Overview
Math 227 Project: Exploring Association Between Two Quantitative Variables
In this project you will select two quantitative variables that may have a linear relationship and then describe and analyze the relationship between the variables using the methods of Chapter 2 (2.5-2.6). Your findings will be summarized in a report including the following parts: 1. Ask a question and collect data. a) Search for two quantitative variables that may have a linear correlation from the internet or any other source (but NOT from the lock5stat.com dataset page). Collect at least 15 observations of matched pair (x,y) data. Possible websites to look for data: ➢ https://datasetsearch.research.google.com/ Google dataset search ➢ https://www.kaggle.com Community published AI and ML repository ➢ https://data.gov/ US government data repository ➢ http://www.city-data.com/ for demographic information about cities ➢ http://www.cde.ca.gov/ta/ac/ap/ for academic performance index ➢ https://archive.ics.uci.edu/ml/datasets.php UCI Machine Learning Repository ➢ baseball-reference.com/; pro-football-reference.com/;basketball-reference.com/ sports data b) Discuss what your data represents, where you found it, and what question your analysis could answer. Use your intuition and/or experience to predict the direction and strength of the possible linear relationship. 2. Analyze the paired Data. a) Enter the raw data into StatKey as two labeled columns. Include a screenshot of this raw data in your report. b) Create a scatterplot of the data and include in report. Does the graph appear to show a linear relationship between the variables? Comment on the direction and strength of the scatterplot. Does it match your intuition before the data was collected? c) Report the linear correlation coefficient (r) between the two variables. d) Find the linear regression model. Interpret the slope and intercept for your model and comment on whether or not the intercept makes sense. e) Pick one of your data points and make a prediction of the response variable (Y-variable) based on the value of the explanatory variable (X-variable). Compute the residual for this predicted value. 3. Summarize Include a short summary of your findings discussing the relationship between the variables, the appropriateness of fitting a linear model and how well the linear model fits the data. The project will be graded out of 50 points on the completeness of the above requirements. Due on day of final exam at midnight. as a pdf file.