As a requirement for Math 107, you are required to do two projects in the term. Here is the first project and it is about linear model – curve fitting, whose example we saw in class.

In this project, you are asked to find a set of data from one of the sources of data (databases) that was provided in the earlier reading example (please read the notes if you have not read them). You are free to find and use a set of data of your choice outside the databases that have been provided with but make sure your data set exhibits a linear pattern if y (the dependent variable) is plotted against x, the independent variable.

Please do the following:

1. Plot the points (x, y) to obtain a scatter plot. Visually judge whether the data points

exhibit a relatively linear trend. Note that you cannot work on the data set unless you

can visually judge that a linear trend exists. If not simply look for another data set.

2. If the data exhibit linearity, fit a linear regression model to the set of data and

superimpose the line of best fit on the scatter plot.

3. State the slope of the line of best fit (that is, the slope of the regression equation).

Carefully interpret the real world meaning of this slope with respect to the problem

you are working with in a sentence or two.

4. Find and state the value of , the correlation coefficient and , the coefficient of

determination from your computer printout. Discuss your findings in a few sentences.

Is R positive or negative? Is the line a good fit to the data? Why or why not? Is the

linear relationship very strong, weak, or nonexistent? Why?

5. Choose a value of interest within the range of the independent variable (x) and use

the line of best fit (the regression equation) to make an estimate or prediction.

Show calculation work.

6. Write a brief narrative of a paragraph or two about your project. Summarize your

findings and be sure to mention any aspect of the linear model project (topic, data,

scatter plot, the regression line, R or estimate, etc) that you found particularly

important or interesting. You may submit all of your project in one document or a

combination of documents, which may consist of word processing documents or

spread sheets or scanned handwritten work, provided it is clearly labeled where

each task can be found. If the scanned work is too faint, such that I am not able to

read it, I will not award you any points. Your work MUST be submitted Online via

the LEO classroom, only (not be submitted in class by hand).

Projects are graded on the basis of completeness, correctness, ease in locating all of the checklist items, and the strength of the narrative portions.

TECHNOLOGY

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To complete the Linear Model portion of the project, you must use technology to create scatter plot, regression line, and superimpose the regression line on the scatter plot, and find R and from the computer printout.

You can:

1. Use Microsoft Excel,

2. Open Office

3. Use hand-held graphing calculator (see section 1.4 in your textbook for help with

Texas Instrument hand-held calculators)

4. Use an online tool. Visit Free Online Tool (VIDEO) to see how to use online

graphing calculator at https://www.meta-calculator.com/online/ to find the line of best

fit, R, , and create a scatter plot, along with the regression line.

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In my example, I used a software, called SPSS. Some of you may have it at your places of work but may not know how to use it. So do not use it. Simply type, ‘linear regression analysis’ in your google search box and several will show up for you to pick one and do your analysis.

This project is worth 100 points. These 100 points will be worth 12% of your course grade. I just want to note here that, students have in the past, earned 10 points because of not following the instructions band waiting to do it in the last minute.

This project is due on Sunday, April 15, 2018.

As a guide, before you begin to work on your data set, please use my example to reproduce the same results. This will let you know that what you will be doing with your data will be correct.