Simple linear regression¶

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]:
heights_fig = plt.figure(figsize=(10,6))
axes = heights_fig.gca()
axes.scatter(M, D, c='red')
axes.set_xlabel("Mother's height (inches)", size=20)
axes.set_ylabel("Daughter's height (inches)", size=20)

Out[2]:
<matplotlib.text.Text at 0x10cf1ce10>

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 [3]:
X = 66
from matplotlib import mlab
xf, yf = mlab.poly_between([X-.5,X+.5], [50,50], [75, 75])
selected_points = (M <= X+.5) * (M >= X-.5)
mean_within_slice = D[selected_points].mean()
scatterplot_slice = axes.fill(xf, yf, facecolor='blue', alpha=0.1, hatch='/')[0]
axes.scatter([X],[mean_within_slice], s=130, c='yellow', marker='^')
heights_fig

Out[3]:
In [4]:
print mean_within_slice

65.1733333333


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 [5]:
X = 60
selected_points = (M <= X+.5) * (M >= X-.5)
mean_within_slice = D[selected_points].mean()
print mean_within_slice

62.4282894737


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

In [6]:
%%R -o slope,intercept
parameters = lm(D ~ M)$coef print(parameters) intercept = parameters[1] slope = parameters[2]  (Intercept) M 29.917437 0.541747  In [7]: axes.plot([M.min(), M.max()], [intercept + slope * M.min(), intercept + slope * M.max()], linewidth=3) heights_fig  Out[7]: 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. • 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 [8]: X = np.random.standard_normal(50) Y = np.random.standard_normal(50) * 2 + 1.5 + 0.1 * X plt.scatter(X, Y)  Out[8]: <matplotlib.collections.PathCollection at 0x10b243ad0> We've briefly seen how to fit a model in R. Let's take a look at how we might fit it in python directly. Now 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]: squared_error_loss  Out[11]: Let's contrast this with the sum of absolute errors. In [13]: absolute_error_loss  Out[13]: 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} An important summary of the fit is the ratio $$R^2 = \frac{SSR}{SST} = 1 - \frac{SSE}{SST}$$ which measures how much variability inY$is explained by$X$. Example: wages vs. experience¶ In this example, we'll look at the output of lm for the wage data and verify that some of the equations we present for the least squares solutions agree with the output. The data was compiled from a study in econometrics Learning about Heterogeneity in Returns to Schooling. In [14]: %%R url = 'http://stats191.stanford.edu/data/wage.csv' wages = read.table(url, sep=',', header=T) print(head(wages)) mean(logwage)   education logwage 1 16.75000 2.845000 2 15.00000 2.446667 3 10.00000 1.560000 4 12.66667 2.099167 5 15.00000 2.490000 6 15.00000 2.330833 Error in mean(logwage) : object 'logwage' not found  In order to access the variables in wages we attach it so that the variables are in the toplevel namespace. In [15]: %%R attach(wages) mean(logwage)  [1] 2.240279  Let's fit the linear regression model. In [16]: %%R wages.lm = lm(logwage ~ education) print(wages.lm)  Call: lm(formula = logwage ~ education) Coefficients: (Intercept) education 1.2392 0.0786  As in the mother-daughter data, we might want to plot the data and add the regression line. In [17]: %%R -h 800 -w 800 plot(education, logwage, pch=23, bg='red', cex=2, cex.lab=3) abline(wages.lm, lwd=4, col='black')  Least squares estimators¶ There are explicit formulae for the least squares estimators, i.e. the minimizers of the error sum of squares. For the slope,$\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 [18]: %%R 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)) print(coef(wages.lm))  [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. Wages example¶ In [19]: %%R sigma.hat = sqrt(sum(resid(wages.lm)^2) / wages.lm$df.resid)
sigma.hat

[1] 0.4037828


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

In [20]:
%%R
summary(wages.lm)

Call:
lm(formula = logwage ~ education)

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

Coefficients:
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 $$T = \frac{Z}{D}.$$

• 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.

