# REGRESSION LINES IN STATA

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1 REGRESSION LINES IN STATA THOMAS ELLIOTT 1. Introduction to Regression Regression analysis is about eploring linear relationships between a dependent variable and one or more independent variables. Regression models can be represented by graphing a line on a cartesian plane. Think back on your high school geometry to get you through this net part. Suppose we have the following points on a line: y What is the equation of the line? y = α + β β = y = = 2 α = y β = 3 2(3) = 3 y = If we input the data into STATA, we can generate the coefficients automatically. The command for finding a regression line is regress. The STATA output looks like: Date: January 3,

2 2 THOMAS ELLIOTT. regress y Source SS df MS Number of obs = F( 1, 3) =. Model Prob > F =. Residual 3 R-squared = Adj R-squared = 1. Total Root MSE = y Coef. Std. Err. t P> t [95% Conf. Interval] _cons The first table shows the various sum of squares, degrees of freedom, and such used to calculate the other statistics. In the top table on the right lists some summary statistics of the model including number of observations, R 2 and such. However, the table we will focus most of our attention on is the bottom table. Here we find the coefficients for the variables in the model, as well as standard errors, p-values, and confidence intervals. In this particular regression model, we find the coefficient (β) is equal to 2 and the constant (α) is -3. This matches the equation we calculated earlier. Notice that no standard errors are reported. This is because the data fall eactly on the line so there is zero error. Also notice that the R 2 term is eactly equal to 1., indicating a perfect fit. Now, let s work with some data that are not quite so neat. data. use hire771. regress salary age We ll use the hire771.dta Source SS df MS Number of obs = F( 1, 3129) = Model Prob > F =. Residual R-squared = Adj R-squared =.867 Total Root MSE = salary Coef. Std. Err. t P> t [95% Conf. Interval] age _cons The table here is much more interesting. We ve regressed age on salary. The coefficient on age is 2.34 and the constant is 93.8 giving us an equation of:

3 REGRESSION LINES IN STATA 3 salary = age How do we interpret this? For every year older someone is, they are epected to receive another \$2.34 a week. A person with age zero is epected to make \$93.8 a week. We can find the salary of someone given their age by just plugging in the numbers into the above equation. So a 25 year old is epected to make: salary = (25) = Looking back at the results tables, we find more interesting things. We have standard errors for the coefficient and constant because the data are messy, they do not fall eactly on the line, generating some error. If we look at the R 2 term,.87, we find that this line is not a very good fit for the data.

4 4 THOMAS ELLIOTT 2. Testing Assumptions The OLS regression model requires a few assumptions to work. These are primarily concerned with the residuals of the model. The residuals are the same as the error - the vertical distance of each data point from the regression line. The assumptions are: Homoscedasticity - the probability distribution of the errors has constant variance Independence of errors - the error values are statistically independent of each other Normality of error - error values are normally distributed for any given value of The easiest way to test these assumptions are simply graphing the residuals on and see what patterns emerge. You can have STATA create a new variable containing the residual for each case after running a regression using the predict command with the residual option. Again, you must first run a regression before running the predict command. regress y predict res1, r You can then plot the residuals on in a scatterplot. Below are three eamples of scatterplots of the residuals Residuals Residuals Residuals Residuals Residuals Residuals (a) What you want to see (b) Not Homoscedastic (c) Not Independent Figure (A) above shows what a good plot of the residuals should look like. The points are scattered along the ais fairly evenly with a higher concentration at the ais. Figure (B) shows a scatter plot of residuals that are not homoscedastic. The variance of the residuals increases as increases. Figure (C) shows a scatterplot in which the residuals are not independent - they are following a non-linear trend line along. This can happen if you are not specifying your model correctly (this plot comes from trying to fit a linear regression model to data that follow a quadratic trend line). If you think the residuals ehibit heteroscedasticity, you can test for this using the command estat hettest after running a regression. It will give you a chi 2 statistic and a p-value. A low p-value indicates the likelihood that the data is heteroscedastic. The consequences of heteroscedasticity in your model is mostly minimal. It will not bias your coefficients but it may bias your standard errors, which are used in calculating the test statistic and p-values for each coefficient. Biased standard errors may lead to finding significance for your coefficients when there isn t any (making a type I error). Most statisticians will tell

