**Chapter Outline
3.0 Regression with Categorical Predictors
3.1 Regression with a 0/1 variable
3.2 Regression with a 1/2 variable
3.3 Regression with a 1/2/3 variable
3.4 Regression with multiple categorical predictors
3.5 Categorical predictor with interactions
3.6 Continuous and Categorical variables
3.7 Interactions of Continuous by 0/1 Categorical variables
3.8 Continuous and Categorical variables, interaction with 1/2/3 variable
3.9 Summary
3.10 Self assessment
3.11 For more information**

**Please note:** This page makes use of the program **xi3** which is no longer being maintained and has been from our
archives. References to **xi3** will be left on this page because they illustrate specific principles of coding categorical
variables.

**3.0 Introduction**

In the previous two chapters, we have focused on regression analyses using continuous variables. However, it is possible to include categorical predictors in a regression analysis, but it requires some extra work in performing the analysis and extra work in properly interpreting the results. This chapter will illustrate how you can use Stata for including categorical predictors in your analysis and describe how to interpret the results of such analyses. Stata has some great tools that really ease the process of including categorical variables in your regression analysis, and we will emphasize the use of these timesaving tools.

This chapter will use the **elemapi2** data that you have seen in the prior
chapters. We will focus on four variables **api00**, **some_col**,
**yr_rnd** and **mealcat**, which takes **meals**
and breaks it up into 3 categories. Let’s have a quick look at these variables.

use http://www.ats.ucla.edu/stat/stata/webbooks/reg/elemapi2 describe api00 some_col yr_rnd mealcatstorage display value variable name type format label variable label ------------------------------------------------------------------------------- api00 int %6.0g api 2000 some_col byte %4.0f parent some college yr_rnd byte %4.0f yr_rnd year round school mealcat byte %18.0g mealcat Percentage free meals in 3 categories

The variable **api00** is a measure of the performance of the schools.
Below we see the codebook information for **api00**

codebook api00

api00 ---------------------------------------------------------------- api 2000 type: numeric (int) range: [369,940] units: 1 unique values: 271 coded missing: 0 / 400 mean: 647.622 std. dev: 142.249 percentiles: 10% 25% 50% 75% 90% 465.5 523.5 643 762.5 850

The variable **some_col** is a continuous variable that measures the
percentage of the parents in the school who have attended college, and the codebook
information is shown below.

codebook some_col

some_col -------------------------------------------------- parent some college type: numeric (byte) range: [0,67] units: 1 unique values: 49 coded missing: 0 / 400 mean: 19.7125 std. dev: 11.3369 percentiles: 10% 25% 50% 75% 90% 2.5 12 19 28 34

The variable **yr_rnd** is a categorical variable that is coded 0 if the
school is not year round, and 1 if year round, see below.

codebook yr_rnd

yr_rnd ------------------------------------------------------ year round school type: numeric (byte) label: yr_rnd range: [0,1] units: 1 unique values: 2 coded missing: 0 / 400 tabulation: Freq. Numeric Label 308 0 No 92 1 Yes

The variable **meals** is the percentage of students who are receiving
state sponsored free meals and can be used as an indicator of poverty. This was broken
into 3 categories (to make equally sized groups) creating the variable **mealcat**.
The
codebook information for **mealcat** is shown below.

codebook mealcat

mealcat ----------------------------------------------------------- (unlabeled) type: numeric (float) label: mealcat range: [1,3] units: 1 unique values: 3 coded missing: 0 / 400 tabulation: Freq. Numeric Label 131 1 0-46% free meals 132 2 47-80% free meals 137 3 81-100% free meals

3.1 Regression with a 0/1 variable

The simplest example of a categorical predictor in a regression analysis is a 0/1
variable, also called a dummy variable. Let’s use the variable **yr_rnd** as
an example of a dummy variable. We can include a dummy variable as a predictor in a
regression analysis as shown below.

regress api00 yr_rnd

Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 1, 398) = 116.24 Model | 1825000.56 1 1825000.56 Prob > F = 0.0000 Residual | 6248671.43 398 15700.1795 R-squared = 0.2260 -------------+------------------------------ Adj R-squared = 0.2241 Total | 8073672.00 399 20234.7669 Root MSE = 125.30 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- yr_rnd | -160.5064 14.8872 -10.78 0.000 -189.7737 -131.239 _cons | 684.539 7.13965 95.88 0.000 670.5028 698.5751 ------------------------------------------------------------------------------

This may seem odd at first, but this is a legitimate analysis. But what does this mean? Let’s go back to basics and write out the regression equation that this model implies.

api00 = _cons + Byr_rnd * yr_rnd

where **_cons** is the intercept (or constant) and we use **Byr_rnd**
to represent the coefficient for variable **yr_rnd**. Filling in the
values from the regression equation, we get

api00 = 684.539 + -160.5064 * yr_rnd

If a school is not a year-round school (i.e. **yr_rnd** is 0) the
regression equation would simplify to

api00 = constant + 0 * Byr_rnd api00 = 684.539 + 0 * -160.5064 api00 = 684.539

If a school is a year-round school, the regression equation would simplify to

api00 = constant + 1 * Byr_rnd api00 = 684.539 + 1 * -160.5064 api00 = 524.0326

We can graph the observed values and the predicted values using the **scatter**
command as shown below. Although **yr_rnd** only has 2 values, we can still
draw a regression line showing the relationship between **yr_rnd** and **api00**.
Based on the results above, we see that the predicted value for non-year round
schools is 684.539 and the predicted value for the year round schools is
524.032, and the slope of the line is negative, which makes sense since the
coefficient for **yr_rnd** was negative (-160.5064).

twoway (scatter api00 yr_rnd) (lfit api00 yr_rnd)

Let’s compare these predicted values to the mean **api00** scores for the
year-round and non-year-round schools.

tabulate yr_rnd, sum(api00)

year round | Summary of api 2000 school | Mean Std. Dev. Freq. ------------+------------------------------------ No | 684.53896 132.11253 308 Yes | 524.03261 98.916043 92 ------------+------------------------------------ Total | 647.6225 142.24896 400

As you see, the regression equation predicts that the value of **api00**
will be the mean value, depending on whether a school is a year round school
or non-year round school.

Let’s relate these predicted values back to the regression equation. For the
non-year-round schools, their mean is the same as the intercept (684.539). The
coefficient for **yr_rnd** is the amount we need to add to get the mean for
the year-round schools, i.e., we need to add -160.5064 to get 524.0326, the mean for the
non year-round schools. In other words, **Byr_rnd** is the mean **api00**
score for the year-round schools minus the mean ** api00** score for the non year-round
schools, i.e., mean(year-round) – mean(non year-round).

It may be surprising to note that this regression analysis with a single dummy variable
is the same as doing a t-test comparing the mean **api00** for the year-round
schools with the non year-round schools (see below). You can see that the t value below
is the same as the t value for **yr_rnd** in the regression above. This is because
**Byr_rnd** compares the year-rounds and non year-rounds (since
the coefficient is mean(year round)-mean(non year-round)).

ttest api00, by(yr_rnd)Two-sample t test with equal variances ------------------------------------------------------------------------------ Group | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] ---------+-------------------------------------------------------------------- No | 308 684.539 7.52781 132.1125 669.7263 699.3516 Yes | 92 524.0326 10.31271 98.91604 503.5477 544.5175 ---------+-------------------------------------------------------------------- combined | 400 647.6225 7.112448 142.249 633.6399 661.6051 ---------+-------------------------------------------------------------------- diff | 160.5064 14.8872 131.239 189.7737 ------------------------------------------------------------------------------ Degrees of freedom: 398 Ho: mean(No) - mean(Yes) = diff = 0 Ha: diff < 0 Ha: diff ~= 0 Ha: diff > 0 t = 10.7815 t = 10.7815 t = 10.7815 P < t = 1.0000 P > |t| = 0.0000 P > t = 0.0000

Since a t-test is the same as doing an **anova**, we can get the same results using the **anova**
command as well.

anova api00 yr_rndNumber of obs = 400 R-squared = 0.2260 Root MSE = 125.30 Adj R-squared = 0.2241 Source | Partial SS df MS F Prob > F -----------+---------------------------------------------------- Model | 1825000.56 1 1825000.56 116.24 0.0000 | yr_rnd | 1825000.56 1 1825000.56 116.24 0.0000 | Residual | 6248671.43 398 15700.1795 -----------+---------------------------------------------------- Total | 8073672.00 399 20234.7669

If we square the t-value from the t-test, we get the same value as the F-value from the anova.

di 10.7815^2116.24074

3.2 Regression with a 1/2 variable

A categorical predictor variable does not have to be coded 0/1 to be used in a regression model. It is easier to understand and interpret the results from a model with dummy variables, but the results from a variable coded 1/2 yield essentially the same results.

