# 4.10 Exercises

- Electricity consumption was recorded for a small town on 12 randomly
chosen days. The following maximum temperatures (degrees Celsius)
and consumption (megawatt-hours) were recorded for each day.
Day 1 2 3 4 5 6 7 8 9 10 11 12 **Mwh**16.3 16.8 15.5 18.2 15.2 17.5 19.8 19.0 17.5 16.0 19.6 18.0 **temp**29.3 21.7 23.7 10.4 29.7 11.9 9.0 23.4 17.8 30.0 8.6 11.8 - Plot the data and find the regression model for Mwh with temperature as an explanatory variable. Why is there a negative relationship?
- Produce a residual plot. Is the model adequate? Are there any outliers or influential observations?
- Use the model to predict the electricity consumption that you would expect for a day with maximum temperature $10^\circ $ and a day with maximum temperature $35^\circ $. Do you believe these predictions?
- Give prediction intervals for your forecasts.
The following R code will get you started:
R codeplot(Mwh ~ temp, data=econsumption)

fit <- lm(Mwh ~ temp, data=econsumption)

plot(residuals(fit) ~ temp, data=econsumption)

forecast(fit, newdata=data.frame(temp=c(10,35)))

- The following table gives the winning times (in seconds) for the
men’s 400 meters final in each Olympic Games from 1896 to 2012 (data
set `olympic`).
1896 54.2 1928 47.8 1964 45.1 1992 43.50 1900 49.4 1932 46.2 1968 43.8 1996 43.49 1904 49.2 1936 46.5 1972 44.66 1908 50.0 1948 46.2 1976 44.27 1912 48.2 1952 45.9 1980 44.60 1920 49.6 1956 46.7 1984 44.27 1924 47.6 1960 44.9 1988 43.87 - Update the data set `olympic` to include the winning times from the last few Olympics.
- Plot the winning time against the year. Describe the main features of the scatterplot.
- Fit a regression line to the data. Obviously the winning times have been decreasing, but at what *average* rate per year?
- Plot the residuals against the year. What does this indicate about the suitability of the fitted line?
- Predict the winning time for the men’s 400 meters final in the 2000, 2004, 2008 and 2012 Olympics. Give a prediction interval for each of your forecasts. What assumptions have you made in these calculations?
- Find out the actual winning times for these Olympics (see www.databaseolympics.com). How good were your forecasts and prediction intervals?

- An elasticity coefficient is the ratio of the percentage change in the forecast variable ($y$) to the percentage change in the predictor variable ($x$). Mathematically, the elasticity is defined as $(dy/dx)\times (x/y)$. Consider the log-log model, $$ \log y=\beta _0+\beta _1 \log x + \varepsilon . $$ Express $y$ as a function of $x$ and show that the coefficient $\beta_1$ is the elasticity coefficient.