Cancer has many different forms. The causes for different types of cancers are largely unknown, but various studies have linked these variables to increases or decreases in levels of cancer. Let the variable Cancer per 100,000 be your response variable. Out of the eight other variables, choose one to be your predictor variable. Your goal will be to create a linear regression model, analyze the results, and see whether your predictor is a statistically significant indicator of higher or lower levels of cancer.
1. Review the Linear Regression in SPSS guide.
2. Copy your two columns of data into a spreadsheet as you did in Unit 1. Make sure to note that Cancer per 100,000 represents your “Y” variable, and the predictor variable that you selected is your “X” variable.
3. Start off by getting a visualization of your data. Using your two data columns, make a scatterplot with statistical software. Place the scatterplot in your project document and comment on what you see. Does there appear to be a trend? (50-75 words)
4. Regardless of your investigation in 2, begin a numerical analyzation by creating a least-squares regression equation. Use statistical software to generate the equation and then post the equation in your project document. Interpret the slope. What does the slope indicate in terms of the independent and dependent variable? (50-75 words)
5. Conduct a formal hypothesis test on the slope of your regression equation. Test whether your slope is significantly different from zero. What do your results imply about the relationship between your independent and dependent variable? (approximately 100 words)
6. Calculate the coefficient of determination (r2). Interpret the result. Does this result support your conclusion from step 4? (50-75 words)
7. Check the assumptions of a regression model by creating a residual plot. Place the residual plot in your response and interpret the results. Does the residual plot imply the assumptions of a regression model are met? (50-75 words)
8. State a conclusion for your work. Using the evidence you’ve gathered in the previous steps, make your own conclusion on the relationship between the two variables. Make sure to connect your results to the real-world variables of your data set. Does your chosen independent variable predict the levels of cancer in counties of Maine? (100-150 words)
Remember that cancer is a complicated disease. Each type of cancer, in each location of the body, has its own properties and is influenced by different factors. If you’re interested, you could check the other predictors to see if any of those were significant, too.
This material may consist of step-by-step explanations on how to solve a problem or examples of proper writing, including the use of citations, references, bibliographies, and formatting. This material is made available for the sole purpose of studying and learning - misuse is strictly forbidden.