Home » 6-1 discussion: applications of correlation discussion topic | applied statistics | Southern New Hampshire University

6-1 discussion: applications of correlation discussion topic | applied statistics | Southern New Hampshire University

 

Choose one of the following two prompts to respond to. In your two follow up posts, respond at least once to each prompt option. Use the discussion topic as a place to ask questions, speculate about answers, and share insights. Be sure to embed and cite your references for any supporting images.

Perform the following analysis by analyzing a possible linear relation between two variables.

Option 1:

Using the data set provided from the NOAA for Manchester, NH, select any month between January 1930 and December 1958. Use the variables “MMXT” and “MMNT” for your analysis. Begin with your chosen month and analyze the next 61 data values (i.e. 5 years and 1 month) to determine if a relationship exists between the maximum temperature (MMXT) and the minimum temperature (MMNT).

Using Excel, StatCrunch, etc. create a scatter plot for your sample. Determine the
linear regression equation and correlation coefficient. Embed this scatter plot in your
initial post.

For your responses to your classmates (two responses required): Discuss the relationships between the scatter plot, the correlation coefficient, and the linear regression equation for the sample. Comment on the similarities and differences between your correlation and linear regression equation and that of your classmates. Why are there differences since you are drawing from the same population? Did you expect the differences will be large? Why or why not?

Option 2:

Write a mathematical scenario to describe each of the scatter plots.

MAT 240 6-1 Discussion Image 1

MAT 240 6-1 Discussion Image 2

Answer prompts made by fellow students with the following information:

Discuss an alternative scenario to represent the data in the scatter plots. In this scenario, assume there is correlation but where it would be inappropriate to conclude causation.

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