Least squares modelling

Learning outcomes

• Understand the difference between correlation and covariance.
• What the objective function of least squares does
• Understand and use an analysis of variance table
• Calculate and interpret the confidence intervals from a least squares model
• Know about the assumptions required to interpret least squares model coefficients
• Use the prediction error range from the model
• Identify outlier points and classify them
• Use the linear model when there are multiple predictor variables (this is what we are building up towards; we will use multiple linear regression extensively in the next topic)

Resources

• Class notes 2015
• Class notes 2014
• Textbook, chapter 4
• Quizzes (with solutions): attempt these after you have watched the videos

Tasks to do first	Quiz	Solution
Watch videos 1, 2, and 3, and complete step 16 of the Software tutorial	Quiz	Solution
Watch videos 4, 5 and 6, and also see the Thermocouple example code (below)	Quiz	Solution
Watch videos 7 and 8, and complete steps 17 and 18 of the Software tutorial	Quiz	Solution
Watch videos 9, 10 and 11, and complete step 19 of the Software tutorial	Quiz	Solution
Watch videos 12, 13, 14 and 15, and complete steps 21 and 22 of the Software tutorial	Quiz	Solution

• Advanced students should read in the course textbook about leverage, discrepancy, and influential points. Once you've done that, please complete step 20 of the Software tutorial and use the code below to detects points with high leverage, discrepancy, and influence.

Extended readings/practice

• Run the code below to see how to build and use a linear model in R, but see step 16 and onwards in the R tutorial as well.
• Try some practice problems.
• Describe a linear regression model you have made for a lab report.
  • What was the \(R^2\) value?
  • How did you calculate the regression model values?
  • Use the same data from your report and instead calculate the standard error, \(S_E\). How do you interpret that \(S_E\) value now?
• Do the YouTube challenge: find a video on YouTube that explains the central limit theorem, or the confidence interval, or least squares in a way that is different to explained in class (hopefully you find better explanations than mine). Share the link with a friend in your class.
• Does eating chocolate lead to winning a Nobel prize?

Class videos from prior years

Videos from 2015

Watch all these videos in this YouTube playlist

1. Overview of this module [02:59]
2. Covariance and correlation [09:46]
3. Why least squares, and some other alternatives [covered in class]
4. Some of the math behind the LS model [09:44]
5. Understanding the analysis of variance (ANOVA) [11:58]
6. Interpretation of standard error [covered in class]
7. Assumptions to derive LS confidence intervals [05:45]
8. Confidence intervals interpreted and used in 3 LS examples [11:54]
9. Prediction intervals from the least squares model [04:24]
10. Checking for violations of the least squares assumptions (1 of 2) [07:27]
11. Checking for violations of the least squares assumptions (2 of 2) [11:46]
12. Introducing multiple linear regression - why we need to use it [2:59]
13. MLR - the matrix equation form and an example [11:25]
14. Interpreting the MLR model coefficients and confidence intervals [04:58]
15. Integer variables in the multiple linear regression model [09:51]

Covered in class | No video | Script

Covered in class | No video | Script

Videos from 2014

See the webpage from 2014

Videos from 2013

See the webpage from 2013

Software codes for this section

Code to show how to build and plot a least squares model in R

Run this code in a web-browser

x <- c(10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5) y <- c(8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68) plot(x,y) model.ls <- lm(y ~ x) summary(model.ls) coef(model.ls) confint(model.ls) names(model.ls) model.ls$resisduals resid(model.ls) plot(x, y) abline(model.ls)

Thermocouple example

• Compare the \(R^2\) and \(S_E\) values. What does each of those values tell you about the model?
• How do you use a least squares model in R to make a prediction on new data?
• Check that the linear regression assumptions are satisfied.
• Run this code in a web-browser

V <- c(0.01, 0.12, 0.24, 0.38, 0.51, 0.67, 0.84, 1.01, 1.15, 1.31) T <- c(273, 293, 313, 333, 353, 373, 393, 413, 433, 453) plot(V, T) model <- lm(T ~ V) summary(model) coef(model) # Get the coefficient confidence intervals confint(model) # Model's residuals resid(model) library(car) # q-q plot of the residuals to check normality qqPlot(resid(model)) plot(V, T) v.new <- seq(0, 1.5, 0.1) t.pred <- coef(model)[1] + coef(model)[2] * v.new lines(v.new, t.pred, type="l", col="blue") # Plot x against the residuals to check for non-linearity plot(V, resid(model)) abline(h=0) # Plot the raw data and the regression line in red plot(V, T) abline(model, col="red")

Build the linear regression in R for video 4

• Run this code in a web-browser

taste <- c(12.3, 20.9, 25.9, 37.3, 5.5, 16.8, 38.9, 54.9, 57.2, 6.4) H2S <- c(3.14, 5.04, 7.60, 8.72, 4.79, 3.66, 9.06, 6.75, 7.91, 4.70) # Always plot your data first: plot(H2S, taste) # Build a linear model model <- lm(taste ~ H2S) n <- length(taste) # Model's predictions y.hat <- predict(model) # error = observed - predicted errors <- taste - y.hat # Now calculate various sums of squares TSS <- sum((taste - mean(taste))**2) RegSS <- sum((y.hat - mean(taste))**2) RSS <- sum(errors**2) # The standard error will be our reference value to judge a model's performance paste0('The SE = ', round( sqrt(RSS/(n-2)),2) ) # Did you know you can do this in R? plot(model)

