Notes on linear
regression analysis (pdf file)

Introduction
to linear regression analysis

Mathematics
of simple regression

Regression examples

·
Beer sales vs. price, part 1: descriptive
analysis

·
Beer sales vs. price, part 2: fitting a simple
model

·
Beer sales vs. price, part 3: transformations
of variables

·
Beer sales vs.
price, part 4: additional predictors

·
NC natural gas consumption vs. temperature

What to look for in
regression output

What’s a good
value for R-squared?

What's the bottom
line? How to compare models

Testing the assumptions of linear regression

Additional notes on regression analysis

Stepwise and all-possible-regressions

Excel file with
simple regression formulas

Excel file with regression formulas
in matrix form

If you are a PC Excel user, you *must* check this out:

RegressIt: free Excel add-in for
linear regression and multivariate data analysis

**What's the bottom
line? How to compare models**

After
fitting a number of different regression or time series forecasting models to a
given data set, you have many criteria by which they can be compared:

**Error measures in the estimation period:***root mean squared*error, mean*absolute*error, mean absolute*percentage*error, mean absolute*scaled*error,*mean*error, mean*percentage*error**Error measures in the validation period (if you have done out-of-sample testing):**Ditto**Residual diagnostics and goodness-of-fit tests:**plots of actual and predicted values; plots of residuals versus time, versus predicted values, and versus other variables; residual autocorrelation plots, cross-correlation plots, and tests for normally distributed errors; measures of extreme or influential observations; tests for excessive runs, changes in mean, or changes in variance (lots of things that can be "OK" or "not OK")**Qualitative considerations:**intuitive reasonableness of the model, simplicity of the model, and above all,*usefulness for decision making!*

With so
many plots and statistics and considerations to worry about, it's sometimes
hard to know which comparisons are most important. What's the real bottom line?

If there
is any one statistic that normally takes precedence over the others, it is the **root
mean squared error (RMSE)**, which is
the square root of the mean squared error. When it is adjusted for the
degrees of freedom for error (sample size minus number of model coefficients),
it is known as **the standard error of the
regression** or **standard error of the estimate** in regression analysis
or as the **estimated white noise standard deviation** in ARIMA analysis.
This is the statistic whose value is minimized during the parameter estimation
process, and it is the statistic that determines the width of the confidence
intervals for predictions. It is a** lower
bound on the standard deviation of the forecast error** (a tight lower bound
if the sample is large and values of the independent variables are not
extreme), so a 95% confidence interval for a forecast is approximately equal to
the point forecast "plus or minus 2 standard errors"--i.e., plus or
minus 2 times the standard error of the regression.

However,
there are a number of other error measures by which to compare the performance
of models in absolute or relative terms:

- The
**mean absolute error (MAE)**is also measured in the same units as the data, and is usually similar in magnitude to, but slightly smaller than, the root mean squared error. It is less sensitive to the occasional very large error because it does not square the errors in the calculation. The mathematically challenged usually find this an easier statistic to understand than the RMSE. MAE and MAPE (below) are not a part of standard regression output, however. They are more commonly found in the output of time series forecasting procedures, such as the one in Statgraphics. It is relatively easy to compute them in RegressIt: just choose the option to save the residual table to the worksheet, create a column of formulas next to it to calculate errors in absolute or absolute-percentage terms, and apply the AVERAGE function.

- The
**mean absolute**is also often useful for purposes of reporting, because it is expressed in generic percentage terms which will make some kind of sense even to someone who has no idea what constitutes a "big" error in terms of dollars spent or widgets sold. The MAPE can only be computed with respect to data that are guaranteed to be strictly positive, so if this statistic is missing from your output where you would normally expect to see it, it’s possible that it has been suppressed due to negative data values.*percentage*error (MAPE)

- The
**mean absolute**is another relative measure of error that is applicable only to time series data. It is defined as the mean absolute error of the model divided by the mean absolute error of a naïve random-walk-without-drift model (i.e., the mean absolute value of the first difference of the series). Thus, it measures the relative reduction in error compared to a naive model. Ideally its value will be significantly less than 1. This statistic, which was proposed by Rob Hyndman in 2006, is very good to look at when fitting regression models to nonseasonal time series data. It is possible for a time series regression model to have an impressive R-squared and yet be inferior to a naïve model, as was demonstrated in the what’s-a-good-value-for-R-squared notes. If the series has a strong seasonal pattern, the corresponding statistic to look at would be the mean absolute error divided by the mean absolute value of the seasonal difference (i.e., the mean absolute error of a naïve seasonal model that predicts that the value in a given period will equal the value observed one season ago).*scaled*error (MASE)

- The
and*mean error*(ME)**mean percentage error (MPE)**that are reported in some statistical procedures are*signed*measures of error which indicate whether the forecasts are*biased*--i.e., whether they tend to be disproportionately positive or negative. Bias is normally considered a bad thing, but it is not the bottom line. Bias is one component of the mean squared error--in fact**mean squared error equals the variance of the errors plus the square of the mean error.**That is:**MSE = VAR(E) + (ME)^2**. Hence, if you try to minimize mean squared error, you are implicitly minimizing the bias as well as the variance of the errors.

- In
a model that includes a
*constant*term, the mean squared error will be minimized when the mean error is*exactly zero*, so you should expect the mean error to always be zero within the estimation period in a model that includes a constant term. (Note: as reported in the Statgraphics Forecasting procedure, the mean error in the estimation period may be slightly different from zero if the model included a log transformation as an option, because the forecasts and errors are automatically unlogged before the statistics are computed--see below.) (Return to top of page)

**The root mean
squared error is more sensitive than other measures to the occasional large
error:** the squaring process gives
disproportionate weight to very large errors. If an occasional large error is not
a problem in your decision situation (e.g., if the true cost of an error is
roughly proportional to the size of the error, not the square of the error),
then the MAE or MAPE may be a more relevant criterion. In many cases these
statistics will vary in unison--the model that is best on one of them will also
be better on the others--but this may not be the case when the error
distribution has outliers. If one model is best on one measure and another is
best on another measure, they are probably pretty similar in terms of their
average errors. In such cases you probably should give more weight to some of
the other criteria for comparing models--e.g., simplicity, intuitive
reasonableness, etc.

**The root mean
squared error and mean absolute error can only be compared between models whose
errors are measured in the same units**
(e.g., dollars, or constant dollars, or cases of beer sold, or whatever). If
one model's errors are adjusted for inflation while those of another or not, or
if one model's errors are in absolute units while another's are in logged
units, their error measures cannot be directly compared. In such cases, you
have to convert the errors of both models into comparable units before
computing the various measures. This means converting the forecasts of one
model to the same units as those of the other by unlogging or undeflating (or
whatever), then subtracting those forecasts from actual values to obtain errors
in comparable units, then computing statistics of those errors. You

In Statgraphics, the user-specified forecasting procedure
will take care of the latter sort of calculations for you: the forecasts and
their errors are automatically converted back into the original units of the
input variable (i.e., all transformations performed as model options within the
forecasting procedure are reversed) before computing the statistics shown in
the Analysis Summary report and Model Comparison report. However, other
procedures in Statgraphics (and most other stat programs) do not make life this
easy for you. (Return to top of page)

**There is no
absolute criterion for a "good" value of RMSE or MAE: **it depends on the units in which the variable is measured
and on the degree of forecasting accuracy, as measured in those units, which is
sought in a particular application. Depending on the choice of units, the RMSE
or MAE of your best model could be measured in zillions or one-zillionths. It
makes no sense to say "the model is good (bad) because the root mean squared
error is less (greater) than x", unless you are referring to a specific
degree of accuracy that is relevant to your forecasting application.

**There is no
absolute standard for a "good" value of adjusted R-squared. **Again,
it depends on the situation, in particular, on the "signal-to-noise
ratio" in the dependent variable. (Sometimes much of the signal can be
explained away by an appropriate data transformation, before fitting a
regression model.) When comparing
regression models that use the *same*
dependent variable and the *same *estimation
period, the *standard error of the
regression goes down as adjusted
R-squared goes up.* Hence, the model with the highest adjusted
R-squared will have the lowest standard error of the regression, and you can
just as well use adjusted R-squared as a criterion for ranking them. However,
when comparing regression models in which the dependent variables were
transformed in different ways (e.g., differenced in one case and undifferenced
in another, or logged in one case and unlogged in another), or which used
different sets of observations as the estimation period, R-squared is not a
reliable guide to model quality. (See the notes on "What's
a good value for R-squared?")

**Don't split
hairs: a model with an RMSE of 3.25 is not significantly better than one with
an RMSE of 3.32.** Remember that the width of the
confidence intervals is proportional to the RMSE, and ask yourself how much of
a relative decrease in the width of the confidence intervals would be
noticeable on a plot. It may be useful to think of this in percentage terms: if
one model's RMSE is 30% lower than another's, that is probably very
significant. If it is 10% lower, that is probably somewhat significant. If it
is only 2% better, that is probably not significant. These distinctions are
especially important when you are trading off model complexity against the
error measures: it is probably not worth adding another independent variable to
a regression model to decrease the RMSE by only a few more percent.

The RMSE and adjusted R-squared statistics already include a
minor adjustment for the number of coefficients estimated in order to make them
"unbiased estimators", but a heavier penalty on model complexity really
ought to be imposed for purposes of selecting among models. Sophisticated
software for automatic model selection generally seeks to minimize error
measures which impose such a heavier penalty, such as the **Mallows Cp statistic**, the **Akaike
Information Criterion (AIC)** or **Schwarz'
Bayesian Information Criterion (BIC).** How these are computed is beyond the
scope of the current discussion, but suffice it to say that when you--rather
than the computer--are selecting among models, you should show some preference
for the model with fewer parameters, other things being approximately equal.

The root mean squared error is a valid indicator of relative
model quality **only if it can be
trusted**. If there is evidence that the model is badly mis-specified
(i.e., if it *grossly* fails the diagnostic tests of its underlying
assumptions) or that the data in the estimation period has been* over-fitted*
(i.e., if the model has a relatively large number of parameters for the number
of observations fitted and its comparative performance deteriorates badly in
the validation period), then the root mean squared error *and all other error
measures* in the estimation period may need to be heavily discounted.

If there is evidence only of *minor* mis-specification of
the model--e.g., modest amounts of autocorrelation in the residuals--this does
not completely invalidate the model or its error statistics. Rather, it only
suggests that some fine-tuning of the model is still possible. For example, it
may indicate that another lagged variable could be profitably added to a
regression or ARIMA model. (Return to top of page)

In trying to ascertain whether the error measures in the
estimation period are reliable, you should consider whether the model under
consideration is *likely* to have overfitted the data. Are its assumptions
intuitively reasonable? Would it be easy or hard to explain this model to
someone else? Do the forecast plots look like a reasonable extrapolation
of the past data? If the assumptions seem reasonable, then it is more likely
that the error statistics can be trusted than if the assumptions were
questionable.

If the model has only one or two parameters (such as a
random walk, exponential smoothing, or simple regression model) and was fitted
to a moderate or large sample of time series data (say, 30 observations or
more), then it is probably unlikely to have overfitted the data. But if it has
many parameters relative to the number of observations in the estimation
period, then overfitting is a distinct possibility. Regression models which are
chosen by applying **automatic model-selection techniques**
(e.g., stepwise or all-possible regressions) to large numbers of uncritically chosen candidate variables* *are
prone to overfit the data, even if the number of regressors in the final model
is small.

As a rough guide against overfitting, calculate the **number of data points in the estimation
period per coefficient estimated** (including seasonal indices if they have
been separately estimated from the same data). If you have less than 10 data points per coefficient estimated,
you should be alert to the possibility of overfitting. Think of it this
way: how large a sample of data would you want in order to estimate a
single parameter, namely the mean? Strictly speaking, the determination
of an adequate sample size ought to depend on the signal-to-noise ratio in the
data, the nature of the decision or inference problem to be solved, and a
priori knowledge of whether the model specification is correct. There are also
efficiencies to be gained when estimating multiple coefficients simultaneously
from the same data. However,
thinking in terms of data points per coefficient is still a useful reality
check, particularly when the sample size is small and the signal is weak. (Return to
top of page)

When fitting regression models to **seasonal time series data** and using dummy variables to estimate
monthly or quarterly effects, you may have little choice about the number of
parameters the model ought to include.
You must estimate the seasonal pattern in some fashion, no matter how
small the sample, and you should always include the full set, i.e., *don’t *selectively remove seasonal
dummies whose coefficients are not significantly different from zero. As a general rule, it is good to have **at least 4 seasons’ worth of data**. More would be better but long time
histories may not be available or sufficiently relevant to what is happening
now, and using a group of seasonal dummy variables as a unit does not carry the
same kind of risk of overfitting as using a similar number of regressors that
are random variables chosen from a large pool of candidates. If it is logical for the series to have
a seasonal pattern, then there is no question of the relevance of the variables
that measure it.