Lets make a copy of the variable **yr_rnd** called **yr_rnd2**
that is coded 1/2, 1=non year-round and 2=year-round.

generate yr_rnd2=yr_rnd recode yr_rnd2 0=1 1=2(400 changes made)

Let’s perform a regression predicting **api00** from **yr_rnd2**.

regress api00 yr_rnd2Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 1, 398) = 116.24 Model | 1825000.56 1 1825000.56 Prob > F = 0.0000 Residual | 6248671.43 398 15700.1795 R-squared = 0.2260 -------------+------------------------------ Adj R-squared = 0.2241 Total | 8073672.00 399 20234.7669 Root MSE = 125.30 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- yr_rnd2 | -160.5064 14.8872 -10.78 0.000 -189.7737 -131.239 _cons | 845.0453 19.35336 43.66 0.000 806.9977 883.0929 ------------------------------------------------------------------------------

Note that the coefficient for **yr_rnd** is the same as **yr_rnd2**.
So, you can see that if you code **yr_rnd** as 0/1 or as 1/2, the regression
coefficient works out to be the same. However the intercept (_cons) is a bit less
intuitive. When we used **yr_rnd**, the intercept was the mean for the non
year-rounds. When using **yr_rnd2**, the intercept is the mean for the non
year-rounds minus **Byr_rnd2**, i.e., 684.539 – (-160.506) = 845.045

Note that you can use 0/1 or 1/2 coding and the results for the coefficient come out the same, but the interpretation of the constant in the regression equation is different. It is often easier to interpret the estimates for 0/1 coding.

In summary, these results indicate that the **api00** scores are
significantly different for the schools depending on the type of school, year
round school vs. non-year round school. Non year-round schools have
significantly higher API scores than year-round schools. Based on the regression results,
non year-
round schools have scores that are 160.5 points higher than year- round
schools.

3.3 Regression with a 1/2/3 variable

**3.3.1 Manually Creating Dummy Variables**

Say, that we would like to examine the relationship between the amount of poverty and
api scores. We don’t have a measure of poverty, but we can use **mealcat** as
a proxy for a measure of poverty. Below we repeat the codebook info for **mealcat**
showing the values for the three categories.

codebook mealcatmealcat ----------------------------------------------------------- (unlabeled) type: numeric (float) label: mealcat range: [1,3] units: 1 unique values: 3 coded missing: 0 / 400 tabulation: Freq. Numeric Label 131 1 0-46% free meals 132 2 47-80% free meals 137 3 81-100% free meals

You might be tempted to try including **mealcat** in a regression like this.

regress api00 mealcatSource | SS df MS Number of obs = 400 -------------+------------------------------ F( 1, 398) = 1207.74 Model | 6072527.52 1 6072527.52 Prob > F = 0.0000 Residual | 2001144.48 398 5028.0012 R-squared = 0.7521 -------------+------------------------------ Adj R-squared = 0.7515 Total | 8073672.00 399 20234.7669 Root MSE = 70.908 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- mealcat | -150.5533 4.332147 -34.75 0.000 -159.0701 -142.0365 _cons | 950.9874 9.421798 100.93 0.000 932.4647 969.5101 ------------------------------------------------------------------------------

But this is looking at the linear effect of **mealcat** with **api00**,
but **mealcat** is not an interval variable. Instead, you will want to code the variable so
that all the information concerning the three levels is accounted for.
You can dummy code **mealcat** like this.

tabulate mealcat, gen(mealcat)mealcat | Freq. Percent Cum. -------------------+----------------------------------- 0-46% free meals | 131 32.75 32.75 47-80% free meals | 132 33.00 65.75 81-100% free meals | 137 34.25 100.00 -------------------+----------------------------------- Total | 400 100.00

We now have created **mealcat1** that is 1 if **mealcat** is
1, and 0 otherwise. Likewise, **mealcat2** is 1 if **mealcat**
is 2, and 0 otherwise and likewise **mealcat3** was created. We can see this
below.

list mealcat mealcat1 mealcat2 mealcat3 in 1/10, nolabelmealcat mealcat1 mealcat2 mealcat3 1. 1 1 0 0 2. 2 0 1 0 3. 3 0 0 1 4. 1 1 0 0 5. 1 1 0 0 6. 1 1 0 0 7. 1 1 0 0 8. 1 1 0 0 9. 1 1 0 0 10. 1 1 0 0

We can now use two of these dummy variables (**mealcat2** and **mealcat3**)
in the regression analysis.

regress api00 mealcat2 mealcat3Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 2, 397) = 611.12 Model | 6094197.67 2 3047098.83 Prob > F = 0.0000 Residual | 1979474.33 397 4986.08143 R-squared = 0.7548 -------------+------------------------------ Adj R-squared = 0.7536 Total | 8073672.00 399 20234.7669 Root MSE = 70.612 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- mealcat2 | -166.3236 8.708331 -19.10 0.000 -183.4438 -149.2034 mealcat3 | -301.338 8.628815 -34.92 0.000 -318.3019 -284.3741 _cons | 805.7176 6.169416 130.60 0.000 793.5887 817.8464 ------------------------------------------------------------------------------

We can test the overall differences among the three groups by using the **test** command as
shown below. This shows that the overall differences among the three groups are significant.

test mealcat2 mealcat3( 1) mealcat2 = 0.0 ( 2) mealcat3 = 0.0 F( 2, 397) = 611.12 Prob > F = 0.0000

The interpretation of the coefficients is much like that for the binary variables. Group 1 is
the omitted group, so **_cons** is the mean for group 1. The coefficient for **mealcat2**
is the mean for group 2 minus the mean of the omitted group (group 1). And the coefficient for
**mealcat3**
is the mean of group 3 minus the mean of group 1. You can verify this by comparing the
coefficients with the means of the groups.

tabulate mealcat, summarize(api00)| Summary of api 2000 mealcat | Mean Std. Dev. Freq. ------------+------------------------------------ 0-46% fre | 805.71756 65.668664 131 47-80% fr | 639.39394 82.13513 132 81-100% f | 504.37956 62.727015 137 ------------+------------------------------------ Total | 647.6225 142.24896 400

Based on these results, we can say that the three groups differ in their **api00**
scores, and that in particular group2 is significantly different from group1 (because **mealcat2**
was significant) and group 3 is significantly different from group 1 (because **mealcat3**
was significant).

3.3.2 Using the xi command

We can use the **xi** command to do the work for us to create the indicator
variables and run the regression all in one command, as shown below.

xi : regress api00 i.mealcati.mealcat _Imealcat_1-3 (naturally coded; _Imealcat_1 omitted) Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 2, 397) = 611.12 Model | 6094197.67 2 3047098.83 Prob > F = 0.0000 Residual | 1979474.33 397 4986.08143 R-squared = 0.7548 -------------+------------------------------ Adj R-squared = 0.7536 Total | 8073672.00 399 20234.7669 Root MSE = 70.612 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Imealcat_2 | -166.3236 8.708331 -19.10 0.000 -183.4438 -149.2034 _Imealcat_3 | -301.338 8.628815 -34.92 0.000 -318.3019 -284.3741 _cons | 805.7176 6.169416 130.60 0.000 793.5887 817.8464 ------------------------------------------------------------------------------

When we use **xi** and include the term **i.mealcat** in the model, Stata creates the
variables **_Imealcat_2** and **_Imealcat_3** that are dummy variables just like **mealcat2**
and **mealcat3** that we created before. There really is no difference between **mealcat2**
and **_Imealcat_2**.

