EViews instructions
These instructions accompany Applied Regression Modeling by Iain Pardoe, 2nd edition
published by Wiley in 2012. The numbered items crossreference with the "computer help" references
in the book. These instructions are based on EViews 7 for Windows, but they (or something similar)
should also work for other versions. Find instructions for other statistical software packages
here.
Getting started and summarizing univariate data
 If desired, change EViews' default options by selecting
Options > General Options.
 To open an EViews data file, select File > Open > EViews Workfile.
You can also open other files, such as Excel spreadsheets or SPSS data files.
 EViews does not appear to offer a way to recall a previously used dialog
box.
 Output appears in separate windows, from where it can be copied and pasted
to a word processor like OpenOffice Writer or Microsoft Word.
 You can access help by selecting Help > EViews Help Topics. For example, to
find out about "boxplots" click the Index tab, type boxplots in the first box, and
select the index entry you want in the second box.
 To transform data or compute a new variable, select
Quick > Generate Series. Type a name (with no spaces) for the new variable in the
Enter equation box, then =, then type a mathematical expression for the variable.
Examples are logx=log(x) for the natural logarithm of X and xsq=x^2 for
x^{2}. Click OK to create the new variable, which will be added to the dataset
(check it looks correct in the Workfile); it can now be used just like any other
variable. If you get an error message, this probably means that there is a syntax
error in your equation—a common mistake is to forget the multiplication
symbol (*) between a number and a variable (e.g., 2*x represents 2x).
 To create indicator (dummy) variables from a qualitative variable, select
Quick > Generate Series. Type, for example, [email protected](x="level", 1, 0),where
x is the qualitative variable and level is the name of one of the categories in
x. Repeat for other indicator variables (if necessary).

 To find a percentile (critical value) for a tdistribution, type
@qtdist(p,df) into the Command window, where p is the lowertail area
(i.e., one minus the onetail significance level) and df is the degrees of freedom. Press
the return/enter key to see the result at the bottom of the screen. For example,
@qtdist(.95,29) returns the 95th percentile of the tdistribution with 29 degrees of
freedom (1.699), which is the critical value for an uppertail test with a 5% significance level. By
contrast, @qtdist(.975,29) returns the 97.5th percentile of the tdistribution with 29
degrees of freedom (2.045), which is the critical value for a twotail test with a 5% significance
level.
 To find a percentile (critical value) for an Fdistribution, type
@qfdist(p,df1,df2) into the Command window, where p is the lowertail
area (i.e., one minus the significance level), df1 is the numerator degrees of freedom, and
df2 is the denominator degrees of freedom. For example, @qfdist(0.95,2,3)
returns the 95th percentile of the Fdistribution with 2 numerator degrees of freedom and 3
denominator degrees of freedom (9.552).
 To find a percentile (critical value) for a chisquared distribution,
type @qchisq(p,df) into the Command window, where p is the lowertail area (i.e., one minus the
significance level) and df is the degrees of freedom. For example,
@qchisq(0.95,2) returns the 95th percentile of the chisquared distribution with 2
degrees of freedom (5.991).

 To find an uppertail area (onetail pvalue) for a tdistribution, type
[email protected](t,df) into the Command window, where t is the value of the
tstatistic and df is the degrees of freedom. For example, [email protected](2.40,29)
returns the uppertail area for a tstatistic of 2.40 from the tdistribution with 29 degrees of
freedom (0.012), which is the pvalue for an uppertail test. By contrast,
=2*([email protected](2.40,29)) returns the twotail area for a tstatistic of 2.40 from the
tdistribution with 29 degrees of freedom (0.023), which is the pvalue for a twotail test.
 To find an uppertail area (pvalue) for an Fdistribution, type
[email protected](f,df1,df2) into the Command window, where f is the value of
the Fstatistic, df1 is the numerator degrees of freedom, and df2 is the
denominator degrees of freedom. For example, [email protected](51.4,2,3) returns the uppertail
area (pvalue) for an Fstatistic of 51.4 for the Fdistribution with 2 numerator degrees of freedom
and 3 denominator degrees of freedom (0.005).
 To find an uppertail area (pvalue) for a chisquared distribution, type
[email protected](chisq,df) into the Command window, where chisq is the value of
the chisquared statistic and df is the degrees of freedom. For example,
[email protected](0.38,2) returns the uppertail area (pvalue) for a chisquared statistic of
0.38 for the chisquared distribution with 2 degrees of freedom (0.827).
 Calculate descriptive statistics for a quantitative variable by selecting
Quick > Show. Type the name of the quantitative
variable into the Objects to display in a single window box and click OK.
Click View > Descriptive Statistics & Tests > Stats Table and click OK to
display the results.
 Create contingency tables or crosstabulations for qualitative
variables by selecting Quick > Show. Type the names of two qualitative
variables separated by spaces into the Objects to display in a single window box and click OK.
Click View > NWay Tabulation and click OK to display the table. Cell
percentages (within rows, columns, or the whole table) can be calculated by selecting the
appropriate options in the Crosstabulation dialog box.
