As the name implies, multivariate regression is a technique that estimates a single regression model with multiple outcome variables and one or more predictor variables.

**Please Note:** The purpose of this page is to show how to use various data analysis commands.
It does not cover all aspects of the research process which researchers are expected to do. In
particular, it does not cover data cleaning and checking, verification of assumptions, model
diagnostics and potential follow-up analyses.

## Examples of multivariate regression analysis

Example 1.

A researcher has collected data on three psychological variables, four academic variables (standardized test scores), and the type of educational program the student is in for 600 high school students. She is interested in how the set of psychological variables relate to the academic variables and gender. In particular, the researcher is interested in how many dimensions are necessary to understand the association between the two sets of variables.

Example 2. A doctor has collected data on cholesterol, blood pressure and weight. She also collected data on the eating habits of the subjects (e.g., how many ounces of red meat, fish, dairy products, and chocolate consumed per week). She wants to investigate the relationship between the three measures of health and eating habits.

Example 3. A researcher is interested in determining what factors influence the health African Violet plants. She collects data on the average leaf diameter, the mass of the root ball, and the average diameter of the blooms, as well as how long the plant has been in the current container. For predictor variables, she measures several elements in the soil, in addition to the amount of light and water each plant receives.

## Description of the data

Let’s pursue Example 1 from above.

We have a hypothetical dataset, https://stats.idre.ucla.edu/wp-content/uploads/2016/02/mvreg.sas7bdat, with 600 observations on seven variables.
The psychological variables are **locus of control**, **self-concept** and
**motivation**. The academic variables are standardized tests scores in
**reading**, **writing**, and **science**, as well as a categorical
variable giving the type of program the student is in (general, academic, or
vocational). In our example the dataset **https://stats.idre.ucla.edu/wp-content/uploads/2016/02/mvreg.sas7bdat** is saved in a
library called **data**.

Let’s look at the data (note that there are no missing values in this data set).

proc means data = data.mvreg; vars locus_of_control self_concept motivation read write science; run;The MEANS Procedure Variable Label N Mean Std Dev Minimum Maximum ------------------------------------------------------------------------------------------------- LOCUS_OF_CONTROL 600 0.0965333 0.6702799 -1.9959567 2.2055113 SELF_CONCEPT 600 0.0049167 0.7055125 -2.5327499 2.0935633 MOTIVATION 600 0.0038979 0.8224000 -2.7466691 2.5837522 READ 600 51.9018333 10.1029831 24.6200066 80.5864944 WRITE 600 52.3848332 9.7264550 20.0688801 83.9348221 SCIENCE 600 51.7633331 9.7061791 21.9895325 80.3694153 -------------------------------------------------------------------------------------------------proc freq data = data.mvreg; table prog; run;The FREQ Procedure program type Cumulative Cumulative PROG Frequency Percent Frequency Percent --------------------------------------------------------- 1 138 23.00 138 23.00 2 271 45.17 409 68.17 3 191 31.83 600 100.00proc corr data = data.mvreg nosimple; var locus_of_control self_concept motivation; run;The CORR Procedure 3 Variables: LOCUS_OF_CONTROL SELF_CONCEPT MOTIVATION Pearson Correlation Coefficients, N = 600 Prob > |r| under H0: Rho=0 LOCUS_OF_ SELF_ CONTROL CONCEPT MOTIVATION LOCUS_OF_CONTROL 1.00000 0.17119 0.24513 <.0001 <.0001 SELF_CONCEPT 0.17119 1.00000 0.28857 <.0001 <.0001 MOTIVATION 0.24513 0.28857 1.00000 <.0001 <.0001proc corr data = data.mvreg nosimple; var read write science; run;The CORR Procedure 3 Variables: READ WRITE SCIENCE Pearson Correlation Coefficients, N = 600 Prob > |r| under H0: Rho=0 READ WRITE SCIENCE READ 1.00000 0.62859 0.69069 <.0001 <.0001 WRITE 0.62859 1.00000 0.56915 <.0001 <.0001 SCIENCE 0.69069 0.56915 1.00000 <.0001 <.0001

## Analysis methods you might consider

Below is a list of some analysis methods you may have encountered. Some of the methods listed are quite reasonable while others have either fallen out of favor or have limitations.

