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11-58. Show that the t-statistic in Equation 11-46 for testing H0: 0 is identical to the t-statistic for testing H0: 1 0. 11-59. A random sample of 50 observations was made on the diameter of spot welds and the corresponding weld shear strength. (a) Given that r 0.62, test the hypothesis that 0, using 0.01. What is the P-value for this test (b) Find a 99% con dence interval for . (c) Based on the con dence interval in part (b), can you conclude that 0.5 at the 0.01 level of signi cance 11-60. Suppose that a random sample of 10,000 (X, Y ) pairs yielded a sample correlation coef cient of r 0.02. (a) What is the conclusion that you would reach if you tested H0: 0 using 0.05 What is the P-value for this test (b) Comment on the practical signi cance versus the statistical signi cance of your answer. 11-61. The following data gave X the water content of snow on April 1 and Y the yield from April to July (in inches) on the Snake River watershed in Wyoming for 1919 to 1935. (The data were taken from an article in Research Notes, Vol. 61, 1950, Paci c Northwest Forest Range Experiment Station, Oregon)

32 30.012 0.0052 4

16

Determine E(Y) Show that 1X 2 2 Y c2 2Y 2 2 1X 22 4 Using part (b), show that E3 1X c2 2 E 3 Y4 Using part (c), complete the derivation of Chebyshev s inequality.

6-1 DATA SUMMARY AND DISPLAY 6-2 RANDOM SAMPLING 6-3 STEM-AND-LEAF DIAGRAMS 6-4 FREQUENCY DISTRIBUTIONS AND HISTOGRAMS

Notice that the Image control became an HTML img tag, and src contains the URL to the image (View State was disabled on the Image control).

6-5 BOX PLOTS 6-6 TIME SEQUENCE PLOTS 6-7 PROBABILITY PLOTS 6-8 MORE ABOUT PROBABILITY PLOTTING (CD ONLY)

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After careful study of this chapter you should be able to do the following: 1. Compute and interpret the sample mean, sample variance, sample standard deviation, sample median, and sample range 2. Explain the concepts of sample mean, sample variance, population mean, and population variance 3. Construct and interpret visual data displays, including the stem-and-leaf display, the histogram, and the box plot 4. Explain the concept of random sampling 5. Construct and interpret normal probability plots 6. Explain how to use box plots and other data displays to visually compare two or more samples of data 7. Know how to use simple time series plots to visually display the important features of timeoriented data. CD MATERIAL 8. Interpret probability plots for distributions other than normal. Answers for most odd numbered exercises are at the end of the book. Answers to exercises whose numbers are surrounded by a box can be accessed in the e-Text by clicking on the box. Complete worked solutions to certain exercises are also available in the e-Text. These are indicated in the Answers to Selected Exercises section by a box around the exercise number. Exercises are also available for some of the text sections that appear on CD only. These exercises may be found within the e-Text immediately following the section they accompany.

190 6-1

In the next example, DropDownList controls are added to allow the user to select a value for each of the parameters required by the RenderText method. A call could be made to LoadColors, LoadFontStyles, LoadFontFamily, and LoadFontSizes, but another method, called LoadSettings, returns a DataSet that is populated with a table for each of the settings. Figure 16.11 shows the modified page with DropDownList controls for each of the settings and a Render button and added Image control. The following code has been added to retrieve the settings and display a new image into a second image control:

Well-constructed data summaries and displays are essential to good statistical thinking, because they can focus the engineer on important features of the data or provide insight about the type of model that should be used in solving the problem. The computer has become an important tool in the presentation and analysis of data. While many statistical techniques require only a hand-held calculator, much time and effort may be required by this approach, and a computer will perform the tasks much more ef ciently. Most statistical analysis is done using a prewritten library of statistical programs. The user enters the data and then selects the types of analysis and output displays that are of interest. Statistical software packages are available for both mainframe machines and personal computers. We will present examples of output from Minitab (one of the most widely-used PC packages), throughout the book. We will not discuss the hands-on use of Minitab for entering and editing data or using commands. This information is found in the software documentation. We often nd it useful to describe data features numerically. For example, we can characterize the location or central tendency in the data by the ordinary arithmetic average or mean. Because we almost always think of our data as a sample, we will refer to the arithmetic mean as the sample mean.

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