 If you have a quantitative variable and a qualitative variable, you can calculate
descriptive statistics for cases grouped in different categories by selecting
Quick > Show. Type the name of the quantitative
variable into the Objects to display in a single window box and click OK.
Click View > Descriptive Statistics & Tests > Stats by Classification and click
OK. Type the name of the qualitative variable that defines the categories into the
Series/Group for classify box, select the statistics to display, and click OK to
display the results.
 EViews does not appear to offer a way to create a stemandleaf plot for a
quantitative variable
 To make a histogram for a quantitative variable, select
Quick > Show. Type the name of the quantitative variable into the
Objects to display in a single window box and click OK. Click
View > Descriptive Statistics & Tests > Histogram and Stats and click OK.
 To make a scatterplot with two quantitative variables, select
Quick > Show. Type the name of the horizontal axis variable followed by a space and then
the name of the vertical axis variable into the Objects to display in a single window box
and click OK. Click View > Graph, select Basic graph for the
General graph type, select Scatter for the Specific Graph type and click
OK.
 All possible scatterplots for more than two variables can be drawn simultaneously
(called a scatterplot matrix}) by selecting Quick > Show. Type the names of the
variables separated by spaces into the Objects to display in a single window box
and click OK. Click View > Graph, select Basic graph for the
General graph type, select Scatter for the Specific Graph type, then
select Scatterplot matrix for Multiple series and click OK.
 You can mark or label cases in a scatterplot with different colors/symbols
according to categories in a qualitative variable by following Help #15, but selecting
Categorical graph for the General Graph type and typing the name of the
qualitative variable into the Within graph box under
Factors  series defining categories. Click OK to
display the graph.
 You can identify individual cases in a scatterplot by hovering over
individual points.
 To remove one of more observations from a dataset, select
Quick > Sample and type appropriate values into the Sample range pairs box.
For example, type 1 9 11 100 to remove observation #10 from a dataset containing 100
observations. To return the dataset to its original state type @all into the Sample
range pairs box.
 To make a bar chart for cases in different categories, select
Quick > Show. Type the name of a quantitative variable into the
Objects to display in a single window box and click OK. Click
View > Graph, select Categorical graph for the General graph type, and
select Bar for the Specific Graph type.
 For frequency bar charts of one qualitative variable, type the name of the
qualitative variable into the Within graph box under
Factors  series defining categories and choose Numbers of observations for
Graph data. Click OK to
display the graph.
 For frequency bar charts of two qualitative variables, type the names of the
qualitative variables separated by a space into the Within graph box under
Factors  series defining categories and choose Numbers of observations for
Graph data. Click OK to
display the graph.
 The bars can also represent various summary functions for the quantitative variable.
For example, to produce a bar chart of means select Means for Graph data. Click OK to
display the graph.
 To make boxplots for cases in different categories, select
Quick > Show. Type the name of the quantitative variable into the
Objects to display in a single window box and click OK. Click
View > Graph, select Categorical graph for the General graph type, and
select Boxplot for the Specific Graph type.
 For just one qualitative variable, type the name of the
qualitative variable into the Within graph box under
Factors  series defining categories. Click OK to
display the graph.
 For two qualitative variables, type the names of the
qualitative variables separated by a space into the Within graph box under
Factors  series defining categories. Click OK to
display the graph.
 To make a QQplot (also known as a normal probability plot) for a
quantitative variable, Quick > Show. Type the name of the quantitative variable into the
Objects to display in a single window box and click OK. Click
View > Graph, select Basic graph for the General graph type, and
select Quantile  Quantile for the Specific Graph type. Click OK to
display the graph.
 EViews does not appear to offer an automatic way to compute a
confidence interval for a univariate population mean. It is possible to calculate such an
interval by hand using EViews output.
 To do a hypothesis test for a univariate population mean, select
Quick > Show. Type the name of the quantitative variable into the
Objects to display in a single window box and click OK. Click
View > Descriptive Statistics & Tests > Simple Hypothesis Tests, type the (null)
hypothesized value into the Mean box, and click OK to display the results.
The pvalue calculated (displayed as "Probability") is a twotailed pvalue; to
obtain a onetailed pvalue you will either need to divide this value by two or subtract it from one
and then divide by two (draw a picture to figure out which).
Simple linear regression
 To fit a simple linear regression model (i.e., find a least squares line),
select Quick > Estimate Equation. Type, for example, y c x into the
Equation specification box, where y is the name of the response variable,
c stands for the "constant" that represents the intercept term, and x is the name
of the predictor variable. Click OK to see the results. In the rare circumstance that you
wish to fit a model without an intercept term (regression through the origin), omit the c
in the Equation specification.
 To add a regression line or least squares line to a scatterplot,
follow Help #15, but select Regression Line for Fit lines in the
Graph Options dialog box.
 To find confidence intervals for the regression parameters in a simple
linear regression model, follow Help #25 (or #31), then select
View > Coefficient Diagnostics > Confidence Intervals. Default values for the confidence
levels are 90%, 95%, and 99%, but you can change this if you want. This applies more generally
to multiple linear regression also.
 To find a fitted value or predicted value of Y (the response
variable) at each value of X (the predictor variable) in the dataset, follow Help #25 (or #31), then
select View > Actual,Fitted,Residual. The fitted or predicted values of Y at each of the
Xvalues in the dataset are displayed in the column headed Fitted.
This applies more generally to multiple linear regression also.
 EViews does not appear to offer an automatic way to find a
confidence interval for the mean of Y at each value of X in the dataset. It is possible to
calculate such intervals by hand using EViews output. This applies more generally to
multiple linear regression also.
 To find a prediction interval for an individual value of Y at each value of
X in the dataset, follow Help #25 (or #31) then select Proc > Forecast. Type names into
the Forecast name and S.E. (optional) boxes and click OK. The forecasts
(fitted or predicted values) and S.E.'s (prediction standard errors) will appear under these names
in the Workfile. The prediction intervals for an individual Yvalue at each of the
Xvalues in the dataset can then be calculated by hand using these values and appropriate
tpercentiles. This applies more generally to multiple linear regression also.
Multiple linear regression
 To fit a multiple linear regression model, select
select Quick > Estimate Equation. Type, for example, y c x1 x2 into the
Equation specification box, where y is the name of the response variable,
c stands for the "constant" that represents the intercept term, and x1 and
x2 are the names of the predictor variables. Click OK to see the results. In the
rare circumstance that you wish to fit a model without an intercept term (regression through the
origin), omit the c in the Equation specification.
 To add a quadratic regression line to a scatterplot, follow Help #15, but
select Regression Line for Fit lines in the Graph Options dialog box,
then click Options and select a Polynomial of order 2 under
X transformations.
 Categories of a qualitative variable can be thought of as defining subsets
of the sample. If there is also a quantitative response and a quantitative predictor variable in
the dataset, a regression model can be fit to the data to represent separate regression lines for
each subset. First use help #15 and #17 to make a scatterplot with the response variable on the
vertical axis, the quantitative predictor variable on the horizontal axis, and the cases marked with
different colors/symbols according to the categories in the qualitative predictor variable. To add
a regression line for each subset to this scatterplot, select Regression Line for
Fit lines in the Graph Options dialog box.
 EViews does not appear to offer an automatic way to find the Fstatistic and
associated pvalue for a nested model Ftest in multiple linear regression. It is possible
to calculate these values by hand using EViews output.
 To save residuals in a multiple linear regression model, follow Help #31
and the residuals are saved by default as variable resid in the Workfile; they can
now be used just like any other variable, for example, to construct residual plots. EViews does
not appear to offer a way to automatically save what Pardoe (2012) calls
standardized residuals. To save what Pardoe (2012) calls studentized residuals,
follow Help #31, select View > Stability Diagnostics > Influence Statistics, check
RStudent, and type a name into the adjoining box to store them in the Workfile.
 EViews does not appear to offer an automatic way to add a loess fitted line
to a scatterplot (useful for checking the zero mean regression assumption in a residual plot).
However, it is possible to add a similar fitted line based on a "kernel fit" by following Help #15
but selecting Kernel Fit for Fit lines in the Graph Options dialog
box.
 To save leverages in a multiple linear regression model, follow Help #31,
select View > Stability Diagnostics > Influence Statistics, check
Hat Matrix, and type a name into the adjoining box to store them in the Workfile.
 EViews does not appear to offer an automatic way to save Cook's distances
in a multiple linear regression model.
 To create a histogram of residuals automatically in a multiple linear
regression model, follow Help #31 then select
View > Residual Diagnostics > Histogram  Normality Test. To create residual plots
manually, first create residuals (see help #35), and then construct scatterplots with these
residuals on the vertical axis.
 To create a correlation matrix of quantitative variables (useful for
checking potential multicollinearity problems), select Quick > Show. Type the names
of the quantitative variables separated by spaces into the
Objects to display in a single window box and click OK. Click
View > Covariance Analysis, deselect Covariance, select Correlation, and
click OK to display the matrix.
 To find variance inflation factors in multiple linear regression, follow
Help #31 then select View > Coefficient Diagnostics > Variance Inflation Factors. The
variance inflation factors are in the column labeled "Centered VIF."
 To draw a predictor effect plot for graphically displaying the effects of
transformed quantitative predictors and/or interactions between quantitative and qualitative
predictors in multiple linear regression, first create a variable representing the effect, say,
"x1effect" (see computer help #6). Then select Quick > Show, type x1 x1effect
into the Objects to display in a single window box, and click OK. Click
View > Graph, select Basic graph for the General graph type and select
Scatter for the Specific Graph type.
 If the "x1effect" variable just involves x1 (e.g., 1 + 3x1 + 4x1^{2}),
you can click OK at this point.
 If the "x1effect" variable also involves a qualitative variable (e.g.,
1 − 2x1 + 3d2x1, where d2 is an indicator variable), you should select
Categorical graph for the General Graph type and type the name of the
qualitative variable into the Within graph box under
Factors  series defining categories. Click OK to display the graph.
See Section 5.5 in Pardoe (2012) for an example. The instructions here create scatterplots rather
than line plots, but lines can be added to the plots with an appropriate choice of
Fit lines in the Graph Options dialog box.
Last updated: June, 2012
© 2012, Iain Pardoe