If you have seasonally adjusted the data based on its own
history, prior to fitting a regression model, you should **count the seasonal indices as additional parameters**, similar in
principle to dummy variables. If
you have few years of data with which to work, there will inevitably be some
amount of overfitting in this process.
ARIMA models appear at first glance to require relatively few parameters
to fit seasonal patterns, but this is somewhat misleading. In order to *initialize*
a seasonal ARIMA model, it is necessary to estimate the seasonal pattern that
occurred in "year 0," which is comparable to the problem of
estimating a full set of seasonal indices. Indeed, it is usually claimed that
more seasons of data are required to fit a seasonal ARIMA model than to fit a
seasonal decomposition model.

Although the confidence intervals for one-step-ahead
forecasts are based almost entirely on RMSE, the **confidence intervals for the longer-horizon forecasts that can be
produced by time-series models depend heavily on the underlying modeling
assumptions,** particularly assumptions about the variability of the trend.
The confidence intervals for some models widen relatively slowly as the
forecast horizon is lengthened (e.g., simple exponential smoothing models with
small values of "alpha", simple moving averages, seasonal random walk
models, and linear trend models). The confidence intervals widen much faster
for other kinds of models (e.g., nonseasonal random walk models, seasonal
random trend models, or linear exponential smoothing models). The rate at which
the confidence intervals widen is not a reliable guide to model quality: what
is important is the model should be making the* correct* assumptions about
how uncertain the future is. It is very important that the model should pass
the various residual diagnostic tests and "eyeball" tests in order
for the confidence intervals for longer-horizon forecasts to be taken
seriously. (Return to top of page)

If you have had the opportunity to do **out-of-sample testing **of the model
(“cross-validation”), then the error measures in the* validation
period* are also very important.
In theory the model's performance in the validation period is the best
guide to its ability to predict the future. The caveat here is the validation
period is often a much* smaller sample of data* than the estimation
period. Hence, it is possible that a model may do unusually well or badly in
the validation period merely by virtue of getting lucky or unlucky--e.g., by
making the right guess about an unforeseeable upturn or downturn in the near
future, or by being less sensitive than other models to an unusual event that
happens at the start of the validation period.

Unless you have enough data to hold out a large and
representative sample for validation, it is probably better to interpret the
validation period statistics in a more qualitative way: do they wave a
"red flag" concerning the possible unreliability of statistics in the
estimation period, or not?

The comparative error statistics that Statgraphics reports
for the estimation and validation periods are in *original, untransformed
units*. If you used a log transformation as a model option in order to
reduce heteroscedasticity in the residuals, you should expect the unlogged
errors in the validation period to be much larger than those in the estimation
period. Of course, you can still compare validation-period statistics across
models in this case. (Return to top of page)

So... the
bottom line is that you should put the most weight on the **error measures in
the estimation period**--most often the RMSE (or standard error of the
regression, which is RMSE adjusted for the relative complexity of the model),
but sometimes MAE or MAPE--when comparing among models. The MASE statistic
provides a very useful reality check for a model fitted to time series data: is
it any better than a naive model? If your software is capable of computing
them, you may also want to look at Cp, AIC or BIC, which more heavily penalize
model complexity. But you should keep an eye on the residual diagnostic tests,
cross-validation tests (if available), and qualitative considerations such as
the intuitive reasonableness and simplicity of your model.

The residual
diagnostic tests are not the bottom line--you should never choose Model A over
Model B merely because model A got more "OK's" on its residual tests.
(What would you rather have: smaller errors or more random-looking errors?) A
model which fails some of the residual tests or reality checks in only a *minor*
way is probably subject to further improvement, whereas it is the model which
flunks such tests in a *major* way that cannot be trusted.

The
validation-period results are not necessarily the last word either, because of
the issue of sample size: if Model A is slightly better in a validation period
of size 10 while Model B is *much* better over an estimation period of
size 40, I would study the data closely to try to ascertain whether Model A
merely "got lucky" in the validation period.

Finally,
remember to **K.I.S.S.** (keep it simple...) If two models are generally
similar in terms of their error statistics and other diagnostics, you should
prefer the one that is simpler and/or easier to understand. The simpler model
is likely to be closer to the truth, and it will usually be more easily
accepted by others. (Return to top of page)

Go on to
next topic: Testing the assumptions
of linear regression