As you can see, the results are the same as in the prior analysis. If we want to
test the overall effect of **mealcat** we use the test command as shown below, which
also gives us the same results as we found using the dummy variables **mealcat2**
and **mealcat3**.

test _Imealcat_2 _Imealcat_3( 1) _Imealcat_2 = 0.0 ( 2) _Imealcat_3 = 0.0 F( 2, 397) = 611.12 Prob > F = 0.0000

Note that if you are doing this in Stata version 6 the variables would be named **Imealc_2**
and **Imealc_3** instead of **_Imealcat_2** and **_Imealcat_3**.
One of the improvements in Stata 7 is that variable names can be longer than 8 characters,
so the names of the variables created by the **xi** command are easier to
understand than in version 6. From this point forward, we will use the variable names that
would be created in version 7.

What if we wanted a different group to be the **reference group**? If we
create dummy variables via **tabulate , generate()** then we can easily choose
which variable will be the omitted group, for example, let’s omit group 3.

regress api00 mealcat1 mealcat2Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 2, 397) = 611.12 Model | 6094197.67 2 3047098.83 Prob > F = 0.0000 Residual | 1979474.33 397 4986.08143 R-squared = 0.7548 -------------+------------------------------ Adj R-squared = 0.7536 Total | 8073672.00 399 20234.7669 Root MSE = 70.612 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- mealcat1 | 301.338 8.628815 34.92 0.000 284.3741 318.3019 mealcat2 | 135.0144 8.61209 15.68 0.000 118.0834 151.9454 _cons | 504.3796 6.032807 83.61 0.000 492.5193 516.2398 ------------------------------------------------------------------------------

With group 3 omitted, the constant is now the mean of group 3 and **mealcat1**
is group1-group3 and **mealcat2** is group2-group3. We see that both of
these coefficients are significant, indicating that group 1 is significantly different from
group 3 and group 2 is significantly different from group 3.

When we use the **xi** command, how can we choose which group is the
omitted group? By default, the first group is omitted, but say we want group 3 to be
omitted. We can use the **char** command as shown below to tell Stata that
we want the third group to be the omitted group for the variable **mealcat**.

char mealcat[omit] 3

Then, when we use the **xi** command using **mealcat** the
mealcat=3 group will be omitted. If you save the data file, Stata will remember this for
future Stata sessions.

xi : regress api00 i.mealcati.mealcat _Imealcat_1-3 (naturally coded; _Imealcat_3 omitted) Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 2, 397) = 611.12 Model | 6094197.67 2 3047098.83 Prob > F = 0.0000 Residual | 1979474.33 397 4986.08143 R-squared = 0.7548 -------------+------------------------------ Adj R-squared = 0.7536 Total | 8073672.00 399 20234.7669 Root MSE = 70.612 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Imealcat_1 | 301.338 8.628815 34.92 0.000 284.3741 318.3019 _Imealcat_2 | 135.0144 8.61209 15.68 0.000 118.0834 151.9454 _cons | 504.3796 6.032807 83.61 0.000 492.5193 516.2398 ------------------------------------------------------------------------------

You can compare and see that these results are identical to those found using **mealcat1**
and **mealcat2** as predictors.

3.3.3 Using the anova command

We can also do this analysis using the **anova** command. The benefit of
the **anova** command is that it gives us the test of the overall effect of **mealcat**
without needing to subsequently use the **test** command as we did with the **regress**
command.

anova api00 mealcatNumber of obs = 400 R-squared = 0.7548 Root MSE = 70.6122 Adj R-squared = 0.7536 Source | Partial SS df MS F Prob > F -----------+---------------------------------------------------- Model | 6094197.67 2 3047098.83 611.12 0.0000 | mealcat | 6094197.67 2 3047098.83 611.12 0.0000 | Residual | 1979474.33 397 4986.08143 -----------+---------------------------------------------------- Total | 8073672.00 399 20234.7669

We can see the **anova** test of the effect of **mealcat** is
the same as the **test** command from the regress command.

We can even follow this with the **anova, regress** command and compare
the parameter estimates with those we performed previously.

anova, regressSource | SS df MS Number of obs = 400 -------------+------------------------------ F( 2, 397) = 611.12 Model | 6094197.67 2 3047098.83 Prob > F = 0.0000 Residual | 1979474.33 397 4986.08143 R-squared = 0.7548 -------------+------------------------------ Adj R-squared = 0.7536 Total | 8073672.00 399 20234.7669 Root MSE = 70.612 ------------------------------------------------------------------------------ api00 Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------------------------------------------------------------------------ _cons 504.3796 6.032807 83.61 0.000 492.5193 516.2398 mealcat 1 301.338 8.628815 34.92 0.000 284.3741 318.3019 2 135.0144 8.61209 15.68 0.000 118.0834 151.9454 3 (dropped) ------------------------------------------------------------------------------

Note: the parameter estimates are the same because **mealcat** is coded
the same way in the **regress** command and in the **anova**
command, in both cases the last category (category 3) being dropped. While you can control
which category is the omitted category when you use the **regress** command, the **anova, regress** command always drops the last category.

3.3.4 Other coding schemes

It is generally very convenient to use dummy coding but that is not the only kind of coding that can be used. As you have seen, when you use dummy coding one of the groups becomes the reference group and all of the other groups are compared to that group. This may not be the most interesting set of comparisons.

Say you want to compare group 1 with groups 2 and 3, and for a second comparison
compare group 2 with group 3. You need to generate
a coding scheme that forms these 2 comparisons. We will illustrate this using a Stata program,
**xi3**, (an enhanced version of **xi**) that will
create the variables you would need for such comparisons (as well as a variety
of other common comparisons).

The comparisons that we have described (comparing group 1 with 2 and 3, and
then comparing groups 2 and 3) correspond to Helmert comparisons (see Chapter
5 for more details). We use the **h.** prefix (instead of the **i.**
prefix) to indicate that we desire Helmert comparisons on the variable mealcat.
Otherwise, you see that **xi3** works much like the **xi** command.

xi3: regress api00 h.mealcath.mealcat _Imealcat_1-3 (naturally coded; _Imealcat_3 omitted) Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 2, 397) = 611.12 Model | 6094197.67 2 3047098.83 Prob > F = 0.0000 Residual | 1979474.33 397 4986.08143 R-squared = 0.7548 -------------+------------------------------ Adj R-squared = 0.7536 Total | 8073672.00 399 20234.7669 Root MSE = 70.612 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Imealcat_1 | 233.8308 7.523544 31.08 0.000 219.0398 248.6218 _Imealcat_2 | 135.0144 8.61209 15.68 0.000 118.0834 151.9454 _cons | 649.8304 3.531285 184.02 0.000 642.888 656.7727 ----------------------------------------------------------------------------

If you compare the parameter estimates with the means (see below) you can verify that
the coefficient for **_Imealcat_1** is the mean of group 1 minus the mean of groups
2 and 3 (805.71756 – (639.39394 + 504.37956) / 2 = 233.83081) and the
coefficient for **_Imealcat_2** is the mean of group 2 minus group 3 (639.39
– 504.37 = 135.01). Both of these
comparisons are significant, indicating that group 1 differs significantly from groups
2 and 3 combined,
and group 2 differs significantly from group 3.

tabulate mealcat, sum(api00)| Summary of api 2000 mealcat | Mean Std. Dev. Freq. ------------+------------------------------------ 0-46% fre | 805.71756 65.668664 131 47-80% fr | 639.39394 82.13513 132 81-100% f | 504.37956 62.727015 137 ------------+------------------------------------ Total | 647.6225 142.24896 400

And the value of **_cons** is the unweighted average of the means of the 3 groups.

display (805.71756 +639.39394 +504.37956)/3649.83035

Using the coding scheme provided by **xi3**, we were able to form
perhaps more interesting tests than those provided by dummy coding. The **
xi3**
program can create variables according to other coding schemes, as well as
custom coding schemes that you create, see **help xi3** and Chapter
5 for more information.