• 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 [22]:
density_fig

Out[22]:

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 [24]:
density_fig

Out[24]:

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

In [26]:
density_fig

Out[26]:

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.

• Let $q=t_{n-1,(1-\alpha/2)}$

\begin{aligned} 1 - \alpha &= P\left(-q \leq \frac{\mu - \overline{X}} {S_X / \sqrt{n}} \leq q \right) \\ &= P\left(-q \cdot {S_X / \sqrt{n}} \leq {\mu - \overline{X}} \leq q \cdot {S_X / \sqrt{n}} \right) \\ &= P\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; $\widehat{\sigma}^2 = \|{e}\|^2 / (n-2) \sim \sigma^2 \cdot \chi^2_{n-2} / (n-2)$.

• 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 [27]:
%%R
SE.beta.1.hat = (sigma.hat * sqrt(1 / sum((education - mean(education))^2)))
Tstat = beta.1.hat / SE.beta.1.hat
data.frame(beta.1.hat, SE.beta.1.hat, Tstat)

  beta.1.hat SE.beta.1.hat    Tstat
1 0.07859951   0.004262471 18.43989


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

In [28]:
%%R
summary(wages.lm)

Call:
lm(formula = logwage ~ education)

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

Coefficients:
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 [29]:
%%R
U = beta.1.hat + qt(0.975, wages.lm$df.resid) * SE.beta.1.hat data.frame(L, U)   L U 1 0.07024057 0.08695845  In [34]: %%R confint(wages.lm)   2.5 % 97.5 % (Intercept) 1.13138690 1.34700175 education 0.07024057 0.08695845  Predicting the mean¶ Once we have estimated a slope$(\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: $$\hat{D}_{new} = \hat{\beta}_0 +\hat{\beta}_1 M_{new}.$$ 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 [35]: heights_fig  Out[35]: In [36]: %%R height.lm = lm(D~M) predict(height.lm, list(M=c(66,60)), interval='confidence', level=0.90)   fit lwr upr 1 65.67274 65.49082 65.85466 2 62.42226 62.27698 62.56753  Forecasting intervals¶ There is yet another type of interval we might consider: can we find an interval that covers the height of a particular daughter knowing only that her mother's height as 66 inches? This interval has to cover the variability of the new random variation with our slice at 66 inches. So, it must be at least as wide as$\sigma$, and we estimate its width to be at least as wide as$\hat{\sigma}$. In [37]: %%R predict(height.lm, list(M=66), interval='prediction', level=0.90)   fit lwr upr 1 65.67274 61.93804 69.40744  In [38]: %%R (69.41-61.94)  [1] 7.47  With so much data in our heights example, this 90% interval will have width roughly 2 * qnorm(0.95) * sigma.hat.height. In [39]: %%R sigma.hat.height = sqrt(sum(resid(height.lm)^2) / height.lm$df.resid)
2 * qnorm(0.95) * sigma.hat.height

[1] 7.455501


The actual width will depend on how accurately we have estimated $(\beta_0, \beta_1)$ as well as $\hat{\sigma}$. Here is the full formula. Again it is based on the $t$ distribution, the only thing we need to change is what we use for the SE.

$$SE(\widehat{\beta}_0 + \widehat{\beta}_1 X_{\text{new}} + \varepsilon_{\text{new}}) = \widehat{\sigma} \sqrt{1 + \frac{1}{n} + \frac{(\overline{X} - X_{\text{new}})^2}{\sum_{i=1}^n \left(X_i-\overline{X}\right)^2}}.$$

The final interval is $$\hat{\beta}_0 + \hat{\beta}_1 X_{\text{new}} \pm t_{n-2, 1-\alpha/2} \cdot SE(\hat{\beta}_0 + \hat{\beta}_1 X_{\text{new}} + \varepsilon_{\text{new}}).$$

In [40]: