I use
the same curve fitting equations as before.
Not exact matches
If you feel like your jeans just aren't
fitting the
same way (even after you are back to pre-baby weight), that could just be because the hips separate and essentially your body has changed (hey,
curves are hot mama!)
I used the
same easy method as the other skirts, which pretty much involves creating a
curved waistband to
fit your waist measurement, plus seam allowances.
They hug
curves and shape at the
same time to help with
fit.
The first thing I noticed was just how well the BlackBerry
Curve 9320 smartphone
fit in my hand, and how good it looks at the
same time.
These trends are derived from exactly the
same data as those used in the original figure, that was used to argue that the global warming had stopped — by two professors and a statistician, the very
same who performed
curve -
fitting and removed data not
fitting their conclusion.
The red
curves, representing the best -
fit are all over the place, and they differ from one sequence to the next, although they are all part of the
same original time series.
Even if you detrend the dCO2 - temperature
curve (that means zero contribution to the trend), you will find the
same fit.
A simple
curve fit of data taken for the
same month of each year reveals beautiful exponential
curves with R ^ 2 values over 0.99.
If both have to be estimated from the
same time series data (with or without «optimal» smoothing), then you have «
curve fitting».
I would say the
same regarding the
curve -
fitting of Liu et al cited by Gail Combs above.
(Actually he's only using 11 coefficients because if you scale Amp1 by x and divide Scale1 through Scale5 by x you get back the
same curve with the
same fit, i.e. the coefficients are not linearly independent.
On the
same graph, I have a shown a normal
curve,
fit to the data.
It turns out that we can give answers to all of these questions, using the
same Hubbert linearizations and normal
curve fits that we use for oil.
However, although its simple linear regression analysis facilities (including polynomials) provides automatically the option for plotting the
fit with CIs for the
fitted line /
curve and for future observations from the
same population, I am unsure about these intervals for autocorrelated data — typically time series.
Following the time - honored principle that if a moron can find the
same result then it is nothing special and likely wrong, I am attempting my own SSN
curve fitting.
I believe the
same source told me that a metric buttload could be substituted by
fitting it to the metric crap - tonne
curve, but you either have to hard code in a correction factor (standard practice is to do so without comment), or you have to truncate the metric buttload plot at the end year of the
curve fitting period to avoid the divergence problem.
They seem to be in essence
curve fits of some variation on a random walk, tweaked to have an overall upward bias and to manage to match some historical data in some ways (or as some studies suggest, tweaked to match each other even more than being tweaked to match reality), with the
same question that any
curve fit therefore has as to whether there is any reason to believe it matches the real world process generating the data.
It makes little difference what the arbitrary function is — it is simply
fitting a
curve and they all have the
same shape.
I would suggest that the appropriate way to do that would be to start with the
same periods 1850 - 1950 and 1950 - 2010, do the
same curve -
fitting, and then calculate the whole 1650 - 2011 period and see how it compares.
It's got the
same curved 1.5 - inch Super AMOLED screen as the original Gear
Fit fitness tracker.
Otherwise, the Note 8's
fit and finish are exactly the
same as the S8, with glass panels that elegantly
curve into a metal frame.
Here the Gear 2 Neo wins out — it is about the
same width, but taller than the G Watch, and it has a
curve to its back design that allows it to
fit on your wrist more comfortably.
The
curved screen could allow for some functionality activated using the edges, but the main benefit is likely the
same as on the S7 Edge and Note 7;
fitting a large screen into a small body.
But we're pretty certain it's going to cost $ 200 stateside, with the
same exact
curved 1.5 - inch Super AMOLED display as the non-Pro Gear
Fit 2 from last year.