3.4 Regression with two categorical predictors

**3.4.1 Using the xi: command **

Previously we looked at using **yr_rnd** to predict **api00**

regress api00 yr_rnd

Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 1, 398) = 116.24 Model | 1825000.56 1 1825000.56 Prob > F = 0.0000 Residual | 6248671.43 398 15700.1795 R-squared = 0.2260 -------------+------------------------------ Adj R-squared = 0.2241 Total | 8073672.00 399 20234.7669 Root MSE = 125.30 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- yr_rnd | -160.5064 14.8872 -10.78 0.000 -189.7737 -131.239 _cons | 684.539 7.13965 95.88 0.000 670.5028 698.5751 ------------------------------------------------------------------------------

And we have also looked at **mealcat** using the **xi** command

xi : regress api00 i.mealcati.mealcat _Imealcat_1-3 (naturally coded; _Imealcat_3 omitted) Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 2, 397) = 611.12 Model | 6094197.67 2 3047098.83 Prob > F = 0.0000 Residual | 1979474.33 397 4986.08143 R-squared = 0.7548 -------------+------------------------------ Adj R-squared = 0.7536 Total | 8073672.00 399 20234.7669 Root MSE = 70.612 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Imealcat_1 | 301.338 8.628815 34.92 0.000 284.3741 318.3019 _Imealcat_2 | 135.0144 8.61209 15.68 0.000 118.0834 151.9454 _cons | 504.3796 6.032807 83.61 0.000 492.5193 516.2398 ------------------------------------------------------------------------------

We can include both **yr_rnd** and **mealcat** together in the same model.

xi : regress api00 i.mealcat yr_rndi.mealcat _Imealcat_1-3 (naturally coded; _Imealcat_3 omitted) Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 3, 396) = 435.02 Model | 6194144.30 3 2064714.77 Prob > F = 0.0000 Residual | 1879527.69 396 4746.28206 R-squared = 0.7672 -------------+------------------------------ Adj R-squared = 0.7654 Total | 8073672.00 399 20234.7669 Root MSE = 68.893 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Imealcat_1 | 281.6832 9.445676 29.82 0.000 263.1132 300.2531 _Imealcat_2 | 117.9458 9.188911 12.84 0.000 99.88066 136.011 yr_rnd | -42.96006 9.361761 -4.59 0.000 -61.36502 -24.55509 _cons | 526.33 7.584533 69.40 0.000 511.419 541.2409 ------------------------------------------------------------------------------

We can test the overall effect of **mealcat** with the test command, which
is significant.

test _Imealcat_1 _Imealcat_2( 1) _Imealcat_1 = 0.0 ( 2) _Imealcat_2 = 0.0 F( 2, 396) = 460.27 Prob > F = 0.0000

Because this model has only main effects (no interactions) you can interpret **Byr_rnd**
as the difference between the year round and non-year round group. The
coefficient for **I_mealcat_1 **(which we will call **B_Imealcat_1**) is the difference between mealcat=1 and mealcat=3, and **B_Imealcat_2** as
the difference between mealcat=2 and mealcat=3.

Let’s dig below the surface and see how the coefficients relate to the predicted
values. Let’s view the cells formed by crossing **yr_rnd** and **mealcat**
and number the cells from cell1 to cell6.

mealcat=1 mealcat=2 mealcat=3 yr_rnd=0 cell1 cell2 cell3 yr_rnd=1 cell4 cell5 cell6

With respect to **mealcat**, the group **mealcat=3** is the
reference category, and with respect to **yr_rnd** the group **yr_rnd=0**
is the reference category. As a result, cell3 is the reference cell. The constant is the
predicted value for this cell.

The coefficient for **yr_rnd** is the difference between **cell3**
and **cell6**. Since this model has only main effects, it is also the
difference between cell2 and cell5, or from cell1 and cell4. In other words, **Byr_rnd**
is the amount you add to the predicted value when you go from non-year round to year round
schools.

The coefficient for **_Imealcat_1** is the predicted difference between
cell1 and cell3. Since this model only has main effects, it is also the predicted
difference between cell4 and cell6. Likewise, **B_Imealcat_2** is the
predicted difference between cell2 and cell3, and also the predicted difference between
cell5 and cell6.

So, the predicted values, in terms of the coefficients, would be

mealcat=1 mealcat=2 mealcat=3 ----------------------------------------------- yr_rnd=0 _cons _cons _cons +BImealcat1 +BImealcat2 ----------------------------------------------- yr_rnd=1 _cons _cons _cons +Byr_rnd +Byr_rnd +Byr_rnd +BImealcat1 +BImealcat2

We should note that if you computed the predicted values for each cell, they would not exactly match the means in the 6 cells. The predicted means would be close to the observed means in the cells, but not exactly the same. This is because our model only has main effects and assumes that the difference between cell1 and cell4 is exactly the same as the difference between cells 2 and 5 which is the same as the difference between cells 3 and 6. Since the observed values don’t follow this pattern, there is some discrepancy between the predicted means and observed means.

3.4.2 Using the anova command

We can run the same analysis using the **anova** command with just main
effects

anova api00 yr_rnd mealcatNumber of obs = 400 R-squared = 0.7672 Root MSE = 68.8933 Adj R-squared = 0.7654 Source | Partial SS df MS F Prob > F -----------+---------------------------------------------------- Model | 6194144.30 3 2064714.77 435.02 0.0000 | yr_rnd | 99946.6332 1 99946.6332 21.06 0.0000 mealcat | 4369143.74 2 2184571.87 460.27 0.0000 | Residual | 1879527.69 396 4746.28206 -----------+---------------------------------------------------- Total | 8073672.00 399 20234.7669

Note that we get the same information that we do from the **xi : regress**
command, followed by the **test** command. The **anova** command
automatically provides the information provided by the **test** command. If
we like, we can also request the parameter estimates later just by doing this.

anova, regressSource | SS df MS Number of obs = 400 -------------+------------------------------ F( 3, 396) = 435.02 Model | 6194144.30 3 2064714.77 Prob > F = 0.0000 Residual | 1879527.69 396 4746.28206 R-squared = 0.7672 -------------+------------------------------ Adj R-squared = 0.7654 Total | 8073672.00 399 20234.7669 Root MSE = 68.893 ------------------------------------------------------------------------------ api00 Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------------------------------------------------------------------------ _cons 483.3699 7.45694 64.82 0.000 468.7098 498.03 yr_rnd 1 42.96006 9.361761 4.59 0.000 24.55509 61.36502 2 (dropped) mealcat 1 281.6832 9.445676 29.82 0.000 263.1132 300.2531 2 117.9458 9.188911 12.84 0.000 99.88066 136.011 3 (dropped) ------------------------------------------------------------------------------

**anova** will display the parameter estimates from the last anova model. However, the
**anova** command is rigid in its determination of which group will be the omitted group and
the last group is dropped. Since this differs from the coding we used in the
regression commands above, the parameter estimates from this anova command will differ
from the regress command above.

In summary, these results indicate the differences between year round and non-year
round schools is significant, and the differences among the three **mealcat**
groups are significant.

