Simple linear regression

In [1]:
options(repr.plot.width=5, repr.plot.height=3)

The first type of model, which we will spend a lot of time on, is the simple linear regresssion model. One simple way to think of it is via scatter plots. Below are heights of mothers and daughters collected by Karl Pearson in the late 19th century.

In [2]:
M = heights$Mheight
D = heights$Dheight
Loading required package: car
Warning message:
“package ‘car’ was built under R version 3.3.2”
In [3]:
heights_fig = ggplot(heights, aes(Mheight, Dheight)) + geom_point() + theme_bw();

A simple linear regression model fits a line through the above scatter plot in a particular way. Specifically, it tries to estimate the height of a new daughter in this population, say $D_{new}$, whose mother had height $H_{new}$. It does this by considering each slice of the data. Here is a slice of the data near $M=66$, the slice is taken over a window of size 1 inch.

In [4]:
X = 66
rect = data.frame(xmin=X-.5, xmax=X+.5, ymin=-Inf, ymax=Inf)
heights_fig + geom_rect(data=rect, aes(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax),
                        inherit.aes = FALSE)
In [5]:
selected_points = (M <= X+.5) & (M >= X-.5)
mean_within_slice = mean(D[selected_points])

We see that, in our sample, the average height of daughters whose height fell within our slice is about 65.2 inches. Of course this height varies by slice. For instance, at 60 inches:

In [6]:
X = 60
selected_points = (M <= X+.5) & (M >= X-.5)
mean_within_slice = mean(D[selected_points])
In [7]:
X = 60
rect = data.frame(xmin=X-.5, xmax=X+.5, ymin=-Inf, ymax=Inf)
heights_fig + geom_rect(data=rect, aes(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax),
                        inherit.aes = FALSE)

The regression model puts a line through this scatter plot in an optimal fashion.

In [8]:
parameters = lm(D ~ M)$coef
intercept = parameters[1]
slope = parameters[2]
(Intercept)           M 
  29.917437    0.541747 
In [9]:
heights_fig + geom_abline(slope=slope, intercept=intercept, color='red', size=3)

What is a "regression" model?

A regression model is a model of the relationships between some covariates (predictors) and an outcome. Specifically, regression is a model of the average outcome given the covariates.

Mathematical formulation

For height of couples data: a mathematical model: $$ {\tt Daughter} = f({\tt Mother}) + \varepsilon $$ where $f$ gives the average height of the daughter of a mother of height Mother and $\varepsilon$ is the random variation within the slice.

Linear regression models

  • A linear regression model says that the function $f$ is a sum (linear combination) of functions of ${\tt Mother}$.

  • Simple linear regression model: $$f({\tt Mother}) = \beta_0 + \beta_1 \cdot {\tt Mother}$$ for some unknown parameter vector $(\beta_0, \beta_1)$.

  • Could also be a sum (linear combination) of fixed functions of Mother: $$f({\tt Mother}) = \beta_0 + \beta_1 \cdot {\tt Mother} + \beta_2 \cdot {\tt Mother}^2 $$

Simple linear regression model

  • Simple linear regression is the case when there is only one predictor: $$ f({\tt Mother}) = \beta_0 + \beta_1 \cdot {\tt Mother}.$$
  • Let $Y_i$ be the height of the $i$-th daughter in the sample, $X_i$ be the height of the $i$-th mother.

  • Model: $$ Y_i = \underbrace{\beta_0 + \beta_1 X_i}_{\text{regression equation}} + \underbrace{\varepsilon_i}_{\text{error}}$$ where $\varepsilon_i \sim N(0, \sigma^2)$ are independent.

  • This specifies a distribution for the $Y$'s given the $X$'s, i.e. it is a statistical model.

Fitting the model

  • We will be using least squares regression. This measures the goodness of fit of a line by the sum of squared errors, $SSE$.

  • Least squares regression chooses the line that minimizes $$ SSE(\beta_0, \beta_1) = \sum_{i=1}^n (Y_i - \beta_0 - \beta_1 \cdot X_i)^2.$$

  • In principle, we might measure goodness of fit differently: $$ SAD(\beta_0, \beta_1) = \sum_{i=1}^n |Y_i - \beta_0 - \beta_1 \cdot X_i|.$$

  • For some loss function $L$ we might try to minimize $$ L(\beta_0,\beta_1) = \sum_{i=1}^n L(Y_i-\beta_0-\beta_1X_i) $$

Why least squares?

