Download Time plots In applications like process control, detecting trends and

ST 370
Probability and Statistics for Engineers
Time plots
In applications like process control, detecting trends and other
changes in a process is important. Plotting data values against the
time at which the observation was made is a key step.
Example: chemical concentrations
conc <- read.csv("Data/Table-15-03.csv")$Concentration;
plot(conc, type = "b")
# Target concentration is 100 grams per liter:
abline(h = 100, col = "green")
# Lower Control Limit is 96 grams per liter:
abline(h = 96, col = "red")
# Plot cumulative deviations from target:
plot(cumsum(conc - 100), type = "b")
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Descriptive Statistics
Time plots
ST 370
Probability and Statistics for Engineers
Example: global temperature
temperature <- read.csv("Data/Temperature.csv");
temperature <- ts(temperature, start = 1850);
plot(temperature)
# Fit a simple model:
tempModel <- read.csv("Data/TempFit.csv");
tempModel <- ts(tempModel, start = 1850);
matplot(time(tempModel), tempModel, type = "l", lty = 1)
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Descriptive Statistics
Time plots
ST 370
Probability and Statistics for Engineers
Scatter plots
When exploring the association of one variable with another, such as
the pull-strength and wire-length variables, the scatter plot is an
important tool:
# wireBond <- read.csv("Data/Table-01-02.csv")
pairs(wireBond)
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Descriptive Statistics
Scatter plots
ST 370
Probability and Statistics for Engineers
Quantile Plots
In a sample of observed data values, the ordered values are called the
order statistics. Each order statistic is a quantile of the sample.
The R function ppoints() shows the fraction represented by each
order statistic.
Probability theory gives a theoretical quantile for each fraction, under
the assumption that the sample follows a given distribution, usually
the normal distribution.
The quantile-quantile plot is a graph of the sample quantiles against
the theoretical quantiles. The R function qqnorm() makes Q-Q plots.
Example: Compressive strengths
qqnorm(alloy)
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Descriptive Statistics
Q-Q plots