3.5 Categorical predictor with interactions

**3.5.1 using xi**

Let’s perform the same analysis that we performed above, this time let’s include the
interaction of **mealcat** by **yr_rnd**. When using **xi**,
it is easy to include an interaction term, as shown below.

xi : regress api00 i.mealcat*yr_rndi.mealcat _Imealcat_1-3 (naturally coded; _Imealcat_3 omitted) i.meal~t*yr_rnd _ImeaXyr_rn_# (coded as above) Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 5, 394) = 261.61 Model | 6204727.82 5 1240945.56 Prob > F = 0.0000 Residual | 1868944.18 394 4743.51314 R-squared = 0.7685 -------------+------------------------------ Adj R-squared = 0.7656 Total | 8073672.00 399 20234.7669 Root MSE = 68.873 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Imealcat_1 | 288.1929 10.44284 27.60 0.000 267.6623 308.7236 _Imealcat_2 | 123.781 10.55185 11.73 0.000 103.036 144.5259 yr_rnd | -33.49254 11.77129 -2.85 0.005 -56.63492 -10.35015 _ImeaXyr_r~1 | -40.76438 29.23118 -1.39 0.164 -98.23297 16.70422 _ImeaXyr_r~2 | -18.24763 22.25624 -0.82 0.413 -62.00347 25.5082 _cons | 521.4925 8.414197 61.98 0.000 504.9502 538.0349 ------------------------------------------------------------------------------

We can test the overall interaction with the **test** command. This interaction effect is not
significant.

test _ImeaXyr_rn_1 _ImeaXyr_rn_2( 1) _ImeaXyr_rn_1 = 0.0 ( 2) _ImeaXyr_rn_2 = 0.0 F( 2, 394) = 1.12 Prob > F = 0.3288

It is important to note how the meaning of the coefficients change in the presence of
these interaction terms. For example, in the prior model, with only main effects, we could
interpret **Byr_rnd** as the difference between the year round and non year
round schools. However, now that we have added the interaction term, the term **Byr_rnd**
represents the difference between cell3 and cell6, or the difference between the year
round and non-year round schools when **mealcat**=3 (because **mealcat**=3 was the omitted
group). The presence of an interaction would imply that the difference between year round
and non-year round schools depends on the level of **mealcat**.
The interaction terms **B_ImeaXyr_rn_1**
and **B_ImeaXyr_rn_2** represent the extent to which the difference between
the year round/non year round schools changes when mealcat=1 and when mealcat=2 (as
compared to the reference group, mealcat=3). For example the term **B_ImeaXyr_rn_1**
represents the difference between year round and non-year round for mealcat=1 vs. the
difference for mealcat=3. In other words, **B_ImeaXyr_rn_1** in this design is (cell1-cell4) –
(cell3-cell6), or it represents how much the effect of **yr_rnd** differs
between mealcat=1 and mealcat=3.

Below we have shown the predicted values for the six cells in terms of the coefficients
in the model. If you compare this to the main effects model, you will see that the
predicted values are the same except for the addition of **_ImeaXyr_rn_1**
(in cell 4) and **_ImeaXyr_rn_2** (in cell 5).

mealcat=1 mealcat=2 mealcat=3 ------------------------------------------------- yr_rnd=0 _cons _cons _cons +BImealcat1 +BImealcat2 ------------------------------------------------- yr_rnd=1 _cons _cons _cons +Byr_rnd +Byr_rnd +Byr_rnd +BImealcat1 +BImealcat2 +B_ImeaXyr_rn_1 +B_ImeaXyr_rn_2

It can be very tricky to interpret these interaction terms if you wish to form specific
comparisons. For example, if you wanted to perform a test of the simple main effect of **yr_rnd**
when **mealcat**=1, i.e., comparing cell1 with cell4, you would want to compare
**_cons+ BImealcat1 vs. _cons + B yr_rnd + BImealcat1+ BImeaXyr_rn_1 and since _cons and Imealcat1 would drop out, we would
test **

test _b[yr_rnd] +_b[_ImeaXyr_rn_1] ==0( 1) yr_rnd + _ImeaXyr_rn_1 = 0.0 F( 1, 394) = 7.70 Prob > F = 0.0058

This test is significant, indicating that the effect of **yr_rnd** is significant
for the **mealcat** = 1 group.

As we will see, such tests can be more easily done via anova.

3.5.2 Using anova

Constructing these interactions can be somewhat easier when using the **anova**
command. As you see below, the **anova** command gives us the test of the overall
main effects and interactions without the need to perform subsequent **test** commands.

anova api00 yr_rnd mealcat yr_rnd*mealcatNumber of obs = 400 R-squared = 0.7685 Root MSE = 68.8732 Adj R-squared = 0.7656 Source | Partial SS df MS F Prob > F ---------------+---------------------------------------------------- Model | 6204727.82 5 1240945.56 261.61 0.0000 | yr_rnd | 99617.3706 1 99617.3706 21.00 0.0000 mealcat | 1796232.80 2 898116.399 189.34 0.0000 yr_rnd*mealcat | 10583.5187 2 5291.75936 1.12 0.3288 | Residual | 1868944.18 394 4743.51314 ---------------+---------------------------------------------------- Total | 8073672.00 399 20234.7669

It is easy to perform tests of simple main effects using the **sme**
command. You can download **sme** from within Stata by
typing **search sme** (see
How can I used the search command to search for programs and get additional
help? for more information about using **search**).

Now we can test the simple main effects of **yr_rnd** at each level of **mealcat**.

sme yr_rnd mealcatTest of yr_rnd at mealcat(1): F(1/394) = 7.7023296 Test of yr_rnd at mealcat(2): F(1/394) = 7.5034121 Test of yr_rnd at mealcat(3): F(1/394) = 8.0955856 Critical value of F for alpha = .05 using ... -------------------------------------------------- Dunn's procedure = 4.7435944 Marascuilo & Levin = 5.4561926 per family error rate = 5.7804 simultaneous test procedure = 8.1680324

The results from **sme** show us the effect of **yr_rnd** at
each of the 3 levels of **mealcat**. We can see that the comparison for
**mealcat** = 1 matches those we computed above using the test statement, however, it was much
easier and less error prone using the **sme** command.

Although this section has focused on how to handle analyses involving interactions, these particular results show no indication of interaction. We could decide to omit interaction terms from future analyses having found the interactions to be non-significant. This would simplify future analyses, however including the interaction term can be useful to assure readers that the interaction term is non-significant.

3.6 Continuous and Categorical variables

**3.6.1 Using regress**

Say that we wish to analyze both continuous and categorical variables in one analysis.
For example, let’s include **yr_rnd** and **some_col** in the
same analysis.

regress api00 yr_rnd some_colSource | SS df MS Number of obs = 400 -------------+------------------------------ F( 2, 397) = 68.54 Model | 2072201.84 2 1036100.92 Prob > F = 0.0000 Residual | 6001470.16 397 15117.0533 R-squared = 0.2567 -------------+------------------------------ Adj R-squared = 0.2529 Total | 8073672.00 399 20234.7669 Root MSE = 122.95 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- yr_rnd | -149.1591 14.87519 -10.03 0.000 -178.4031 -119.9151 some_col | 2.235689 .5528656 4.04 0.000 1.148779 3.322599 _cons | 637.8581 13.50332 47.24 0.000 611.3111 664.405 ------------------------------------------------------------------------------

We can create the predicted values using the **predict** command.

predict yhat(option xb assumed; fitted values)

Let’s graph the predicted values by **some_col**.

scatter yhat some_col

The coefficient for **some_col** indicates that for every unit increase in **some_col**
the **api00** score is predicted to increase by 2.23 units. This is the slope
of the lines shown in the above graph. The graph has two lines, one for the year round
schools and one for the non-year round schools. The coefficient for **yr_rnd**
is -149.16, indicating that as **yr_rnd** increases by 1 unit, the **api00**
score is expected to decrease by about 149 units. As you can see in the graph, the top
line is about 150 units higher than the lower line. You can see that the intercept is 637
and that is where the upper line crosses the Y axis when X is 0. The lower line crosses
the line about 150 units lower at about 487.

3.6.2 Using anova

We can run this analysis using the **anova** command. The **anova** command
assumes that the variables are categorical, thus, we need to use the **continuous()** option
(which can be abbreviated as **cont()**) to specify that **some_col** is a
continuous variable.

anova api00 yr_rnd some_col, cont(some_col)Number of obs = 400 R-squared = 0.2567 Root MSE = 122.951 Adj R-squared = 0.2529 Source | Partial SS df MS F Prob > F -----------+---------------------------------------------------- Model | 2072201.84 2 1036100.92 68.54 0.0000 | yr_rnd | 1519992.67 1 1519992.67 100.55 0.0000 some_col | 247201.276 1 247201.276 16.35 0.0001 | Residual | 6001470.16 397 15117.0533 -----------+---------------------------------------------------- Total | 8073672.00 399 20234.7669

If we square the t-values from the **regress** command (above), we would find that they
match those of the **anova** command.