  • With least squares, the minimizers have explicit formulae -- not so important with today's computer power -- especially when $L$ is convex.

  • Resulting formulae are linear in the outcome $Y$. This is important for inferential reasons. For only predictive power, this is also not so important.

  • If assumptions are correct, then this is maximum likelihood estimation.

  • Statistical theory tells us the maximum likelihood estimators (MLEs) are generally good estimators.

Choice of loss function

The choice of the function we use to measure goodness of fit, or the loss function, has an outcome on what sort of estimates we get out of our procedure. For instance, if, instead of fitting a line to a scatterplot, we were estimating a center of a distribution, which we denote by $\mu$, then we might consider minimizing several loss functions.

Choice of loss function

  • If we choose the sum of squared errors: $$ SSE(\mu) = \sum_{i=1}^n (Y_i - \mu)^2. $$ Then, we know that the minimizer of $SSE(\mu)$ is the sample mean.

  • On the other hand, if we choose the sum of the absolute errors $$ SAD(\mu) = \sum_{i=1}^n |Y_i - \mu|.$$ Then, the resulting minimizer is the sample median.

  • Both of these minimization problems also have population versions as well. For instance, the population mean minimizes, as a function of $\mu$ $$ \mathbb{E}((Y-\mu)^2) $$ while the population median minimizes $$ \mathbb{E}(|Y-\mu|). $$

Visualizing the loss function

Let's take some a random scatter plot and view the loss function.

In [10]:
X = rnorm(50)
Y = 1.5 + 0.1 * X + rnorm(50) * 2
parameters = lm(Y ~ X)$coef
intercept = parameters[1]
slope = parameters[2]
ggplot(data.frame(X, Y), aes(X, Y)) + geom_point() + geom_abline(slope=slope, intercept=intercept)

Let's plot the loss as a function of the parameters. Note that the true intercept is 1.5 while the true slope is 0.1.

In [11]:
grid_intercept = seq(intercept - 2, intercept + 2, length=100)
grid_slope = seq(slope - 2, slope + 2, length=100)
loss_data = expand.grid(intercept_=grid_intercept, slope_=grid_slope)
loss_data$squared_error = numeric(nrow(loss_data))
for (i in 1:nrow(loss_data)) {
    loss_data$squared_error[i] = sum((Y - X * loss_data$slope_[i] - loss_data$intercept_[i])^2)
squared_error_fig = (ggplot(loss_data, aes(intercept_, slope_, fill=squared_error)) + 
                     geom_raster() +

Let's contrast this with the sum of absolute errors.

In [12]:
loss_data$absolute_error = numeric(nrow(loss_data))
for (i in 1:nrow(loss_data)) {
    loss_data$absolute_error[i] = sum(abs(Y - X * loss_data$slope_[i] - loss_data$intercept_[i]))
absolute_error_fig = (ggplot(loss_data, aes(intercept_, slope_, fill=absolute_error)) + 
                      geom_raster() +

Geometry of least squares

The following picture will be with us, in various guises, throughout much of the course. It depicts the geometric picture involved in least squares regression.

It requires some imagination but the picture should be thought as representing vectors in $n$-dimensional space, l where $n$ is the number of points in the scatterplot. In our height data, $n=1375$. The bottom two axes should be thought of as 2-dimensional, while the axis marked "$\perp$" should be thought of as $(n-2)$ dimensional, or, 1373 in this case.

Important lengths

The (squared) lengths of the above vectors are important quantities in what follows.

There are three to note: $$ \begin{aligned} SSE &= \sum_{i=1}^n(Y_i - \widehat{Y}_i)^2 = \sum_{i=1}^n (Y_i - \widehat{\beta}_0 - \widehat{\beta}_1 X_i)^2 \\ SSR &= \sum_{i=1}^n(\overline{Y} - \widehat{Y}_i)^2 = \sum_{i=1}^n (\overline{Y} - \widehat{\beta}_0 - \widehat{\beta}_1 X_i)^2 \\ SST &= \sum_{i=1}^n(Y_i - \overline{Y})^2 = SSE + SSR \\ R^2 &= \frac{SSR}{SST} = 1 - \frac{SSE}{SST} = \widehat{Cor}(\pmb{X},\pmb{Y})^2. \end{aligned} $$

Important lengths

An important summary of the fit is the ratio $$ R^2 = \frac{SSR}{SST} = 1 - \frac{SSE}{SST} $$ which measures how much variability in $Y$ is explained by $X$.

