and lines of best fit, has students work through a basic analysis of a data set using Excel, then has students complete a more complex analysis of a baseball data set. I created this for my standard 8th grade math class to cover CCSSM.8.SP.1, CCSSM.8.SP.3, MP.4, and MP.5 in an engaging and practical way. I had students working in pairs, but this could be used for individual work as well. For students who are more advanced and need a little more of a challenge, I have included extension questions and a data file to accompany them.

Included are:

~Project (.pdf)

~Project (.docx)

~Powerpoint to introduce project to class (.ppt)

~Excel data file for project (.xls)

~Optional project extension (.docx)

~Excel data file for optional extension (.xls)

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Reported resources will be reviewed by our team. Report this resource to let us know if this resource violates TPT's content guidelines.

Excel Data Analysis Project

PlayThroughMath
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Highlights

Digital downloads
Grades icon
Grades
7th - 11th
Subjects icon
Subjects
Standards icon
Standards
Pages
N/A
Teaching Duration
3 days

Description

This mini Project covers the concepts of scatterplots and linear regression lines using data analysis in Excel. The project introduces the concepts of scatterplots and lines of best fit, has students work through a basic analysis of a data set using Excel, then has students complete a more complex analysis of a baseball data set. I created this for my standard 8th grade math class to cover CCSSM.8.SP.1, CCSSM.8.SP.3, MP.4, and MP.5 in an engaging and practical way. I had students working in pairs, but this could be used for individual work as well. For students who are more advanced and need a little more of a challenge, I have included extension questions and a data file to accompany them.

Included are:

~Project (.pdf)

~Project (.docx)

~Powerpoint to introduce project to class (.ppt)

~Excel data file for project (.xls)

~Optional project extension (.docx)

~Excel data file for optional extension (.xls)

Report this resource to TPT
Reported resources will be reviewed by our team. Report this resource to let us know if this resource violates TPT's content guidelines.

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Questions & Answers

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Standards

to see state-specific standards (only available in the US).
Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.
Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. For example, in a linear model for a biology experiment, interpret a slope of 1.5 cm/hr as meaning that an additional hour of sunlight each day is associated with an additional 1.5 cm in mature plant height.
Model with mathematics. Mathematically proficient students can apply the mathematics they know to solve problems arising in everyday life, society, and the workplace. In early grades, this might be as simple as writing an addition equation to describe a situation. In middle grades, a student might apply proportional reasoning to plan a school event or analyze a problem in the community. By high school, a student might use geometry to solve a design problem or use a function to describe how one quantity of interest depends on another. Mathematically proficient students who can apply what they know are comfortable making assumptions and approximations to simplify a complicated situation, realizing that these may need revision later. They are able to identify important quantities in a practical situation and map their relationships using such tools as diagrams, two-way tables, graphs, flowcharts and formulas. They can analyze those relationships mathematically to draw conclusions. They routinely interpret their mathematical results in the context of the situation and reflect on whether the results make sense, possibly improving the model if it has not served its purpose.
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