**3.7 Interactions of Continuous by 0/1 Categorical variables**

Above we showed an analysis that looked at the relationship between **some_col** and
**api00** and also included **yr_rnd**. We saw that this produced a graph
where we saw the relationship between **some_col** and **api00** but there were two
regression lines, one higher than the other but with equal slope. Such a model
assumed that the slope was the same for the two groups.
Perhaps the slope might be different for these groups. Let’s run
the regressions separately for these two groups beginning with the non-year round schools.

regress api00 some_col if yr_rnd==0Source | SS df MS Number of obs = 308 -------------+------------------------------ F( 1, 306) = 4.91 Model | 84700.8576 1 84700.8576 Prob > F = 0.0274 Residual | 5273591.67 306 17233.9597 R-squared = 0.0158 -------------+------------------------------ Adj R-squared = 0.0126 Total | 5358292.53 307 17453.7216 Root MSE = 131.28 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- some_col | 1.409427 .6357572 2.22 0.027 .1584181 2.660436 _cons | 655.1103 15.23704 42.99 0.000 625.1277 685.0929 ------------------------------------------------------------------------------predict yhat0 if yr_rnd==0(option xb assumed; fitted values) (92 missing values generated)scatter yhat0 api00 some_col if yr_rnd==0, connect(l i) msymbol(i o) sort

Likewise, let’s look at the year round schools.

regress api00 some_col if yr_rnd==1Source | SS df MS Number of obs = 92 -------------+------------------------------ F( 1, 90) = 65.08 Model | 373644.064 1 373644.064 Prob > F = 0.0000 Residual | 516734.838 90 5741.4982 R-squared = 0.4196 -------------+------------------------------ Adj R-squared = 0.4132 Total | 890378.902 91 9784.38354 Root MSE = 75.773 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- some_col | 7.402618 .9176327 8.07 0.000 5.57958 9.225655 _cons | 407.0391 16.51462 24.65 0.000 374.2299 439.8482 ------------------------------------------------------------------------------predict yhat1 if yr_rnd==1(option xb assumed; fitted values) (308 missing values generated)

scatter yhat1 api00 some_col if yr_rnd==1, connect(l i) msymbol(i o) sort

Note that the slope of the regression line looks much steeper for the year round
schools than for the non-year round schools. This is confirmed by the regression equations
that show the slope for the year round schools to be higher (7.4) than non-year round
schools (1.3). We can compare these to see if these are significantly different from each
other by including the interaction of **some_col** by **yr_rnd**, an interaction of a
continuous variable by a categorical variable.

3.7.1 Computing interactions manually

We will start by manually computing the interaction of **some_col** by **yr_rnd**.
Let’s start fresh and use the **elemapi2** data file using the **, clear**
option to clear out any variables we have previously created.

use http://www.ats.ucla.edu/stat/stata/webbooks/reg/elemapi2, clear

Next, let’s make a variable that is the interaction of some college (**some_col**)
and year round schools (**yr_rnd**) called **yrXsome**.

gen yrXsome = yr_rnd*some_col

We can now run the regression that tests whether the coefficient for **some_col** is
significantly different for year round schools and non-year round schools. Indeed, the
**yrXsome** interaction effect is significant.

regress api00 some_col yr_rnd yrXsomeSource | SS df MS Number of obs = 400 -------------+------------------------------ F( 3, 396) = 52.05 Model | 2283345.48 3 761115.162 Prob > F = 0.0000 Residual | 5790326.51 396 14622.0366 R-squared = 0.2828 -------------+------------------------------ Adj R-squared = 0.2774 Total | 8073672.00 399 20234.7669 Root MSE = 120.92 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- some_col | 1.409427 .5856022 2.41 0.017 .2581494 2.560705 yr_rnd | -248.0712 29.85895 -8.31 0.000 -306.7731 -189.3694 yrXsome | 5.99319 1.57715 3.80 0.000 2.892557 9.093824 _cons | 655.1103 14.03499 46.68 0.000 627.5179 682.7027 ------------------------------------------------------------------------------

We can make a graph showing the regression lines for the two types of schools showing
how different their regression lines are. We first create the predicted value,
we call it **yhata**.

predict yhata(option xb assumed; fitted values)

Then, we create separate variables for the two types of schools which will be called
**yhata0** for non-year round schools and **yhata1** for year round schools.

separate yhata, by(yr_rnd)storage display value variable name type format label variable label ------------------------------------------------------------------------------- yhata0 float %9.0g yhata, yr_rnd == 0 yhata1 float %9.0g yhata, yr_rnd == 1

We can then graph the predicted values for the two types of schools by **some_col**.
You can see how the two lines have quite different slopes, consistent with the fact that
the **yrXsome** interaction was significant. The **c(ll[_])**
option indicates that **yhata0** should be connected with a line, and **yhata1**
should be connected with dashed lines (because we included **[_]** after the **l**
). If we had used **l[.]** it would have made a dotted line. The options to
make dashed and dotted lines are new to Stata 7 and you can find more information via **help
grsym** .

line yhata0 yhata1 some_col, sort

We can replot the same graph including the data points.

twoway (line yhata0 yhata1 some_col, sort) (scatter api00 some_col)

The graph above used the same kind of dots for the data points for both types of
schools. Let’s make separate variables for the **api00** scores for the two types of
schools called **api000** for the non-year round schools and **api001**
for the year round schools.

separate api00, by(yr_rnd)storage display value variable name type format label variable label ------------------------------------------------------------------------------- api000 int %6.0g api00, yr_rnd == 0 api001 int %6.0g api00, yr_rnd == 1

We can then make the same graph as above except show the points differently for the two types of schools. Below we use small circles for the non-year round schools, and triangles for the year round schools.

twoway (line yhata0 yhata1 some_col, sort) (scatter api000 api001 some_col)

Let’s quickly run the regressions again where we performed separate regressions for the two groups

Non-year round

regress api00 some_col if yr_rnd==0Source | SS df MS Number of obs = 308 -------------+------------------------------ F( 1, 306) = 4.91 Model | 84700.8576 1 84700.8576 Prob > F = 0.0274 Residual | 5273591.67 306 17233.9597 R-squared = 0.0158 -------------+------------------------------ Adj R-squared = 0.0126 Total | 5358292.53 307 17453.7216 Root MSE = 131.28 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- some_col | 1.409427 .6357572 2.22 0.027 .1584181 2.660436 _cons | 655.1103 15.23704 42.99 0.000 625.1277 685.0929 ------------------------------------------------------------------------------

Year round

regress api00 some_col if yr_rnd==1Source | SS df MS Number of obs = 92 -------------+------------------------------ F( 1, 90) = 65.08 Model | 373644.064 1 373644.064 Prob > F = 0.0000 Residual | 516734.838 90 5741.4982 R-squared = 0.4196 -------------+------------------------------ Adj R-squared = 0.4132 Total | 890378.902 91 9784.38354 Root MSE = 75.773 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- some_col | 7.402618 .9176327 8.07 0.000 5.57958 9.225655 _cons | 407.0391 16.51462 24.65 0.000 374.2299 439.8482 ------------------------------------------------------------------------------

Now, let’s show the regression for both types of schools with the interaction term.

regress api00 some_col yr_rnd yrXsomeSource | SS df MS Number of obs = 400 -------------+------------------------------ F( 3, 396) = 52.05 Model | 2283345.48 3 761115.162 Prob > F = 0.0000 Residual | 5790326.51 396 14622.0366 R-squared = 0.2828 -------------+------------------------------ Adj R-squared = 0.2774 Total | 8073672.00 399 20234.7669 Root MSE = 120.92 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- some_col | 1.409427 .5856022 2.41 0.017 .2581494 2.560705 yr_rnd | -248.0712 29.85895 -8.31 0.000 -306.7731 -189.3694 yrXsome | 5.99319 1.57715 3.80 0.000 2.892557 9.093824 _cons | 655.1103 14.03499 46.68 0.000 627.5179 682.7027 ------------------------------------------------------------------------------

Note that the coefficient for **some_col** in the combined analysis is the same as the coefficient for **some_col**
for the non-year round schools? This is because non-year round schools are the reference
group. Then, the coefficient for the **yrXsome** interaction in the
combined analysis is the **Bsome_col**
for the year round schools (7.4) minus **Bsome_col** for the non year round schools
(1.41) yielding 5.99. This interaction is the difference in the slopes of **some_col**
for the two types of schools, and this is why this is useful for testing whether the
regression lines for the two types of schools are equal. If the two types of schools had
the same regression coefficient for **some_col**, then the coefficient for
the **yrXsome**
interaction would be 0. In this case, the difference is significant, indicating that the
regression lines are significantly different.