Example: wages vs. experience

In this example, we'll look at the output of lm for the wage data and verify that some of the equations we present for the least squares solutions agree with the output. The data was compiled from a study in econometrics Learning about Heterogeneity in Returns to Schooling.

In [13]:
url = '/Users/jonathantaylor/Desktop//wage.csv'
wages = read.table(url, sep=',', header=TRUE)
  education  logwage
1  16.75000 2.845000
2  15.00000 2.446667
3  10.00000 1.560000
4  12.66667 2.099167
5  15.00000 2.490000
6  15.00000 2.330833

In order to access the variables in wages we attach it so that the variables are in the toplevel namespace.

In [14]:

Let's fit the linear regression model.

In [15]:
wages.lm = lm(logwage ~ education)
lm(formula = logwage ~ education)

(Intercept)    education  
     1.2392       0.0786  

As in the mother-daughter data, we might want to plot the data and add the regression line.

Earlier, we used ggplot2, below we use base R instead. Typically ggplot2 will be more attractive, though its result are sometimes a little difficult to tweak (in my limited experience).

In [16]:
logwage_fig = (ggplot(wages, aes(education, logwage)) + geom_point() + theme_bw() +

Least squares estimators

There are explicit formulae for the least squares estimators, i.e. the minimizers of the error sum of squares.

For the slope, $\hat{\beta}_1$, it can be shown that $$ \widehat{\beta}_1 = \frac{\sum_{i=1}^n(X_i - \overline{X})(Y_i - \overline{Y} )}{\sum_{i=1}^n (X_i-\overline{X})^2} = \frac{\widehat{Cov}(X,Y)}{\widehat{Var}( X)}.$$

Knowing the slope estimate, the intercept estimate can be found easily: $$ \widehat{\beta}_0 = \overline{Y} - \widehat{\beta}_1 \cdot \overline{ X}.$$

Wages example

In [17]:
beta.1.hat = cov(education, logwage) / var(education)
beta.0.hat = mean(logwage) - beta.1.hat * mean(education)
print(c(beta.0.hat, beta.1.hat))
[1] 1.23919433 0.07859951
(Intercept)   education 
 1.23919433  0.07859951 

Estimate of $\sigma^2$

There is one final quantity needed to estimate all of our parameters in our (statistical) model for the scatterplot. This is $\sigma^2$, the variance of the random variation within each slice (the regression model assumes this variance is constant within each slice...).

The estimate most commonly used is $$ \hat{\sigma}^2 = \frac{1}{n-2} \sum_{i=1}^n (Y_i - \hat{\beta}_0 - \hat{\beta}_1 X_i)^2 = \frac{SSE}{n-2} = MSE $$

Above, note the practice of replacing the quantity $SSE(\hat{\beta}_0,\hat{\beta}_1)$, i.e. the minimum of this function, with just $SSE$.

The term MSE above refers to mean squared error: a sum of squares divided by what we call its degrees of freedom. The degrees of freedom of SSE, the error sum of squares is therefore $n-2$. Remember this $n-2$ corresponded to $\perp$ in the picture above...

Using some statistical calculations that we will not dwell on, if our simple linear regression model is correct, then we can see that $$ \frac{\hat{\sigma}^2}{\sigma^2} \sim \frac{\chi^2_{n-2}}{n-2} $$ where the right hand side denotes a chi-squared distribution with $n-2$ degrees of freedom.

(Note: our estimate of $\sigma^2$ is not the maximum likelihood estimate.)

Wages example

In [18]:
sigma.hat = sqrt(sum(resid(wages.lm)^2) / wages.lm$df.resid)
c(sigma.hat, sqrt(sum((logwage - predict(wages.lm))^2) / wages.lm$df.resid))
  1. 0.40378278229989
  2. 0.40378278229989

The summary from R also contains this estimate of $\sigma$:

In [19]:
lm(formula = logwage ~ education)

     Min       1Q   Median       3Q      Max 
-1.78239 -0.25265  0.01636  0.27965  1.61101 

            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.239194   0.054974   22.54   <2e-16 ***
education   0.078600   0.004262   18.44   <2e-16 ***
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4038 on 2176 degrees of freedom
Multiple R-squared:  0.1351,	Adjusted R-squared:  0.1347 
F-statistic:   340 on 1 and 2176 DF,  p-value: < 2.2e-16

Inference for the simple linear regression model

What do we mean by inference?