- Multivariate multiple regression, the focus of this page.
- Separate OLS Regressions - You could analyze these data using separate OLS regression analyses for each outcome variable. The individual coefficients, as well as their standard errors, will be the same as those produced by the multivariate regression. However, the OLS regressions will not produce multivariate results, nor will they allow for testing of coefficients across equations.
- Canonical correlation analysis might be feasible if don't want to consider one set of variables as outcome variables and the other set as predictor variables.

## Multivariate regression analysis

Technically speaking, we will be conducting a multivariate multiple regression. This regression is "multivariate" because there is more than one outcome variable. It is a "multiple" regression because there is more than one predictor variable. Of course, you can conduct a multivariate regression with only one predictor variable, although that is rare in practice.

To conduct a multivariate regression in SAS, you can use **proc glm**,
which is the same procedure that is often used to perform ANOVA or OLS
regression. The syntax for estimating a multivariate regression is similar
to running a model with a single outcome, the primary difference is the use of
the **manova** statement so that the output includes the
multivariate statistics. The f- and p-values for four
multivariate criterion are given, including Wilks' lambda, Lawley-Hotelling
trace, Pillai's trace, and Roy's largest root. By specifying **h=_ALL_** on
the **manova** statement, we indicate that we would like multivariate
statistics for all of the predictor variables in the model, if we were only
interested in the multivariate statistics for some variables, we could replace
**_ALL_** with the name of a variable (e.g. **h=read**).

proc glm data = data.mvreg; class prog; model locus_of_control self_concept motivation = read write science prog / solution ss3; manova h=_ALL_; run; quit;

The SAS output for multivariate regression can be very long, especially if
the model has many outcome variables. The output from our example has four
parts: one for each of the three outcome variables, and the fourth from the **manova** statement. Below we will discuss the
output in sections.

The GLM Procedure Class Level Information Class Levels Values PROG 3 1 2 3 Number of Observations Read 600 Number of Observations Used 600

Above we see that the class variable **prog** has three levels. Just below
the class level information, we see the number
of observations read form the data and the number of observations used in the analysis. If
the variables used in the analysis contained missing values the number of observations used
would be smaller than the number of observations read.

Dependent Variable: LOCUS_OF_CONTROL Sum of Source DF Squares Mean Square F Value Pr > F Model 5 50.2595509 10.0519102 27.28 <.0001 Error 594 218.8562365 0.3684448 Corrected Total 599 269.1157874 R-Square Coeff Var Root MSE LOCUS_OF_CONTROL Mean 0.186758 628.7948 0.606997 0.096533 Source DF Type III SS Mean Square F Value Pr > F READ 1 4.16815963 4.16815963 11.31 0.0008 WRITE 1 4.72524304 4.72524304 12.82 0.0004 SCIENCE 1 0.92248638 0.92248638 2.50 0.1141 PROG 2 5.02961991 2.51480995 6.83 0.0012

- The dependent variable,
**locus_of_control**, is listed at the top of the output above. - The ANOVA table for
**locus_of_control**gives the sum of squares and mean square for both the model and error term. The model for**locus_of_control**is statistically significant with a p-value of less than 0.0001. - Below the ANOVA table we see the R-square value of 0.187, indicating that 18.7% of variance in
**locus_of_control**is explained by the model. - The final table shown above gives the predictor variables in the model,
along with the type III sum of squares for each variable. We can see that
**read**,**write**, and**prog**are statistically significant. Note that because**prog**is a class variable with three levels, it uses 2 degrees of freedom (shown in the column labeled DF).

Standard Parameter Estimate Error t Value Pr > |t| Intercept -1.373094234 B 0.16259260 -8.44 <.0001 READ 0.012504619 0.00371779 3.36 0.0008 WRITE 0.012145048 0.00339136 3.58 0.0004 SCIENCE 0.005761477 0.00364116 1.58 0.1141 PROG 1 -0.251670509 B 0.06846988 -3.68 0.0003 PROG 2 -0.123875431 B 0.05760714 -2.15 0.0319 PROG 3 0.000000000 B . . . NOTE: The X'X matrix has been found to be singular, and a generalized inverse was used to solve the normal equations. Terms whose estimates are followed by the letter 'B' are not uniquely estimable.

- The table above gives the parameter estimates, their standard errors,
t-value, and associated p-value. The coefficients are interpreted in the
same manner as OLS regression coefficients. For example, a one unit increase in
**read**is associated with a 0.013 increase in the predicted value of**locus_of_control**. - The note shown above is SAS's way of telling us that it could not include the terms for all
three levels of
**prog**and the intercept in the model. Instead it has included the intercept and terms for**prog**=1 and**prog**=2, leaving**prog**=3 as the reference group.

The output for the first outcome variable (**locus_of_control**) is
followed by similar output for each additional outcome (**self_concept** and
**motivation**). This output is shown below, but we will not discuss it
further, instead we will move on to the multivariate output.

The GLM Procedure Dependent Variable: SELF_CONCEPT Sum of Source DF Squares Mean Square F Value Pr > F Model 5 16.1107053 3.2221411 6.79 <.0001 Error 594 282.0402900 0.4748153 Corrected Total 599 298.1509953 R-Square Coeff Var Root MSE SELF_CONCEPT Mean 0.054035 14014.91 0.689068 0.004917 Source DF Type III SS Mean Square F Value Pr > F READ 1 0.04557875 0.04557875 0.10 0.7568 WRITE 1 0.59051932 0.59051932 1.24 0.2652 SCIENCE 1 0.78237876 0.78237876 1.65 0.1998 PROG 2 14.21838537 7.10919268 14.97 <.0001 Standard Parameter Estimate Error t Value Pr > |t| Intercept 0.0510179965 B 0.18457670 0.28 0.7823 READ 0.0013076138 0.00422047 0.31 0.7568 WRITE -.0042934282 0.00384990 -1.12 0.2652 SCIENCE 0.0053059405 0.00413348 1.28 0.1998 PROG 1 -.4233591913 B 0.07772768 -5.45 <.0001 PROG 2 -.1468757972 B 0.06539618 -2.25 0.0251 PROG 3 0.0000000000 B . . . NOTE: The X'X matrix has been found to be singular, and a generalized inverse was used to solve the normal equations. Terms whose estimates are followed by the letter 'B' are not uniquely estimable. The GLM Procedure Dependent Variable: MOTIVATION Sum of Source DF Squares Mean Square F Value Pr > F Model 5 60.7672827 12.1534565 20.96 <.0001 Error 594 344.3614302 0.5797330 Corrected Total 599 405.1287128 R-Square Coeff Var Root MSE MOTIVATION Mean 0.149995 19533.65 0.761402 0.003898 Source DF Type III SS Mean Square F Value Pr > F READ 1 2.49445035 2.49445035 4.30 0.0385 WRITE 1 9.85052717 9.85052717 16.99 <.0001 SCIENCE 1 2.25173630 2.25173630 3.88 0.0492 PROG 2 30.18084209 15.09042104 26.03 <.0001 Standard Parameter Estimate Error t Value Pr > |t| Intercept -.6911458885 B 0.20395228 -3.39 0.0007 READ 0.0096735465 0.00466350 2.07 0.0385 WRITE 0.0175354486 0.00425404 4.12 <.0001 SCIENCE -.0090014528 0.00456739 -1.97 0.0492 PROG 1 -.6196960376 B 0.08588699 -7.22 <.0001 PROG 2 -.2593666472 B 0.07226102 -3.59 0.0004 PROG 3 0.0000000000 B . . . NOTE: The X'X matrix has been found to be singular, and a generalized inverse was used to solve the normal equations. Terms whose estimates are followed by the letter 'B' are not uniquely estimable.

The final section of output for our model is output for the multivariate tests of the model.

