Create a scatter plot of your data

Assignment Help Engineering Mathematics

Reference no: EM131679795

Simple Linear Regression (SLR) Mini-Project

In this small project, you will be finding a data set to analyze from the internet that is interesting to you. You will then use an online linear regression calculator to develop a linear model based on your selected data set.

Complete the following and summarize your findings in the form of a report. The report should be well written, free of grammatical mistakes, and coherent. To obtain full credit on this assignment, your responses should be evidence of careful thought on the questions below. The report should be 1½ pages minimum, and no more than 4.

1. Find a data set to analyze online. (Several sets can be found from the attached links in the announcement.) Try to find one that is interesting to you! Briefly describe the context behind the data set. What is the data representing? What is the importance behind the data set? Is there an intuitive cause and effect relationship between your response and predictor variables?

2. Briefly discuss the possible concerns with your data set. Is the sample size large enough? Does the source of your data seem credible?

3. Using the link found in the announcement, create a scatter plot of your data. Inspect the scatter plot. Does it look as though a linear model would be suitable for the data set? Would you guess the linear model would have a positive trend or a negative trend? Would the relationship be strong, moderate, or weak? Are there any apparent outliers? That is, are there any data points that look unusual or look like they don't belong?

4. Underneath the scatter plot generated on the website, click "linear regression." Report the line of best fit and round the coefficients to the nearest tenth. Interpret the meaning of the slope and the intercept in the context of your model.

5. Underneath the graph of the line on the site, click "correlation coefficient." Report the correlation coefficient. What does this number say about the strength of your model? Is this model adequate for making predictions?

6. Now that you have your model and know its strength, what are some ways that you could improve the strength of your model? Would you need more data? Would you prefer to search for a more credible data source? Are there certain data points that you would remove from your existing data?

Attachment:- Assignment Files.rar

Reference no: EM131679795

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