  • Generally, by inference, we mean "learning something about the relationship between the sample $(X_1, \dots, X_n)$ and $(Y_1, \dots, Y_n)$."

  • In the simple linear regression model, this often means learning about $\beta_0, \beta_1$. Particular forms of inference are confidence intervals or hypothesis tests. More on these later.

  • Most of the questions of inference in this course can be answered in terms of $t$-statistics or $F$-statistics.

  • First we will talk about $t$-statistics, later $F$-statistics.

Examples of (statistical) hypotheses

  • One sample problem: given an independent sample $\pmb{X}=(X_1, \dots, X_n)$ where $X_i\sim N(\mu,\sigma^2)$, the null hypothesis $H_0:\mu=\mu_0$ says that in fact the population mean is some specified value $\mu_0$.

  • Two sample problem: given two independent samples $\pmb{Z}=(Z_1, \dots, Z_n)$, $\pmb{W}=(W_1, \dots, W_m)$ where $Z_i\sim N(\mu_1,\sigma^2)$ and $W_i \sim N(\mu_2, \sigma^2)$, the null hypothesis $H_0:\mu_1=\mu_2$ says that in fact the population means from which the two samples are drawn are identical.

Testing a hypothesis

We test a null hypothesis, $H_0$ based on some test statistic $T$ whose distribution is fully known when $H_0$ is true.

For example, in the one-sample problem, if $\bar{X}$ is the sample mean of our sample $(X_1, \dots, X_n)$ and $$ S^2 = \frac{1}{n-1} \sum_{i=1}^n (X_i-\bar{X})^2 $$ is the sample variance. Then $$ T = \frac{\bar{X}-\mu_0}{S/\sqrt{n}} $$ has what is called a Student's t distribution with $n-1$ degrees of freedom when $H_0:\mu=\mu_0$ is true.

When the null hypothesis is not true, it does not have this distribution!

General form of a (Student's) $T$ statistic

  • A $t$ statistic with $k$ degrees of freedom, has a form that becomes easy to recognize after seeing it several times.

  • It has two main parts: a numerator and a denominator. The numerator $Z \sim N(0,1)$ while $D \sim \sqrt{\chi^2_k/k}$ that is assumed independent of $Z$.

  • The $t$-statistic has the form $$ T = \frac{Z}{D}. $$

General form of a (Student's) $T$ statistic

  • Another form of the $t$-statistic is $$ T = \frac{\text{estimate of parameter} - \text{true parameter}}{\text{accuracy of the estimate}}. $$

  • In more formal terms, we write this as $$ T = \frac{\hat{\theta} - \theta}{SE(\hat{\theta})}. $$ Note that the denominator is the accuracy of the estimate and not the true parameter (which is usually assumed fixed, at least for now). The term $SE$ or standard error will, in this course, usually refer to an estimate of the accuracy of estimator. Therefore, it is often the square root of an estimate of the variance of an estimator.

General form of a (Student's) $T$ statistic

  • In our simple linear regression model, a natural $t$-statistic is $$ \frac{\hat{\beta}_1 - \beta_1}{SE(\hat{\beta}_1)}. $$ We've seen how to compute $\hat{\beta}_1$, we never get to see the true $\beta_1$, so the only quantity we have anything left to say about is the standard error $SE(\hat{\beta}_1)$.

  • How many degrees of freedom would this $T$ have?

Comparison of Student's $t$ to normal distribution

As the degrees of freedom increases, the population histogram, or density, of the $T_k$ distribution looks more and more like the standard normal distribution usually denoted by $N(0,1)$.