So, if we look at the graph of the two regression lines we can see the
difference in the slopes of the regression lines (see graph below).
Indeed, we can see that the non-year round schools (the solid line) have a
smaller slope (1.4) than the slope for the year
round schools (7.4). The difference between these slopes is 5.99, the
coefficient for **yrXsome**.

line yhata0 yhata1 some_col, sort

**3.7.2 Computing interactions with xi**

We can use the **xi** command for doing this kind of analysis as well. Let’s
start fresh and use the **elemapi2** file.

use http://www.ats.ucla.edu/stat/stata/webbooks/reg/elemapi2, clear

We can run a model just like the model we showed above using the **xi** command. You
can compare the results to those above and see that we get the exact same results.

xi : regress api00 i.yr_rnd*some_coli.yr_rnd _Iyr_rnd_1-2 (naturally coded; _Iyr_rnd_1 omitted) i.yr_rnd*some~l _Iyr_Xsome__# (coded as above) Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 3, 396) = 52.05 Model | 2283345.48 3 761115.162 Prob > F = 0.0000 Residual | 5790326.51 396 14622.0366 R-squared = 0.2828 -------------+------------------------------ Adj R-squared = 0.2774 Total | 8073672.00 399 20234.7669 Root MSE = 120.92 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Iyr_rnd_2 | -248.0712 29.85895 -8.31 0.000 -306.7731 -189.3694 some_col | 1.409427 .5856022 2.41 0.017 .2581494 2.560705 _Iyr_Xsome~2 | 5.99319 1.57715 3.80 0.000 2.892557 9.093824 _cons | 655.1103 14.03499 46.68 0.000 627.5179 682.7027 ------------------------------------------------------------------------------

The **i.yr_rnd*some_col** term creates 3 terms, **some_col**, **_Iyr_rnd_2**
an indicator variable for yr_rnd representing whether the school is year round and the
variable **_Iyr_Xsome~2** representing the interaction of **yr_rnd** by **some_col**.

As we did above, we can create predicted values and create graphs showing the regression lines for the two types of schools. We omit showing these commands.

**3.7.3 Computing interactions with anova**

We can also run a model just like the model we showed above using the **anova** command.
We include the terms **yr_rnd** **some_col** and the interaction **yr_rnr*some_col**

anova api00 yr_rnd some_col yr_rnd*some_col, contin(some_col)Number of obs = 400 R-squared = 0.2828 Root MSE = 120.922 Adj R-squared = 0.2774 Source | Partial SS df MS F Prob > F ----------------+---------------------------------------------------- Model | 2283345.48 3 761115.162 52.05 0.0000 | yr_rnd | 1009279.99 1 1009279.99 69.02 0.0000 some_col | 456473.187 1 456473.187 31.22 0.0000 yr_rnd*some_col | 211143.646 1 211143.646 14.44 0.0002 | Residual | 5790326.51 396 14622.0366 ----------------+---------------------------------------------------- Total | 8073672.00 399 20234.7669

As we illustrated above, we can compute the predicted values using the predict command and graph the separate regression lines. These commands are omitted.

In this section we found that the relationship between **some_col** and **api00**
depended on whether the school is a year round school or a non-year round
school. For the year round schools, the relationship between **some_col**
and **api00** was significantly stronger than for non-year round
schools. In general, this type of analysis allows you to test whether the strength
of the relationship between two continuous variables varies based on the categorical
variable.

**3.8 Continuous and Categorical variables, interaction with 1/2/3 variable**
The prior examples showed how to do regressions with a continuous variable and a
categorical variable that has 2 levels. These examples will extend this further by
using a categorical variable with 3 levels, **mealcat**.

**3.8.1 using xi**

We can use the **xi** command to run a model with **some_col**, **mealcat** and
the interaction of these two variables.

xi : regress api00 i.mealcat*some_coli.mealcat _Imealcat_1-3 (naturally coded; _Imealcat_1 omitted) i.meal~t*some~l _ImeaXsome__# (coded as above) Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 5, 394) = 263.00 Model | 6212306.88 5 1242461.38 Prob > F = 0.0000 Residual | 1861365.12 394 4724.27696 R-squared = 0.7695 -------------+------------------------------ Adj R-squared = 0.7665 Total | 8073672.00 399 20234.7669 Root MSE = 68.733 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Imealcat_2 | -239.03 18.66502 -12.81 0.000 -275.7255 -202.3345 _Imealcat_3 | -344.9476 17.05743 -20.22 0.000 -378.4825 -311.4126 some_col | -.9473385 .4873679 -1.94 0.053 -1.905505 .0108284 _ImeaXsome~2 | 3.140945 .7292897 4.31 0.000 1.707159 4.57473 _ImeaXsome~3 | 2.607308 .8960435 2.91 0.004 .8456841 4.368933 _cons | 825.8937 11.99182 68.87 0.000 802.3177 849.4697 ------------------------------------------------------------------------------

The interaction now has two terms (**_ImeaXsome~2** and **_ImeaXsome~3**). To
get an overall test of this interaction, we can use the **test** command.

test _ImeaXsome__2 _ImeaXsome__3( 1) _ImeaXsome__2 = 0.0 ( 2) _ImeaXsome__3 = 0.0 F( 2, 394) = 10.32 Prob > F = 0.0000

These results indicate that the overall interaction is indeed significant. This means that the regression lines from the 3 groups differ significantly. As we have done before, let’s compute the predicted values and make a graph of the predicted values so we can see how the regression lines differ.

predict yhatc(option xb assumed; fitted values)separate yhatc, by(mealcat)storage display value variable name type format label variable label ------------------------------------------------------------------------------- yhatc1 float %9.0g yhatc, mealcat == 1 yhatc2 float %9.0g yhatc, mealcat == 2 yhatc3 float %9.0g yhatc, mealcat == 3

Since we had three groups, we get three regression lines, one for each category of **mealcat**.
The solid line is for group 1, the dashed line for group 2, and the dotted line is for
group 3.

line yhatc1 yhatc2 yhatc3 some_col, sort

Group 1 was the omitted group, therefore the slope of the line for group 1 is the
coefficient for **some_col** which is -.94. Indeed, this line has a downward
slope. If we add the coefficient for **some_col** to the coefficient for **_ImeaXsome~2**
we get the coefficient for group 2, i.e., 3.14 + -.94 yields 2.2, the slope for group 2.
Indeed, group 2 shows an upward slope. Likewise, if we add the coefficient for **some_col**
to the coefficient for **_ImeaXsome~3** we get the coefficient for group 3, i.e., 2.6 +
-.94 yields 1.66, the slope for group 3,. So, the slopes for the 3 groups are

group 1: -0.94 group 2: 2.2 group 3: 1.66

The test of the coefficient for **_ImeaXsome~2 **tested whether the coefficient for
group 2 differed from group 1, and indeed this was significant. Likewise, the test
of the coefficient for **_ImeaXsome~3 **tested whether the coefficient for group 3
differed from group 1, and indeed this was significant. What did the test of the
coefficient **some_col** test? This coefficient represents the coefficient for
group 1, so this tested whether the coefficient for group 1 (-0.94) was significantly
different from 0. This is probably a non-interesting test.

