# For michelle lewis only | Mathematics homework help

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ASSIGNMENT 1

The purpose of the Signature Assignment is to have you work with real-life data to answer a real-life question using the tools, technology, and skills of MTH/219. In Week 2, you will work with the data you collected to analyze the data to draw conclusions.

Review the instructional video on how to use regression in Microsoft® Excel®.

Input your data into Microsoft® Excel® (data and years).

Create a scatterplot.

Insert a linear trendline. Make sure to show the equation and the R-square value. Also, make sure you label each axis and give a title to your graph. If you need help creating your visual, watch How to create scatterplot with trendline in Excel®.

In the same Microsoft® Excel® worksheet, answer the following questions. Each question should be 90 to 175 words.

1. Does the line fit the data? How can you tell?
2. What does the slope of the line mean in your real-life data? How can you interpret the slope of your line?
3. What does the y-intercept mean in your real-life data? How can you interpret the y-intercept?

ASSIGNMENT 2

The purpose of the Signature Assignment is to have you work with real-life data to answer a real-life question using the tools, technology, and skills of MTH/219. In Week 3, you will determine the type of polynomial your data is best represented. You will use that polynomial to make a prediction about your data.

Review the instructional video on how to use regression in Microsoft® Excel® (if you have forgotten from Week 2).

Input your data into Microsoft® Excel® (data and years).

Create a scatterplot.

Insert a polynomial trendline with degree of 4. Make sure to show the equation and the R-square value. Also make sure you label each axis and give a title to your graph. Then repeat this process for a 5th degree polynomial.

Answer the following questions in the same Microsoft® Excel® worksheet. Each question should be 175 to 260 words.

1. Which of the polynomials fit the data better? How can you tell?
2. Compare and contrast the polynomial regression with the linear regression from Week 2. What is the best model for your data so far?
3. What happens to your polynomial models over time? In other words, what do you expect to happen to the value if you measure it 50 years from now?

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