In [20]:
rejection_region = function(dens, q_lower, q_upper, xval) {
    fig = (ggplot(data.frame(x=xval), aes(x)) +
        stat_function(fun=dens, geom='line') +
        stat_function(fun=function(x) {ifelse(x > q_upper | x < q_lower, dens(x), NA)},
                      geom='area', fill='#CC7777') + 
            labs(subtitle=text, y='Density', x='T') +

xval = seq(-4,4,length=101)
q = qnorm(0.975)
Z_fig = rejection_region(dnorm, -q, q, xval) + 
          annotate('text', x=2.5, y=dnorm(2)+0.3, label='Z statistic',

This change in the density has an effect on the rejection rule for hypothesis tests based on the $T_k$ distribution. For instance, for the standard normal, the 5% rejection rule is to reject if the so-called $Z$-score is larger than about 2 in absolute value.

In [21]:

For the $T_{10}$ distribution, however, this rule must be modified.

In [22]:
q10 = qt(0.975, 10)
T_fig = (Z_fig + stat_function(fun=function(x) {ifelse(x > q10 | x < -q10, dt(x, 10), NA)},
                      geom='area', fill='#7777CC', alpha=0.5) +
         stat_function(fun=function(x) {dt(x, 10)}, color='blue') + 
         annotate('text', x=2.5, y=dnorm(2)+0.27, label='T statistic, df=10',

One sample problem revisited

Above, we used the one sample problem as an example of a $t$-statistic. Let's be a little more specific.

  • Given an independent sample $\pmb{X}=(X_1, \dots, X_n)$ where $X_i\sim N(\mu,\sigma^2)$ we can test $H_0:\mu=0$ using a $T$-statistic.

  • We can prove that the random variables $$\overline{X} \sim N(\mu, \sigma^2/n), \qquad \frac{S^2_X}{\sigma^2} \sim \frac{\chi^2_{n-1}}{n-1}$$ are independent.

  • Therefore, whatever the true $\mu$ is $$ \frac{\overline{X} - \mu}{S_X / \sqrt{n}} = \frac{ (\overline{X}-\mu) / (\sigma/\sqrt{n})}{S_X / \sigma} \sim t_{n-1}.$$

  • Our null hypothesis specifies a particular value for $\mu$, i.e. 0. Therefore, under $H_0:\mu=0$ (i.e. assuming that $H_0$ is true), $$\overline{X}/(S_X/\sqrt{n}) \sim t_{n-1}.$$

Confidence interval

The following are examples of confidence intervals you may have already seen.

  • One sample problem: instead of deciding whether $\mu=0$, we might want to come up with an (random) interval $[L,U]$ based on the sample $\pmb{X}$ such that the probability the true (nonrandom) $\mu$ is contained in $[L,U]$ equal to $1-\alpha$, i.e. 95%.

  • Two sample problem: find a (random) interval $[L,U]$ based on the sampl es $\pmb{Z}$ and $\pmb{W}$ such that the probability the true (nonrandom) $\mu_1-\mu_2$ is contained in $[L,U]$ is equal to $1-\alpha$, i.e. 95%.

Confidence interval for one sample problem

  • In the one sample problem, we might be interested in a confidence interval for the unknown $\mu$.

  • Given an independent sample $(X_1, \dots, X_n)$ where $X_i\sim N(\mu,\sigma^2)$ we can test construct a $(1-\alpha)*100\%$ using the numerator and denominator of the $t$-statistic.

Confidence interval for one sample problem

  • Let $q=t_{n-1,(1-\alpha/2)}$

    $$ \begin{aligned} 1 - \alpha &= P_{\mu}\left(-q \leq \frac{\mu - \overline{X}} {S_X / \sqrt{n}} \leq q \right) \\ &= P_{\mu}\left(-q \cdot {S_X / \sqrt{n}} \leq {\mu - \overline{X}} \leq q \cdot {S_X / \sqrt{n}} \right) \\ &= P_{\mu}\left(\overline{X} - q \cdot {S_X / \sqrt{n}} \leq {\mu} \leq \overline{X} + q \cdot {S_X / \sqrt{n}} \right) \\ \end{aligned} $$

  • Therefore, the interval $\overline{X} \pm q \cdot {S_X / \sqrt{n}}$ is a $(1-\alpha)*100\%$ confidence interval for $\mu$.

Inference for $\beta_0$ or $\beta_1$

  • Recall our model $$ Y_i = \beta_0 + \beta_1 X_i + \varepsilon_i,$$ errors $\varepsilon_i$ are independent $N(0, \sigma^2)$.