5 REGRESSION LINES IN STATA 5 you that you should only worry about heteroscedasticity if it is pretty severe in your data. There are a variaty of fies (most of them complicated) but one of the easiest is specifying vce(robust) as an option in your regression command. This uses a more robust method to calculate standard errors that is less likely to be biased by a number of things, including heteroscedasticity. If you find a pattern in the residual plot, then you ve probably misspecified your regression model. This can happen when you try to fit a linear model to non-linear data. Take another look at the scatterplots for your dependent and independent variables to see if any non-linear relationships emerge. We ll spend some time in future labs going over how to fit non-linear relationships with a regression model. To test for normality in the residuals, you can generate a normal probability plot of the residuals: pnorm varname Normal F[(res1-m)/s] Empirical P[i] = i/(n+1) Normal F[(res1-m)/s] Normal F[(res4-m)/s] Normal F[(res4-m)/s] Empirical P[i] = i/(n+1) Empirical P[i] = i/(n+1) Empirical P[i] = i/(n+1) (d) Normally Distributed (e) Not Normal What this does is plot the cumulative distribution of the data against a cumulative distribution of normally distributed data with similar means and standard deviation. If the data are normally distributed, then the plot should create a straight line. The resulting graph will produce a scatter plot and a reference line. Data that are normally distributed will not deviate far from the reference line. Data that are not normally distributed will deviate. In the figures above, the graph on the left depicts normally distributed data (the residuals in (A) above). The graph on the right depicts non-normally distributed data (the residuals in (C) above). Depending on how far the plot deviates from the reference line, you may need to use a different regression model (such as poisson or negative binomial).

6 6 THOMAS ELLIOTT 3. Interpreting Coefficients Using the hire771 dataset, the average salary for men and women is: Table 1. Average Salary Avg. Salary Male Female Total We can run a regression of salary on se with the following equation: Salary = α + βse Salary = Se Remember that regression is a method of averages, predicting the average salary given values of. So from this equation, we can calculate what the predicted average salary for men and women would be from this equation: Table 2. Predicted Salary equation predicted salary Male = α Female = 1 α + β How might we interpret these coefficients? We can see that α is equal to the average salary for men. α is always the predicted average salary when all values are equal to zero. β is the effect that has on y. In the above equation, is only ever zero or one so we can interpret the β as the effect on predicted average salary when is one. So the predicted average salary when is zero, or for men, is \$ a week. When is one, or for women, the average predicted salary decreases by \$72.83 a week (remember that β is negative). So women are, on average, making \$72.83 less per week than men. Remember: this only works for single regression with a dummy variable. Using a continuous variable or including other independent variables will not yield cell averages. Quickly, let s see what happens when we include a second dummy variable: Salary = α + β se se + β HO HO Salary = se HO

7 REGRESSION LINES IN STATA 7 Table 3. Average Salaries Male Female Field Office Home Office Table 4. Predicted Salaries Field Office Male Female α α + β se Home Office α + β HO α + β se + β HO We can see that the average salaries are not given using the regression method. As we learned in lecture, this is because we only have three coefficients to find four average salaries. More intuitively, the regression is assuming equal slopes for the four different groups. In other words, the effect of se on salary is the same for people in the field office and in the home office. Additionally, the effect of office location is the same for both men and women. If we want the regression to accurately reflect the cell averages, we should allow the slope of one variable to vary for the categories of the other variables by including an interaction term (see interaction handout). Including an interacting term between se and home office will reproduce the cell averages accurately.

