Summary of regression analyses with BMI Z score and sum of skinfolds as dependent variables showing regression coefficients (95 % CI)
from simple regression analysis and multiple regression after backward elimination of nonsignificant variables
The predicted September sea ice area in the East Siberian and Laptev Seas,
from a simple regression model using summer (Aug - Sep - Oct) sea surface temperatures in the North Atlantic as the predictor, is below normal but greater than in 2009.
Not exact matches
Of course Eichengreen knows far more about the gold standard than most economists, and is far
from being its harshest critic, so he'd undoubtedly be an outlier in the
simple regression, y = α + β (x)(where y is vehemence of criticism of the gold standard and x is ignorance of the subject).
With the Chart tool, you can draw charts with frequencies
from 5 - minute to monthly, add trend lines, and compare the price chart with such indicators as
Simple Moving Average or Linear
Regression Line, etc..
I wonder about two things: 1 / how much the resulting red curve differs
from simple degree - 2 polynomial
regression of the data 2 / what the result of this analysis would be if applied to periods 1910 - today or 1970 - today
In the end, I suspect that the F&R method and it's decedents are no more valid methods for estimating trend than
simple OLS
from linear
regression.
1.5 C Projections:
From my
simple «CO > Temp» best - fit
regression model (based on NASA temp set), I believe the equilibrium temperature will hit 1.5 C in 2025 (based on a baseline of 1955, and 2.5 ppm annual rise of CO2), and has already hit 1.5 C in 2017 if based on a baseline of 1880 - 1900 (adding 0.24 C to the 1955 baseline).
Hamilton, 4.0 + / - 0.3, Statistical A
simple regression model for NSIDC mean September extent as a function of mean daily sea ice area
from August 1 to 5, 2012 (and a quadratic function of time) predicts a mean September 2012 extent of 4.02 million km2, with a confidence interval of plus or minus.32.
The data is annual data
from 1955 to 1995, with a mean of 23.025 C, a Standard Deviation of 0.2981 C, and a trend of 0.05 + / - 0.08 C / 10 years, as determined by
simple linear
regression.
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.
After detrending the proportions using a
simple regression on year, the ACF shows no correlations are significantly different
from zero.
As shown in Figure 1,
simple slopes tests of the plotted
regression lines revealed diabetes conflict with mothers was associated with poorer adherence for Caucasians, but the slope was not significantly different
from zero for the Latinos.