  • In our heights example, we might want to now if there really is a linear association between ${\tt Daughter}=Y$ and ${\tt Mother}=X$. This can be answered with a hypothesis test of the null hypothesis $H_0:\beta_1=0$. This assumes the model above is correct, but that $\beta_1=0$.

  • Alternatively, we might want to have a range of values that we can be fairly certain $\beta_1$ lies within. This is a confidence interval for $\beta_1$.

Geometric picture of test

The hypothesis test has a geometric interpretation which we will revisit later for other models. It is a comparison of two models. The first model is our original model.

The second model is the null model in which we have set $\beta_1=0$. This model says that $$ Y_i = \beta_0 + \varepsilon_i. $$ This model says that the mean of the $Y$'s is unrelated to that of $X$.

Strictly speaking, we should write $Y_i|X$ on the left hand side as this is a model of the $Y_i$'s given the entire set of $X$ observations. If the pairs of mothers and daughters are drawn from some population independently than we may write $Y_i | X_i$.

Setup for inference

  • Let $L$ be the subspace of $\mathbb{R}^n$ spanned $\pmb{1}=(1, \dots, 1)$ and ${X}=(X_1, \dots, X\ _n)$.

  • Then, $${Y} = P_L{Y} + ({Y} - P_L{Y}) = \widehat{{Y}} + (Y - \widehat{{Y}}) = \widehat{{Y}} + e$$

  • In our model $\mu=\beta_0 \pmb{1} + \beta_1 {X} \in L$ so that $$ \widehat{{Y}} = \mu + P_L{\varepsilon}, \qquad {e} = P_{L^{\perp}}{{Y}} = P_{L^{\perp}}{\varepsilon}$$

  • Our assumption that $\varepsilon_i$'s are independent $N(0,\sigma^2)$ tells us that: ${e}$ and $\widehat{{Y}}$ are independent; $\widehat{\sigma}^2 = \|{e}\|^2 / (n-2) \sim \sigma^2 \cdot \chi^2_{n-2} / (n-2)$.

Setup for inference

  • In turn, this implies $$ \widehat{\beta}_1 \sim N\left(\beta_1, \frac{\sigma^2}{\sum_{i=1}^n(X_i-\overline{X})^2}\right).$$

  • Therefore, $$\frac{\widehat{\beta}_1 - \beta_1}{\sigma \sqrt{\frac{1}{\sum_{i=1}^n(X_i-\overline{X})^2}}} \sim N(\ 0,1).$$

  • The other quantity we need is the standard error or SE of $\hat{\beta}_1$. This is obtained from estimating the variance of $\widehat{\beta}_1$, which, in this case means simply plugging in our estimate of $\sigma$, yielding $$ SE(\widehat{\beta}_1) = \widehat{\sigma} \sqrt{\frac{1}{\sum_{i=1}^n(X_i-\overline{X})^2}} \qquad \text{independent of $\widehat{\beta}_1$}$$

Testing $H_0:\beta_1=\beta_1^0$

  • Suppose we want to test that $\beta_1$ is some pre-specified value, $\beta_1^0$ (this is often 0: i.e. is there a linear association)

  • Under $H_0:\beta_1=\beta_1^0$ $$\frac{\widehat{\beta}_1 - \beta^0_1}{\widehat{\sigma} \sqrt{\frac{1}{\sum_{i=1}^n(X_i-\overline{X})^2}}} = \frac{\widehat{\beta}_1 - \beta^0_1}{ \frac{\widehat{\sigma}}{\sigma}\cdot \sigma \sqrt{\frac{1}{ \sum_{i=1}^n(X_i-\overline{X})^2}}} \sim t_{n-2}.$$

  • Reject $H_0:\beta_1=\beta_1^0$ if $|T| > t_{n-2, 1-\alpha/2}$.

Wage example

Let's perform this test for the wage data.

In [23]:
SE.beta.1.hat = (sigma.hat * sqrt(1 / sum((education - mean(education))^2)))
Tstat = (beta.1.hat - 0) / SE.beta.1.hat
data.frame(beta.1.hat, SE.beta.1.hat, Tstat)
0.07859951 0.00426247118.43989

Let's look at the output of the lm function again.

