STA3215CBE Rasmussen Analysis with Correlation and Regression Assignment

Anonymous

Question Description

Competency

Determine and interpret the linear correlation coefficient, and use linear regression to find a best fit line for a scatter plot of the data and make predictions.

Scenario

According to the U.S. Geological Survey (USGS), the probability of a magnitude 6.7 or greater earthquake in the Greater Bay Area is 63%, about 2 out of 3, in the next 30 years. In April 2008, scientists and engineers released a new earthquake forecast for the State of California called the Uniform California Earthquake Rupture Forecast (UCERF).

As a junior analyst at the USGS, you are tasked to determine whether there is sufficient evidence to support the claim of a linear correlation between the magnitudes and depths from the earthquakes. Your deliverables will be a PowerPoint presentation you will create summarizing your findings and an excel document to show your work.

Concepts Being Studied

  • Correlation and regression
  • Creating scatterplots
  • Constructing and interpreting a Hypothesis Test for Correlation using r as the test statistic

You are given a spreadsheet that contains the following information:

  • Magnitude measured on the Richter scale
  • Depth in km

Using the spreadsheet, you will answer the problems below in a PowerPoint presentation.

What to Submit

The PowerPoint presentation should answer and explain the following questions based on the spreadsheet provided above.

  • Slide 1: Title slide
  • Slide 2: Introduce your scenario and data set including the variables provided.
  • Slide 3: Construct a scatterplot of the two variables provided in the spreadsheet. Include a description of what you see in the scatterplot.
  • Slide 4: Find the value of the linear correlation coefficient r and the critical value of r using α = 0.05. Include an explanation on how you found those values.
  • Slide 5: Determine whether there is sufficient evidence to support the claim of a linear correlation between the magnitudes and the depths from the earthquakes. Explain.
  • Slide 6: Find the regression equation. Let the predictor (x) variable be the magnitude. Identify the slope and the y-intercept within your regression equation.
  • Slide 7: Is the equation a good model? Explain. What would be the best predicted depth of an earthquake with a magnitude of 2.0? Include the correct units.
  • Slide 8: Conclude by recapping your ideas by summarizing the information presented in context of the scenario.

Along with your PowerPoint presentation, you should include your Excel document which shows all calculations.

Unformatted Attachment Preview

MAG 0,70 0,74 0,64 0,39 0,70 2,20 1,98 0,64 1,22 0,20 1,64 1,32 2,95 0,90 1,76 1,01 1,26 0,00 0,65 1,46 1,62 1,83 0,99 1,56 0,40 1,28 0,83 1,34 0,54 1,25 0,92 1,00 0,79 0,79 1,44 1,00 2,24 2,50 1,79 1,25 1,49 0,84 1,42 1,00 1,25 1,42 1,35 0,93 0,40 1,39 DEPTH 7,2 2,2 13,9 15,5 3,0 2,4 14,4 5,7 6,1 9,1 17,2 8,7 9,3 12,3 7,4 7,0 17,1 8,8 6,0 19,1 12,7 4,7 8,6 6,0 14,6 4,9 19,1 9,9 16,1 4,6 4,9 16,1 14,0 4,2 5,9 5,4 15,6 7,7 15,4 16,4 4,9 8,1 7,5 14,1 11,1 16,0 5,5 7,3 3,1 6,0 ...
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Tutor Answer

JesseCraig
School: Rice University

Attached.

Analysis with Correlation and Regression

Presented by:
Institution affiliation:
Date:

Introduction
➢ Correlation and regression analysis assist in identifying a relationship
between two variables.
➢ The variables analyzed in this presentation are the magnitudes and depths

from the earthquakes.
➢ The data set consists of 50 observations.
➢ The hypothesis tested is:

H0: Ƿ ≠ 0
H1: Ƿ = 0

Scatter plot
➢ The data points are evenly

DEPTH VS. MAG
25.0

distributed with no clear

relationship between the two
variables.
➢ The slope of the trend line is

DEPTH

20.0
y = 0.1933x + 9.4225
R² = 0.0005

15.0
10.0
5.0

0.0
0.00

0.50

1...

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Anonymous
Thanks for the help.

Anonymous
Outstanding. Studypool always delivers quality work.

Anonymous
Tutor was very helpful and took the time to explain concepts to me. Very responsive, managed to get replies within the hour.

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