The comparisons in the above analyses don’t seem to be as interesting as comparing
group 1 vs. 2 and then comparing group 2 vs. 3. These successive comparisons seem
much more interesting. We can do this by making group 2 the omitted group, and then
each group would be compared to group 2. As we have done before, we will use the **char**
command to indicate that we want group 2 to be the omitted category and then rerun the
regression.

char mealcat[omit] 2 xi : regress api00 i.mealcat*some_coli.mealcat _Imealcat_1-3 (naturally coded; _Imealcat_2 omitted) i.meal~t*some~l _ImeaXsome__# (coded as above) Source | SS df MS Number of obs = 400 -------------+------------------------------ F( 5, 394) = 263.00 Model | 6212306.88 5 1242461.38 Prob > F = 0.0000 Residual | 1861365.12 394 4724.27696 R-squared = 0.7695 -------------+------------------------------ Adj R-squared = 0.7665 Total | 8073672.00 399 20234.7669 Root MSE = 68.733 ------------------------------------------------------------------------------ api00 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- _Imealcat_1 | 239.03 18.66502 12.81 0.000 202.3345 275.7255 _Imealcat_3 | -105.9176 18.7545 -5.65 0.000 -142.789 -69.0462 some_col | 2.193606 .5425274 4.04 0.000 1.126996 3.260217 _ImeaXsome~1 | -3.140945 .7292897 -4.31 0.000 -4.57473 -1.707159 _ImeaXsome~3 | -.5336362 .9272014 -0.58 0.565 -2.356517 1.289245 _cons | 586.8637 14.30311 41.03 0.000 558.7438 614.9837 ------------------------------------------------------------------------------

Now, the test of **_ImeaXsome~1** tests whether the coefficient for group 1 differs
from group 2, and it does. Then, the test of **_ImeaXsome~3** tests whether the
coefficient for group 3 significantly differs from group 2, and it does not. This makes
sense given the graph and given the estimates of the coefficients that we have, that -.94
is significantly different from 2.2 but 2.2 is not significantly different from
1.66.

**3.8.2 Using Anova**

We can perform the same analysis using the **anova** command, as shown below.
The **anova** command gives us somewhat less flexibility since we cannot choose which group is
the omitted group.

use elemapi2, clear anova api00 mealcat some_col mealcat*some_col, cont(some_col)Number of obs = 400 R-squared = 0.7695 Root MSE = 68.7334 Adj R-squared = 0.7665 Source | Partial SS df MS F Prob > F -----------------+---------------------------------------------------- Model | 6212306.88 5 1242461.38 263.00 0.0000 | mealcat | 2012065.49 2 1006032.75 212.95 0.0000 some_col | 36366.3662 1 36366.3662 7.70 0.0058 mealcat*some_col | 97468.1685 2 48734.0843 10.32 0.0000 | Residual | 1861365.12 394 4724.27696 -----------------+---------------------------------------------------- Total | 8073672.00 399 20234.7669

Because the **anova** command omits the 3rd category, and the analysis we showed
above omitted the second category, the parameter estimates will not be the same. You can
compare the results from below with the results above and see that the parameter estimates
are not the same. Because group 3 is dropped, that is the reference category and all
comparisons are made with group 3.

anova, regressSource | SS df MS Number of obs = 400 -------------+------------------------------ F( 5, 394) = 263.00 Model | 6212306.88 5 1242461.38 Prob > F = 0.0000 Residual | 1861365.12 394 4724.27696 R-squared = 0.7695 -------------+------------------------------ Adj R-squared = 0.7665 Total | 8073672.00 399 20234.7669 Root MSE = 68.733 ------------------------------------------------------------------------------ api00 Coef. Std. Err. t P>|t| [95% Conf. Interval] ------------------------------------------------------------------------------ _cons 480.9461 12.13063 39.65 0.000 457.0973 504.795 meals3 1 344.9476 17.05743 20.22 0.000 311.4126 378.4825 2 105.9176 18.7545 5.65 0.000 69.0462 142.789 3 (dropped) some_col 1.65997 .7519086 2.21 0.028 .1817153 3.138225 meals3*some_col 1 -2.607308 .8960435 -2.91 0.004 -4.368933 -.8456841 2 .5336362 .9272014 0.58 0.565 -1.289245 2.356517 3 (dropped) ------------------------------------------------------------------------------

These analyses showed that the relationship between **some_col** and **api00**
varied, depending on the level of **mealcat**. In comparing group 1 with group 2,
the coefficient for **some_col** was significantly different, but there was no
difference in the coefficient for **some_col** in comparing groups 2 and 3.

3.9 Summary

This covered four techniques for analyzing data with categorical variables, 1)
manually constructing indicator variables, 2) creating indicator variables using the **xi**
command, 3) coding variables using **xi3**, and 4) using the **anova** command.
Each method has its advantages and disadvantages, as described below.

Manually constructing indicator variables can be very tedious and even error prone. For very simple models, it is not very difficult to create your own indicator variables, but if you have categorical variables with many levels and/or interactions of categorical variables, it can be laborious to manually create indicator variables. However, the advantage is that you can have quite a bit of control over how the variables are created and the terms that are entered into the model.

The **xi** command can really ease the creation of indicator variables,
and make it easier to include interactions in your models by allowing you to include
interaction terms such as i.prog*female . The **xi** command also gives you the
flexibility to decide which category would be the omitted category (unlike the **anova**
command).

The **anova** command eliminates the need to create indicator variables
making it easy to include variables that have lots of categories, and making it easy to
create interactions by allowing you to include terms like **some_col*mealcat**.
It can be easier to perform tests of simple main effects with the **anova**
command. However, the **anova** command is not flexible in letting you choose
which category is the omitted category (the last category is always the omitted category).

As you will see in the next chapter, the regress command includes additional options
like the **robust** option and the **cluster** option that allow
you to perform analyses when you don’t exactly meet the assumptions of ordinary least
squares regression. In such cases, the **regress** command offers
features not available in the **anova** command and may be more advantageous
to use.

**3.10 Self Assessment**

1. Using the **elemapi2** data file ( use http://www.ats.ucla.edu/stat/stata/webbooks/reg/elemapi2
) convert the variable **ell** into 2 categories using the
following coding, 0-25 on **ell** becomes 0, and 26-100 on **ell**
becomes 1. Use this recoded version of **ell** to predict **api00**
and interpret the results.

2. Convert the variable **ell** into 3 categories coding those
scoring 0-14 on ell as 1, and those 15/41 as 2 and 42/100 as 3. Do an analysis
predicting **api00** from the **ell** variable
converted to a 1/2/3 variable. Interpret the results.

3. Do a regression analysis predicting **api00** from **yr_rnd**
and the **ell** variable converted to a 0/1 variable. Then create
an interaction term and run the analysis again. Interpret the results of these
analyses.

4. Do a regression analysis predicting **api00** from **ell**
coded as 0/1 (from question 1) and **some_col**, and the
interaction of these two variables. Interpret the results, including showing a
graph of the results.

5. Use the variable **ell** converted into 3 categories (from
question 2) and predict api00 from **ell** in 3 categories, from **some_col**
and the interaction. of these two variables. Interpret the results, including
showing a graph.

Click here for our answers to these self assessment questions.

**3.11 For more information**

- Stata Manuals
**[R] xi****[R] anova****[R] test**

- Web Links
- Creating Dummy Variables
**–**Stata FAQ- How can I create dummy variables in Stata - Models with interactions of continuous and categorical variables – Stata FAQ- How can I compare regression coefficients between 2 groups – Stata FAQ- How can I compare regression coefficients across 3 (or more) groups
- Other- Stata FAQ: How can I form various tests comparing the different levels of a categorical variable after anova or regress? – Stata FAQ- Why do estimation commands sometimes drop variables (from Stata FAQs)

- Creating Dummy Variables