In [24]:
lm(formula = logwage ~ education)

     Min       1Q   Median       3Q      Max 
-1.78239 -0.25265  0.01636  0.27965  1.61101 

            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.239194   0.054974   22.54   <2e-16 ***
education   0.078600   0.004262   18.44   <2e-16 ***
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4038 on 2176 degrees of freedom
Multiple R-squared:  0.1351,	Adjusted R-squared:  0.1347 
F-statistic:   340 on 1 and 2176 DF,  p-value: < 2.2e-16

We see that R performs this test in the second row of the Coefficients table. It is clear that wages are correlated with education.

Why reject for large |T|?

  • Observing a large $|T|$ is unlikely if $\beta_1 = \beta_1^0$: reasonable to conclude that $H_0$ \ is false.

  • Common to report $p$-value: $$\mathbb{P}(|T_{n-2}| > |T|) = 2 \mathbb{P} (T_{n-2} > |T|)$$

In [25]:
2*(1 - pt(Tstat, wages.lm$df.resid))
In [26]:

Confidence interval based on Student's $t$ distribution

  • Suppose we have a parameter estimate $\widehat{\theta} \sim N(\theta, {\sigma}_{\theta}^2)$, and standard error $SE(\widehat{\theta})$ such that $$ \frac{\widehat{\theta}-\theta}{SE(\widehat{\theta})} \sim t_{\nu}.$$

  • We can find a $(1-\alpha) \cdot 100 \%$ confidence interval by: $$ \widehat{\theta} \pm SE(\widehat{\theta}) \cdot t_{\nu, 1-\alpha/2}.$$

  • To prove this, expand the absolute value as we did for the one-sample CI $$ 1 - \alpha = \mathbb{P}\left(\left|\frac{\widehat{\theta} - \theta}{SE(\widehat{\theta})} \right| < t_{\nu, 1-\alpha/2}\right).$$

Confidence interval for regression parameters

  • Applying the above to the parameter $\beta_1$ yields a confidence interval of the form $$ \hat{\beta}_1 \pm SE(\hat{\beta}_1) \cdot t_{n-2, 1-\alpha/2}.$$

  • We will need to compute $SE(\hat{\beta}_1)$. This can be computed using this formula $$ SE(a_0\hat{\beta}_0 + a_1\hat{\beta}_1) = \hat{\sigma} \sqrt{\frac{a_0^2}{n} + \frac{(a_0\overline{X} - a_1)^2}{\sum_{i=1}^n \left(X_i-\overline{X}\right)^2}}.$$

We also need to find the quantity $t_{n-2,1-\alpha/2}$. This is defined by $$ \mathbb{P}(T_{n-2} \geq t_{n-2,1-\alpha/2}) = \alpha/2. $$ In R, this is computed by the function qt.

In [27]:
alpha = 0.05
n = length(M)

Not surprisingly, this is close to that of the normal distribution, which is a Student's $t$ with $\infty$ for degrees of freedom.

In [28]:

We will not need to use these explicit formulae all the time, as R has some built in functions to compute confidence intervals.

In [29]:
L = beta.1.hat - qt(0.975, wages.lm$df.resid) * SE.beta.1.hat
U = beta.1.hat + qt(0.975, wages.lm$df.resid) * SE.beta.1.hat
data.frame(L, U)
In [30]:
2.5 %97.5 %

Predicting the mean

Once we have estimated a slope $(\hat{\beta}_1)$ and an intercept $(\hat{\beta}_0)$, we can predict the height of the daughter born to a mother of any particular height by the plugging-in the height of the new mother, $M_{new}$ into our regression equation: $$ E[{D}_{new}] = {\beta}_0 +{\beta}_1 M_{new}. $$ This equation says that our best guess at the height of the new daughter born to a mother of height $M_{new}$ is $\hat{D}_{new}$. Does this say that the height will be exactly this value? No, there is some random variation in each slice, and we would expect the same random variation for this new daughter's height as well.

We might also want a confidence interval for the average height of daughters born to a mother of height $M_{new}=66$ inches: $$ \hat{\beta}_0 + 66 \cdot \hat{\beta}_1 \pm SE(\hat{\beta}_0 + 66 \cdot \hat{\beta}_1) \cdot t_{n-2, 1-\alpha/2}. $$

Recall that the parameter of interest is the average within the slice. Let's look at our picture again:

In [31]:
height.lm = lm(D~M)
predict(height.lm, list(M=c(66,60)), interval='confidence', level=0.90)