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Owner: bao16c
Created: Nov 11, 2017
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Views: 337
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Body Temperature Part 2
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Data set 1. Body Temperature   [Info]
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Result 1: Scatter Plot   [Info]
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 The two quantitive data values I used were body temperature and heart rate. The scatter plot is positive, however the data is spread out quite a bit.

Yes, their are outliers that should be removed such as (100,78), (98,89), (98.2,57), (98.7,59), (99.3,63), (97.1,82), (97,80), (96.8,75),(97.7,84)

An appropriate significance level for my data would be .05 which will allow for a greater margin of error. 


Result 2: Simple Linear Regression   [Info]
Simple linear regression results:
Dependent Variable: Heart Rate
Independent Variable: Body Temp
Heart Rate = -166.28472 + 2.443238 Body Temp
Sample size: 130
R (correlation coefficient) = 0.2536564
R-sq = 0.064341571
Estimate of error standard deviation: 6.8577393

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-166.2847280.912346 ≠ 0128-2.05512170.0419
Slope2.4432380.82351902 ≠ 01282.96682650.0036

Analysis of variance table for regression model:

My correlation coefficient is .25 which representing a fairly weak correlation between body temperature and heart rate.

My terms are significant because my p-value is .0036 which is way below .05.

The line of best fit would be -166.3 heart rate and the slope would be a rise of 2.44. Therefore for every increase in heart rate theres a 2.44 increase in heart rate.

 R squared is approximately .06 which represents that 6% of the variation in body temperature is accounted for heart rate.


Result 3: Scatter Plot line   [Info]
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The line of best fit is not a good fit for my data because it only hits about 7 data points throughout the graph. There are data points not being represented by the line of best fit.

There is a very weak correlation between the heart rate and body temperature. There is no causation between the two variables.

HTML link:
<A href="">Body Temperature Part 2</A>

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By xg15
Nov 14, 2017

Nice report !

Always Learning