No, this is a cute problem, though: the definition of R^2 changes
without the intercept, because the
"empty" model used for calculating the total sums of squares is always
predicting 0 (so the total sums
of squares are sums of squares of the observations themselves, without
centering around the sample
mean).
Your interpretation of the p-value for the intercept in the first
model is also backwards: 0.9535 is extremely
weak evidence against the hypothesis that the intercept is 0. That
is, the intercept might be near zero, but
could also be something veru different. With a standard error of 229,
your 95% confidence interval
for the intercept (if you trusted it based on other things) would have
a margin of error of well over 400. If you
told me that an intercept of, say 350 or 400 were consistent with your
knowledge of the problem, I wouldn't
blink.
This is a very small data set: if you sent an R command such as:
x <- c(x1, x2, ..., xn)
y <- c(y1, y2, ..., yn)
you might even get some more interesting feedback. One of the many
good intro stats textbooks might
also be helpful as you get up to speed.
Jay
---------------------------------------------
Original post:
Message: 135
Date: Fri, 18 Feb 2011 11:49:41 +0100
From: Jan <jrheinlaen...@gmx.de>
To: "R-help@r-project.org list" <r-help@r-project.org>
Subject: [R] lm without intercept
Message-ID: <1298026181.2847.19.camel@jan-laptop>
Content-Type: text/plain; charset="UTF-8"
Hi,
I am not a statistics expert, so I have this question. A linear model
gives me the following summary:
Call:
lm(formula = N ~ N_alt)
Residuals:
Min 1Q Median 3Q Max
-110.30 -35.80 -22.77 38.07 122.76
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.5177 229.0764 0.059 0.9535
N_alt 0.2832 0.1501 1.886 0.0739 .
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Residual standard error: 56.77 on 20 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.151, Adjusted R-squared: 0.1086
F-statistic: 3.558 on 1 and 20 DF, p-value: 0.07386
The regression is not very good (high p-value, low R-squared).
The Pr value for the intercept seems to indicate that it is zero with a
very high probability (95.35%). So I repeat the regression forcing the
intercept to zero:
Call:
lm(formula = N ~ N_alt - 1)
Residuals:
Min 1Q Median 3Q Max
-110.11 -36.35 -22.13 38.59 123.23
Coefficients:
Estimate Std. Error t value Pr(>|t|)
N_alt 0.292046 0.007742 37.72 <2e-16 ***
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
Residual standard error: 55.41 on 21 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.9855, Adjusted R-squared: 0.9848
F-statistic: 1423 on 1 and 21 DF, p-value: < 2.2e-16
1. Is my interpretation correct?
2. Is it possible that just by forcing the intercept to become zero, a
bad regression becomes an extremely good one?
3. Why doesn't lm suggest a value of zero (or near zero) by itself if
the regression is so much better with it?
Please excuse my ignorance.
Jan Rheinl?nder
--
John W. Emerson (Jay)
Associate Professor of Statistics
Department of Statistics
Yale University
http://www.stat.yale.edu/~jay
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