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Overview

Overview. Engineers and scientists are constantly exposed to collections of facts, or data, both in their professional capacities and in everyday activities.

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Overview

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  1. Overview Engineers and scientists are constantly exposed to collections of facts, or data, both in their professional capacities and in everyday activities. An investigation will typically focus on a well-defined collection of objects constituting a population of interest. In one study, the population might consist of all gelatin capsules of a particular type produced during a specified period.

  2. Constraints on time, money, and other scarce resources usually make a census impractical or infeasible. Instead, a subset of the population—a sample—is selected in some prescribed manner. We are usually interested only in certain characteristics of the objects in a population: the number of flaws on the surface of each casing, the thickness of each capsule wall, the gender of an engineering graduate, the age at which the individual graduated, and so on.

  3. A characteristic may be categorical, such as gender or type of malfunction, or it may be numerical in nature. In the former case, the value of the characteristic is a category (e.g. female or insufficient solder), whereas in the latter case, the value is a number (e.g., age = 23 or diameter = .502 cm).

  4. A variable is any characteristic whose value may change from one object to another in the population. We shall initially denote variables by lowercase letters from the end of our alphabet. Examples include x = brand of calculator owned by a student y = number of visits to a particular Web site during a specified period z = braking distance of an automobile under specified conditions

  5. Data results from making observations either on a single variable or simultaneously on two or more variables.A univariate data set consists of observations on a single variable. For example, we might determine the type of transmission, automatic (A) or manual (M), on each of ten automobiles recently purchased at a certain dealership, resulting in the categorical data set M A A A M A A M A A

  6. Multivariate data arises when observations are made on more than one variable. For example, a research physician might determine the systolic blood pressure, diastolic blood pressure, and serum cholesterol level for each patient participating in a study.

  7. Example 1 cont’d Figure 1.1 shows what is called a stem-and-leaf display as well as a histogram. A Minitab stem-and-leaf display (tenths digit truncated) and histogram for the charity fundraising percentage data Figure 1.1

  8. Notation The number of observations in a single sample, that is, the sample size, will often be denoted by n, so that n = 4 for the sample of universities {Stanford, Iowa State, Wyoming, Rochester} . If two samples are simultaneously under consideration, either m and n or n1 and n2 can be used to denote the numbers of observations.

  9. Histogram, Bar Chart, Pie Chart Variables can be either categorical or numerical. A numerical variable is discrete if its set of possible values either is finite or else can be listed in an infinite sequence. A numerical variable is continuous if its possible values consist of an entire interval on the number line. Bar chart and pie chart are used for a categorical or a discrete numerical variable. Histogram is for a numerical variable, either discrete or continuous.

  10. Example 12 The weights (lb) of the 124 players listed on the rosters of the San Francisco 49ers and the New England Patriots as of Nov. 20, 2009. NFL player weights Histogram

  11. Histograms

  12. Histogram Shapes Histograms come in a variety of shapes. A unimodal histogram is one that rises to a single peak and then declines. A bimodal histogram has two different peaks. Bimodality can occur when the data set consists of observations on two quite different kinds of individuals or objects.

  13. The sample mean xof observations x1, x2,. . ., xn, is given by

  14. The word median is synonymous with “middle,” and the sample median is indeed the middle value once the observations are ordered from smallest to largest. Quartiles divide the data set into four equal parts, Q1, Q2, Q3. Similarly, a data set can be even more finely divided using percentiles; the 99th percentile separates the highest 1% from the bottom 99%, and so on.

  15. Measures of Variability for Sample Data The sample variance, denoted by s2, is given by The sample standard deviation, denoted by s, is the (positive) square root of the variance: Note that s2 and s are both nonnegative. The unit for s is the same as the unit for the corresponding variable.

  16. Boxplot The data considered is the following sample of TN (total nitrogen) loads (kg N/day) from a particular Chesapeake Bay location, displayed here in increasing order.

  17. Boxplot The whiskers in the boxplot extend out to the smallest observation, 9.69, on the low end and 312.45, the largest observation that is not an outlier, on the upper end. A boxplot of the nitrogen load data showing mild and extreme outliers

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