In this project you will perform regression analysis on data to develop a mathematical model that relates two variables. Then you will use this model to make predictions.
Objectives
Find and use data directly from the internet
Produce a scatter plot of the data
Perform a regression analysis to find the equation of the line that best fits the data
Display the results, plotted data and the regression equation together for visual comparison
Use the model to make predictions
Make any conclusions about the data
Steps for the Project
1. Find and select your data
You are responsible for finding data. Check with your teacher to be sure the data you have selected would be a good fit for the project. Find a minimum of 20 data points.
2. Create a scatter plot.
You need to verify the presence of a relationship between the two variables. Make a scatter plot with these two variables, and show your independent and dependent variables. Label the axes and the graph accordingly (y vs. x).
3. Regression analysis.
Input your data onto a graph. Calculate the regression equation and the correlation coefficient. Add the regression line to your scatter plot.
4. Make conclusions.
Explain the significance of your results and how you can interpret them. Describe what sort of correlation exists. Is there a strong, weak, or no correlation in the data?
5. Apply it to the real world.
How might your conclusions impact the real world? What sorts of useful applications might you be able to make from your model? Write about ways that you might take advantage of the data. If you feel your data was not particularly useful due to a low correlation coefficient, write about what other patterns you may see in the scatter plot or how it is useful to know that there is little correlation between the variables.
6. Presentation.
You will present your results in 1-2 page written report and on a poster. The written report includes a table of your raw data, your calculations for finding the regression line/equation [in slope-intercept form: y = mx + b], any predictions, analysis and/or impact on the real world, and a reflection about the whole project. The poster should include a title, a scatterplot graph with the regression line, pictures to enhance your topic, the type of correlation, and possible predictions/impact on the world.
Checklist
Data table
Scatter plot with regression line
Regression equation and correlation coefficient
1 page report and reflection [raw data, regression equation, prediction, analysis and/or impact on the real world, reflection]
Poster [scatter graph with regression line, appropriate pictures, predictions, correlation]
Rubric
1
2
3
4
Representation of Data
Minimal data; data/scatter plots missing
Some missing data/scatter plots
Data present and accurate save for minor errors
Data accurately portrayed in tables and graphs
Application of Regression
Regression not performed
Regression results inaccurate due to faulty calculation
Regression results slightly off due to data entry
Regression results perfectly accurate
Conclusions from Results
Displays no evidence of understanding of conclusions
Misrepresents results by drawing conflicting conclusions
Results largely interpreted correctly with some minor misinterpretations
Shows clear understanding of the results and why they were reached
Real World Interpretation
Makes no connection to the real world
Has some ideas of how data connects to real world situation
Shows connections to real world but could be more thorough
Strongly connects results to real world applications
Presentation
Written report and/or poster is missing and demonstrates little understanding of the requirements. present
Written report and poster show demonstrate a progressing understanding, but or cannot explain ideas thoroughly
Written report and poster demonstrate a proficient understanding of the project and is able to communicate well
Written report and poster demonstrate an exemplary understanding of project and shows considerable time and thought invested
Data:
Drivers
races
wins
Dale Earnhardt Jr.
36
4
Austin Dillon
9
0
Clint Bowyer
24
0
Jeff Gordon
46
6
Kevin Harvick
33
2
Kurt Busch
33
1
Ron Hornaday Jr.
1
0
Joey Logano
18
1
Denny Hamlin
24
1
Corey LaJoie
2
0
Jimmie Johnson
32
3
Ricky Stenhouse Jr.
11
1
Matt Crafton
1
0
Elliott Sadler
27
0
Kyle Busch
25
1
Tony Stewart
35
4
Matt Kenseth
36
2
Paul Menard
21
0
Greg Biffle
28
1
Kasey Kahne
28
0
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