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I just called my cardiologist's office for an answer to this one (I'm scheduled for a nuclear stress test that uses the Bruce protocol). Here's what the person who will administer my test told me:

It's the percentage of 90 degrees, 0% being no incline, 100% being straight up, perpendicular to the floor. The test begins at 1.7 mph with an incline of 10%, or 9 degrees. The incline is raised every three minutes by 2%, or 1.8 degrees. So at Level 2, the incline is 10.8 degrees, Level 3 12.6 degrees, Level 4 15.2 degrees, etc. -Joe

Q: What is the correlation between gradient of incline and degrees of the angle in Bruce protocol?

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There is no correlation between degrees and metres. They are totally different measurements so no conversion is possible.

To calculate the degrees of freedom for a correlation, you have to subtract 2 from the total number of pairs of observations. If we denote degrees of freedom by df, and the total number of pairs of observations by N, then: Degrees of freedom, df=N-2. For instance, if you observed height and weight in 100 subjects, you have 100 pairs of observations since each observation of height and weight constitutes one pair. If you want to calcualte the correlation for these two variables (height and weight), your degrees of freedom would be calculated as follows: N=100 df=N-2 Therefore, df=100-2=98 The degrees of freedom are a function of the parameters; you subtract the amount of parameters free to vary from the n to get the df, so logically in a correlation we should subtract 2 from n, as we are looking at a correlation between 2 variables.

Yes. 1.08 is about 47.2 degrees.

The total degrees between 45 degrees and -5 degrees is 50 degrees.

Ok, 'rad' is 'radians,' 'deg' is 'degrees,' and 'grad' is 'gradient,' all used in calculus.

Related questions

There is no correlation between degrees and metres. They are totally different measurements so no conversion is possible.

The correlation between cricket chirps and the temperature is very approximate.

No higher than 140 degrees F, (60 degrees C).

To calculate the degrees of freedom for a correlation, you have to subtract 2 from the total number of pairs of observations. If we denote degrees of freedom by df, and the total number of pairs of observations by N, then: Degrees of freedom, df=N-2. For instance, if you observed height and weight in 100 subjects, you have 100 pairs of observations since each observation of height and weight constitutes one pair. If you want to calcualte the correlation for these two variables (height and weight), your degrees of freedom would be calculated as follows: N=100 df=N-2 Therefore, df=100-2=98 The degrees of freedom are a function of the parameters; you subtract the amount of parameters free to vary from the n to get the df, so logically in a correlation we should subtract 2 from n, as we are looking at a correlation between 2 variables.

A temperature gradient of 10 degrees per metre.

Varies obviously, but the average geothermal gradient in the Earth's continental crust is 25 degrees Centigrade/kilometre

Yes. 1.08 is about 47.2 degrees.

Geothermal gradient is the rate of increasing temperature with respect to an increasing depth in the Earth's interior. It is approximately 25 degrees Celsius per kilometer of depth.

The soil temperatre increases as depth increases due to the heat created by the compression of the surrounding earth. The rate of change of temperature with depth is referred to as the geothermal gradient. The geothermal gradient varies depending on location, so there is no uniform answer. On average, the geothermal gradient is approximately 75 degrees F per mile. In volcanically active areas, the gradient can be as high as 150 degrees F per mile. In ocean trenches, the gradient may be as low as 15 degrees F per mile. Decay of naturally occurring radioactive elements may also cause localized increases in temperature in some locations.

There are several statistical measures of correlation: some require only a nominal scale, that is, data classified according to two criteria; others require an ordinal scale, which is the ability to determine whether one measurement is bigger or smaller than another; others require an interval scale, which allows you to determine the difference in values but not the ratio between them. [A good example of the latter is temperature measured in any scale other than Kelvin: the difference between 10 degrees C and 15 degrees C is 5 C degrees, but 15 C is not 1.5 times as warm as 10 C.]The contingency coefficient, which is suitable for nominal data, has a chi-squared distribution.The Spearman rank correlation, requiring ordinal data, has its own distribution for small data sets but as the number of units increases to n, the distribution approaches Student's t-distribution with n-2 degrees of freedom.The Kendall rank correlation coefficient can be used in identical situations and gives the same measure of significance. However, the Kendall coefficient can also be used to test partial correlation - whether the correlation between two variables is "genuine" or whether it arises because both variables are actually correlated to a third variable.The Pearson's product moment correlation coefficient (PMCC) is the most powerful but requires measurement on an interval scale as well as an underlying bivariate Normal distribution.The significance levels of these correlation measures are tabulated for testing.A simple "rule of thumb" for testing the significance of PMCC is that values below -0.7 or above 0.7 are highly significant. Values in the ranges (-0.7, -0.3) and (0.3, 0.7) are moderate, and values between -0.3 and +0.3 are not significant.

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The slope of the line between the two points is (1.5/10) = 15% .The angle is the angle whose tangent is 0.15, roughly 8.53 degrees (rounded)