Complete least squares regression example in R, with testing and checking afterwards

• Run this code in a web-browser

bio <- read.csv('http://openmv.net/file/bioreactor-yields.csv') summary(bio) # Plot data plot(bio, main="Raw data") # For convenience: y <- bio$yield x <- bio$temperature # Build linear model: interpret coeff, CI and SE model <- lm(y ~ x) summary(model) confint(model) # Visualize predictions plot(x, y, main="Model with prediction line") abline(model) # Residuals normal? library(car) qqPlot(model) # Structure in residuals? plot(resid(model), main="Model residuals") abline(h=0, col="blue") # Residuals in time order? # You might have to rearrange the row order, # the plot using the same code as previous plot # Look at the autocorrelation to check for lack of # independence (can be inaccurate for small datasets) acf(resid(model), main="ACF of residuals") # Predictions vs residuals plot(predict(model), resid(model), main="Predictions vs residuals") abline(h=0, col="blue") # x-data vs residuals plot(x, resid(model), main="x vs residuals") abline(h=0, col="blue") identify(x, resid(model)) # Predictions-vs-y plot(y, predict(model), main="y vs residuals") abline(a=0, b=1, col="blue") identify(y, predict(model))

Test for influence, discrepancy and leverage in R with a least squares model

• You have to run the code below 3 times: each for model A, B and C.
• Run this code in a web-browser

# Create some data to demonstrate with # (ordinarily you'd use your own "x" and "y" variables) N = 25 set.seed(41) x <- rnorm(N, sd=4, mean=10) y <- x*4 - 6 + log(x) + rnorm(N, sd=3) # Discrepant point (model A) x[N+1] = 11.4 y[N+1] = 72.6 # Influential point (model B) x[N+1] = 25 y[N+1] = 42 # High leverage point (model C) x[N+1] = 25 y[N+1] = 92.6 # Build the model 3 times: once for A, B and C # by commenting out the relevant lines. # # For example, comment out lines for B and C when # trying to understand what a discrepant point in # model A is. # # Point number 26 will always be the problematic # point in these examples. model <- lm(y~x) summary(model) plot(x, y, main="x vs y raw data") abline(model, col="blue") identify(x, y) # you'd pick out point #26 here # Leverage: hatvalues plot(hatvalues(model), ylim=c(0,1), main="Hat values") N <- length(x) avg.hat <- 2/N abline(h=2*avg.hat, col="darkgreen") abline(h=3*avg.hat, col="red") identify(hatvalues(model)) # you'd pick out point #26 here # Discrepancy: rstudent plot(rstudent(model), main="Studentized residuals") abline(h=c(-2, 2), col="red") # Influence: Cook's D plot(cooks.distance(model), main="Cook's D") K <- length(model$coefficients) cutoff <- 4/(N-K) abline(h=cutoff, col="red") identify(cooks.distance(model)) # you'd pick out point #26 here build <- seq(1,N) remove <- -c(26) # remove point 26 and rebuild the model model.update <- lm(model, subset=build[remove]) # Did the model improve? library(car) influenceIndexPlot(model.update)

Build a multiple linear regression in R (manually and automatically)

• Compare the manual and automatic results to ensure the model coefficients are the same.
• Also see the example after this one
• Run this code in a web-browser

# Calculate the model manually x1.raw <- c(1,3,4,7,9,9) x2.raw <- c(9,9,6,3,1,2) y.raw <- c(3,5,6,8,7,10) n <- length(x1.raw) x1 <- x1.raw - mean(x1.raw) x2 <- x2.raw - mean(x2.raw) y <- y.raw - mean(y.raw) X <- cbind(x1, x2) # Calculate: b = inv(X'X) X'y XTX <- t(X) %*% X # compare this to cov(X)*(n-1) XTY <- t(X) %*% y XTX.inv <- solve(XTX) b <- XTX.inv %*% XTY b # Let R calculate the model: model <- lm(y.raw ~ x1.raw + x2.raw) summary(model)

Build the multiple linear regression in R for video 13

• This is the same as the prior example, except we use the centered values directly
• Run this code in a web-browser

x1 <- c(-4.5, -2.5, -1.5, 1.5, 3.5, 3.5) x2 <- c( 4, 4, 1, -2, -4, -3) y <- matrix(c(-3.5, -1.5, -0.5, 1.5, 0.5, 3.5), nrow=6, ncol=1) X <- cbind(x1, x2) XtX <- t(X) %*% X Xty <- t(X) %*% y XtX.inv <- solve(XtX) b <- XtX.inv %*% Xty summary(lm(y ~ x1 + x2))