The GLM Procedure Multivariate Analysis of Variance Characteristic Roots and Vectors of: E Inverse * H, where H = Type III SSCP Matrix for READ E = Error SSCP Matrix Characteristic Characteristic Vector V'EV=1 Root Percent LOCUS_OF_CONTROL SELF_CONCEPT MOTIVATION 0.02414400 100.00 0.05725523 -0.00912678 0.02560444 0.00000000 0.00 -0.00704393 0.05979895 0.00102214 0.00000000 0.00 -0.03710958 -0.01295454 0.04972124 MANOVA Test Criteria and Exact F Statistics for the Hypothesis of No Overall READ Effect H = Type III SSCP Matrix for READ E = Error SSCP Matrix S=1 M=0.5 N=295 Statistic Value F Value Num DF Den DF Pr > F Wilks' Lambda 0.97642519 4.76 3 592 0.0027 Pillai's Trace 0.02357481 4.76 3 592 0.0027 Hotelling-Lawley Trace 0.02414400 4.76 3 592 0.0027 Roy's Greatest Root 0.02414400 4.76 3 592 0.0027

- The second table shown above gives the tests for the overall effect of
**read**. These results indicate that**read**is statistically significant regardless of what type of multivariate criteria is used (i.e., all of the p-values are less than 0.01).

SAS prints similar output for each of the
predictor variables in the model (in this case **write**, **science**,
and **prog**), this output is shown below, but we will not discuss it
further. Instead we will move on to additional tests.

Characteristic Roots and Vectors of: E Inverse * H, where H = Type III SSCP Matrix for WRITE E = Error SSCP Matrix Characteristic Characteristic Vector V'EV=1 Root Percent LOCUS_OF_CONTROL SELF_CONCEPT MOTIVATION 0.05552705 100.00 0.03976623 -0.02762931 0.04077279 0.00000000 0.00 0.00235865 0.05460081 0.01173502 0.00000000 0.00 0.05583890 0.00907776 -0.03645138 MANOVA Test Criteria and Exact F Statistics for the Hypothesis of No Overall WRITE Effect H = Type III SSCP Matrix for WRITE E = Error SSCP Matrix S=1 M=0.5 N=295 Statistic Value F Value Num DF Den DF Pr > F Wilks' Lambda 0.94739400 10.96 3 592 <.0001 Pillai's Trace 0.05260600 10.96 3 592 <.0001 Hotelling-Lawley Trace 0.05552705 10.96 3 592 <.0001 Roy's Greatest Root 0.05552705 10.96 3 592 <.0001 Multivariate Analysis of Variance Characteristic Roots and Vectors of: E Inverse * H, where H = Type III SSCP Matrix for SCIENCE E = Error SSCP Matrix Characteristic Characteristic Vector V'EV=1 Root Percent LOCUS_OF_CONTROL SELF_CONCEPT MOTIVATION 0.01687455 100.00 0.03609681 0.03206920 -0.04456052 0.00000000 0.00 -0.02316137 0.05234944 0.01603289 0.00000000 0.00 0.05353009 -0.00762467 0.02976812 MANOVA Test Criteria and Exact F Statistics for the Hypothesis of No Overall SCIENCE Effect H = Type III SSCP Matrix for SCIENCE E = Error SSCP Matrix S=1 M=0.5 N=295 Statistic Value F Value Num DF Den DF Pr > F Wilks' Lambda 0.98340548 3.33 3 592 0.0193 Pillai's Trace 0.01659452 3.33 3 592 0.0193 Hotelling-Lawley Trace 0.01687455 3.33 3 592 0.0193 Roy's Greatest Root 0.01687455 3.33 3 592 0.0193 Characteristic Roots and Vectors of: E Inverse * H, where H = Type III SSCP Matrix for PROG E = Error SSCP Matrix Characteristic Characteristic Vector V'EV=1 Root Percent LOCUS_OF_CONTROL SELF_CONCEPT MOTIVATION 0.12087752 99.34 0.01903925 0.02549291 0.03813193 0.00080748 0.66 0.04668032 -0.04866125 0.01435613 0.00000000 0.00 0.04651187 0.02844692 -0.03832351 Multivariate Analysis of Variance MANOVA Test Criteria and F Approximations for the Hypothesis of No Overall PROG Effect H = Type III SSCP Matrix for PROG E = Error SSCP Matrix S=2 M=0 N=295 Statistic Value F Value Num DF Den DF Pr > F Wilks' Lambda 0.89143832 11.67 6 1184 <.0001 Pillai's Trace 0.10864869 11.35 6 1186 <.0001 Hotelling-Lawley Trace 0.12168500 12.00 6 787.56 <.0001 Roy's Greatest Root 0.12087752 23.89 3 593 <.0001 NOTE: F Statistic for Roy's Greatest Root is an upper bound. NOTE: F Statistic for Wilks' Lambda is exact.

As mentioned above, if you ran a separate regression for each outcome variable, you would get exactly the same coefficients, standard errors, t- and p-values, and confidence intervals as shown above. So why conduct a multivariate regression? One of the advantages is that you can conduct tests of the coefficients across the different models. Below we show a few of the hypothesis tests you can perform.

For the first test, the null hypothesis is that the coefficient for **prog**=1
is equal to the coefficient for **prog**=2 for each dependent variable
separately. An alternative way to state this hypothesis is that the difference
between the two coefficients (i.e., **prog**=1 - **prog**=2) is equal to 0.
The **estimate** statement can be used to perform this test. The text between the apostrophes (i.e.,
' ) is a label for the output. Next we list the variable name (**prog**)
followed by a series of numbers, one for each level of **prog** in order,
these are the values by which the coefficients will be multiplied to perform the
test. To estimate the difference between the coefficient for **prog**=1
and **prog**=2 we multiply the coefficient for **prog**=1 by 1, and the
coefficient for **prog**=2 by -1, **prog**=3 is not involved in this test,
so we multiply it by 0.

proc glm data = data.mvreg; class prog; model locus_of_control self_concept motivation = read write science prog / solution ss3; manova h= _ALL_ ; estimate 'prog 1 vs. prog 2' prog 1 -1 0; run; quit;

The output produced by this model is similar to the output for the previous
model, except that it contains additional output associated with the use of the
**estimate** statement. To save space, we will only show the additional
output.

Dependent Variable: LOCUS_OF_CONTROL Standard Parameter Estimate Error t Value Pr > |t| prog 1 vs. prog 2 -0.12779508 0.06395501 -2.00 0.0462 Dependent Variable: SELF_CONCEPT Standard Parameter Estimate Error t Value Pr > |t| prog 1 vs. prog 2 -0.27648339 0.07260235 -3.81 0.0002 Dependent Variable: MOTIVATION Standard Parameter Estimate Error t Value Pr > |t| prog 1 vs. prog 2 -0.36032939 0.08022363 -4.49 <.0001

There is separate output for each of the outcome variables. Each of the tables in the output gives
the estimate (in this case the difference between the coefficients), the standard error of this estimate, the t-value
and associated p-value. The output indicates that the coefficient for **prog**=1 is significantly different
from the coefficient for **prog**=2 for each of the outcomes.

The next example tests the null hypothesis that the coefficient for the
variable **write** in the equation with **locus_of_control** as the
outcome is equal to the coefficient for **write** in the equation with **
self_concept** as the outcome. We request this test by adding a second **
manova** statement, where **h** gives the predictor variable or variables
to be tested (i.e., **h=write**) and **m** gives the combination of outcome variables to test
(i.e., **m=locus_of_control - self_concept**).

proc glm data = data.mvreg; class prog ; model locus_of_control self_concept motivation = read write science prog / solution ss3; manova h= _ALL_ ; manova h=write m=locus_of_control - self_concept; run; quit;

Again, we will only show the portion of the output associated with the new **manova** statement.
The first table (shown below) gives the matrix for the outcome variables.
In
this case, we want to subtract the coefficients for **self_concept** (multiplied by
-1) from the values of the coefficients for **locus_of_control** (multiplied by 1).
Because motivation isn't involved in the test, it is multiplied by zero.

M Matrix Describing Transformed Variables LOCUS_OF_ CONTROL SELF_CONCEPT MOTIVATION MVAR1 1 -1 0 The GLM Procedure Multivariate Analysis of Variance Characteristic Roots and Vectors of: E Inverse * H, where H = Type III SSCP Matrix for WRITE E = Error SSCP Matrix Variables have been transformed by the M Matrix Characteristic Characteristic Vector V'EV=1 Root Percent MVAR1 0.02001074 100.00 0.04807919 MANOVA Test Criteria and Exact F Statistics for the Hypothesis of No Overall WRITE Effect on the Variables Defined by the M Matrix Transformation H = Type III SSCP Matrix for WRITE E = Error SSCP Matrix S=1 M=-0.5 N=296 Statistic Value F Value Num DF Den DF Pr > F Wilks' Lambda 0.98038183 11.89 1 594 0.0006 Pillai's Trace 0.01961817 11.89 1 594 0.0006 Hotelling-Lawley Trace 0.02001074 11.89 1 594 0.0006 Roy's Greatest Root 0.02001074 11.89 1 594 0.0006

The last table in the output shows that regardless of which multivariate statistic is used,
the coefficient for **write** with **locus_of_control **as the outcome
and the coefficient for **write** with **self_concept** as the outcome
are significantly different.

For the final example, we test the null hypothesis that the coefficient for **
science** in the equation for **locus_of_control** is equal to the
coefficient for **science** in the equation for **self_concept**, and that
the coefficient for the variable **write** in the equation for **
locus_of_control** is equal to the coefficient for **write** in the
equation for **self_concept**. To perform this test we need to use both the
**contrast** statement and the **manova** statement. In the **contrast**
statement, we specify the predictor variables we wish to test, in this case, we
want to multiply the coefficients for **write** and **science** by 1. In
the **manova** statement, we specify the portions of the test specific to the
outcome variables, in this case, we want to compare the coefficients for **
locus_of_control** and **self_concept**, by subtracting one set of
coefficients from the other.

proc glm data = data.mvreg; class prog; model locus_of_control self_concept motivation = read write science prog / solution ss3; contrast 'write & science' write 1, science 1 /e; manova m=locus_of_control - self_concept; run; quit;

As before, we will only show the portions of output associated with the test we are performing. Towards the beginning of the output (just after the class level information section) we see the table of contrasts for the coefficients. The matrix has two columns, one for each of the effects we wish to test.

Coefficients for Contrast write & science Row 1 Row 2 Intercept 0 0 READ 0 0 WRITE 1 0 SCIENCE 0 1 PROG 1 0 0 PROG 2 0 0 PROG 3 0 0

The output shown below is generated by the **manova** statement, and as before
it appears towards the end of the output.

M Matrix Describing Transformed Variables LOCUS_OF_ CONTROL SELF_CONCEPT MOTIVATION MVAR1 1 -1 0

Multivariate Analysis of Variance Characteristic Roots and Vectors of: E Inverse * H, where H = Contrast SSCP Matrix for write & science E = Error SSCP Matrix Variables have been transformed by the M Matrix Characteristic Characteristic Vector V'EV=1 Root Percent MVAR1 0.02150343 100.00 0.04807919 MANOVA Test Criteria and Exact F Statistics for the Hypothesis of No Overall write & science Effect on the Variables Defined by the M Matrix Transformation H = Contrast SSCP Matrix for write & science E = Error SSCP Matrix S=1 M=0 N=296 Statistic Value F Value Num DF Den DF Pr > F Wilks' Lambda 0.97894924 6.39 2 594 0.0018 Pillai's Trace 0.02105076 6.39 2 594 0.0018 Hotelling-Lawley Trace 0.02150343 6.39 2 594 0.0018 Roy's Greatest Root 0.02150343 6.39 2 594 0.0018

The last table in the above output shows that regardless of which multivariate statistic is used, taken together, the two sets of coefficients are significantly different.

## Things to consider

- The residuals from multivariate regression models are assumed to be multivariate normal. This is analogous to the assumption of normally distributed errors in univariate linear regression (i.e., OLS regression).
- Multivariate regression analysis is not recommended for small samples.
- The outcome variables should be at least moderately correlated for the multivariate regression analysis to make sense.

## References

Afifi, A., Clark, V. and May, S. 2004. Computer-Aided Multivariate Analysis. 4th ed. Boca Raton, Fl: Chapman & Hall/CRC.

## See also

SAS Library: Multivariate regression in SAS