8 8 THOMAS ELLIOTT 4. Multiple Regression So far, we ve been talking about single regression with only one independent variable. For eample, we ve regressed salary on age: Source SS df MS Number of obs = F( 1, 3129) = Model Prob > F =. Residual R-squared = Adj R-squared =.867 Total Root MSE = salary Coef. Std. Err. t P> t [95% Conf. Interval] age _cons There are a couple problems with this model, though. First, it is not a very good fit (R 2 =.87). Second, there are probably confounding variables. For eample, education may be confounded with age (the older you are, the more education you are likely to have). So it makes sense to try to control for one while regressing salary on the other. This is multiple regression. In STATA, we simply include all the independent variables after the dependent variable:. regress salary age educ Source SS df MS Number of obs = F( 2, 3128) = Model Prob > F =. Residual R-squared = Adj R-squared =.2493 Total Root MSE = salary Coef. Std. Err. t P> t [95% Conf. Interval] age educ _cons In the model above, we are controlling for education when analyzing age. We are also controlling for age when analyzing education. So, we can say that age being equal, for every advance in education, someone can epect to make \$13 more a week. We can also say that education being equal, for every year older someone is they can epect to make \$2 more a week. The effect of one variable controlled for when analyzing the other variable. One analogy would be that the regression model is dividing everyone up into similar age groups and then analyzing the effect of education within each group. At the same time, the model

9 REGRESSION LINES IN STATA 9 is dividing everyone into groups of similar education and then analyzing the effect of age within each group. This isn t a perfect analogy, but it helps to visualize what it means when we say the regression model is finding the effect of age on salary controlling for education and vice versa. This is true of all the variables you include in your model. For eample, maybe education doesn t have anything to do with salary, its just that men are more educated than women and so the more salient variable determining your salary is se. We can find out the effect of education controlling for se by including the se variable:. regress salary age educ se Source SS df MS Number of obs = F( 3, 3127) = 488. Model Prob > F =. Residual R-squared = Adj R-squared =.3182 Total Root MSE = salary Coef. Std. Err. t P> t [95% Conf. Interval] age educ se _cons Here, we can describe the effects of the variables in the following way: When comparing men and women with similar ages and educations, women are epected to make \$53 less a week than men. Comparing people who are the same se and have the same education, each year older confers an additional \$2.14 per week on average. Comparing people who are the same se and age, each additional advancement in education confers, on average, an additional \$1.24 more per week. Comparing the two models, we see that the effect of education decreases slightly when we include se, but the education variable is still significant (p <.). Additionally, if we ran a regression model with just se, the se coefficient would be (the difference in average salary for men and women) but here the coefficient is only 53. indicating that age and education eplain a fair amount of the difference in the salaries of men and women.

10 1 THOMAS ELLIOTT 5. Quadratic Terms Normal OLS regression assumes a linear relationship between the dependent variable and the independent variables. When you run a regression of y on, the model assumes that y increases at the same rate for each value of. But what happens when this isn t true. Assume a dataset in which the relationship between and y is quadratic rather than linear. The scatterplot for such data may look like Figure 1. Figure 1. Scatterplot y yfitted values y Fitted values Briefly, let s review the algebra behind quadratic equations. Quadratic equations describe parabolas, which are part of the family of conic shapes (along with circles, ellipses, and hyperbolas). The quadratic equation typically looks like: y = a 2 + b + c c is our y-intercept (or our α). If a is positive, the parabola opens up (forms a bowl). If a is negative, the parabola opens down (forms a hill). The inflection point is the point at which the sign of the slope changes (from negative to positive or vice versa). So in the scatterplot above, we should epect the coefficient on the 2 term to be negative. As I said before, normal OLS regression assumes a linear relationship so if we were to run a regression of y on alone, we get the results in Figure 2. The linear equation is included in the scatter plot in Figure 1. Notice that in the regression output, the coefficient on is very small and not statistically significantly different from zero. Essentially, the linear model says that has no effect on y. However, we can clearly see that and y do have a relationship, it simply is not linear. How do we specify a proper model for this data?

11 REGRESSION LINES IN STATA 11. regress y Figure 2. Linear Regression Model Source SS df MS Number of obs = F( 1, 498) =.4 Model Prob > F =.8455 Residual R-squared = Adj R-squared = -.19 Total Root MSE =.831 y Coef. Std. Err. t P> t [95% Conf. Interval] _cons We can write the quadratic equation in regression parlance: y = α + β 1 + β 2 ( ) So we include the variable twice, once by itself and a second time multiplied by itself once. In STATA, we can generate that quadratic term easily: gen 2 = * And then include it in the regression (see Figure 3). regress y 2 Figure 3. Quadratic Regression Model Source SS df MS Number of obs = F( 2, 497) = Model Prob > F =. Residual R-squared = Adj R-squared =.8696 Total Root MSE =.2897 y Coef. Std. Err. t P> t [95% Conf. Interval] _cons And we see that this model does much better at eplaining the data. Both the and the 2 terms are significant and our R 2 is fairly high at.87. The regression equation for this model would be:

12 12 THOMAS ELLIOTT y = Graphing the fitted regression line to the scatter plot shows a much better description of the data (see Figure 4). Figure 4. Scatterplot with Fitted Line y yfitted values y Fitted values Note: You must include both orders of your independent variable in a quadratic model. Do not include just the quadratic term - this is wrong and misspecifies the model. To learn how to interpret the coefficients, let s try this with some real data, using the hire771 dataset. First, regress salary and age (See Figure 5). We ve seen this before but let s review interpreting the coefficients. First, our regression equation is: Salary = age So for every year older someone is, the average starting salary increases by \$2.34 per week. 2 year olds average starting salary: (2) = or \$14.55 a week. 5 year olds average starting salary: (5) = or \$21.63 a week.

13 REGRESSION LINES IN STATA 13. regress salary age Figure 5. Salary on Age; Linear Source SS df MS Number of obs = F( 1, 3129) = Model Prob > F =. Residual R-squared = Adj R-squared =.867 Total Root MSE = salary Coef. Std. Err. t P> t [95% Conf. Interval] age _cons Now, as Prof. Noymer pointed out in lecture, age often has diminishing returns, indicating a potential quadratic relationship. Let s generate a quadratic age variable and run a regression (See Figure 6).. gen age2 = age*age Figure 6. Salary on Age; Quadratic. regress salary age age2 Source SS df MS Number of obs = F( 2, 3128) = Model Prob > F =. Residual R-squared = Adj R-squared =.134 Total Root MSE = salary Coef. Std. Err. t P> t [95% Conf. Interval] age age _cons We interpret the age coefficient the same as before, but the age2 coefficient requires some eplaining. First, the regression equation is: Salary = Age.152 Age 2 As before, we can say that for each age older someone is, their average starting salary will increase by \$12.63 a week. The age2 coefficient we can think of as an interaction effect of

14 14 THOMAS ELLIOTT age on itself. For each year older someone is, the effect of age decreases by.152 so the difference in average starting salary for 21 year olds over 2 year olds is bigger than the difference between 51 and 5. Average starting salary for 2 year olds is: (2).152(2 2 ) = Average starting salary for 5 year olds is: (5).152(5 2 ) = Plotting the regression line along with the scatter plot shows a better description of the relationship than a linear model (See Figure 7). Figure 7. Salary on Age Scatterplot age salary Fitted values age 5 6 salary Fitted values NOTE: You do not need to know how to do the following, but I include it for those who are curious. To find the effect of age on salary at any specific age, you will need to derive the derivative of the regression equation. Remember that the derivative of an equation gives you the slope of the curve at any given point. So the derivative of our regression equation is: y = dy = (.152) d dy = d Now we can plug any age into the derivative equation above and it will give us the effect of age on salary at that age. So we plug in 2 and that will give us the effect of age on salary

15 REGRESSION LINES IN STATA 15 when one moves from 2 to 21 (more or less). Similarly, plugging in 5 will give us the effect of age on salary when one moves from 5 to 51: dy d dy d = (2) = = (5) = So according to the results above, moving from 2 to 21 is epected to add \$6.55 per week to one s salary on average. However, moving from 5 to 51 is epected to subtract \$2.57 per week from one s salary on average. So age is beneficial to salary until some age between 2 and 5, after which increases in age will decrease salary. The point at which the effect of age switches from positive to negative is the inflection point (the very top of the curve). The effect of age at this point will be zero as it transitions from positive to negative. We can find the age at which this happens by substituting in zero for the slope: = = = So increases in age adds to one s salary until 41 years, after which age subtracts from salary. Remember: this data is starting salary for 3 new employees at the firm so people are not getting pay cuts when they get older. Rather, the firm is offering lower starting salaries to people who are older than 41 years than people who are around 41 years old.

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