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228 Part IV: Quality AssuranceContinuedThe correlation coefficient is a unit-less measure, which can attain any value in theinterval [–1, +1]. As the strength of the association between the two variables grows,the absolute value of r approaches 1. Thus, when there is a perfect association between thetwo variables then r = 1 or –1, depending on whether the association is positive or negative.In other words, r = 1 if the two variables are moving in the same direction (increasingor decreasing) and r = –1 if they are moving in the opposite direction.Perfect association means that if we know the value of one variable, then the valueof the other variable can be determined without any error. The other special case iswhen r = 0, which means that there is no association between the two variables. As arule of thumb, the association is weak, moderate, or strong when the absolute value ofr is less than 0.3, between 0.3 and 0.7, or greater than 0.7 respectively.HistogramsPart IV.A.4Histograms are very popular graphs used to represent quantitative data graphically,and they provide very useful information about a data set, for example,information about trends, patterns, location/center, and dispersion of the data.Such information is not particularly apparent from raw data.Construction of a histogram involves two major steps as follows:Step 1. Prepare a frequency distribution table for the given data.Step 2. Use the frequency distribution table prepared in step 1 to constructthe histogram. From here the steps involved in constructing ahistogram are exactly the same as the steps taken to construct abar chart except that in a histogram there is no gap between theintervals marked on the x-axis. We illustrate the construction ofa histogram in Example 18.23.Note that a histogram is called a frequency histogram or a relative frequency histogramdepending on whether the heights of the rectangles erected over the intervalsmarked on the x-axis are proportional to the frequencies or to the relativefrequencies. In both types of histograms the width of the rectangles is equal tothe class width. In fact, the two types of histograms are identical except that thescales used on the y-axes are different. This point should also become clear fromExample 18.23.Another graph that becomes the basis of probability distributions, whichwe are going to study in later chapters, is called the frequency polygon or relativefrequency polygon depending on which histogram is used to construct the graph.To construct the frequency or relative frequency polygon, first mark the midpointson the top ends of the rectangles of the corresponding histogram and thensimply join these midpoints. Note that we include classes with zero frequenciesat the lower as well as at the upper end of the histogram so that we can connectthe polygon with the x-axis. The curves obtained by joining the midpoints arecalled the frequency or relative frequency polygons as the case may be. The frequency
Chapter 18: A. Basic Statistics and Applications 229EXAMPLE 18.23The following data give the survival time (in hours) of 50 parts involved in a field testunder extreme operating conditions.60 100 130 100 115 30 60 145 75 80 89 57 64 92 87 110 180195 175 179 159 155 146 157 167 174 87 67 73 109 123 135 129 141154 166 179 37 49 68 74 89 87 109 119 125 56 39 49 190a. Construct a frequency distribution table for the above data.b. Construct frequency and relative frequency histograms for the above data.Solution:1. Find the range of the data2. Determine the number of classesR = 195 – 30 = 165m = 1 + 3.3 log 50 = 6.57By rounding it we consider the number of classes to be equal to seven.3. Compute the class widthClass width = R/m = 165/7 = 23.57By rounding up this number we have class width equal to 24. As noted earlier, we alwaysround up the class width to a whole number or to any other convenient number thatmay be easy to work with. Note that if we round down the class width, then some of theobservations may be left out of our count and not belong to any class. Consequently,the total frequency will be less than n. The frequency distribution table for the data inthis example is shown as Table 18.9.Table 18.9 Frequency distribution table for the survival time of parts.Class Tally Frequency Relative Cumulativefrequency frequency[30–54) ///// 5 5/50 5[54–78) ///// ///// 10 11/50 16[78–102) ///// //// 9 8/50 24[102–126) ///// // 7 7/50 31[126–150) ///// / 6 6/50 37[150–174) ///// / 6 6/50 43[174–198] ///// // 7 7/50 50Total 50 1Part IV.A.4Continued
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Chapter 18: A. Basic Statistics and Applications 229
EXAMPLE 18.23
The following data give the survival time (in hours) of 50 parts involved in a field test
under extreme operating conditions.
60 100 130 100 115 30 60 145 75 80 89 57 64 92 87 110 180
195 175 179 159 155 146 157 167 174 87 67 73 109 123 135 129 141
154 166 179 37 49 68 74 89 87 109 119 125 56 39 49 190
a. Construct a frequency distribution table for the above data.
b. Construct frequency and relative frequency histograms for the above data.
Solution:
1. Find the range of the data
2. Determine the number of classes
R = 195 – 30 = 165
m = 1 + 3.3 log 50 = 6.57
By rounding it we consider the number of classes to be equal to seven.
3. Compute the class width
Class width = R/m = 165/7 = 23.57
By rounding up this number we have class width equal to 24. As noted earlier, we always
round up the class width to a whole number or to any other convenient number that
may be easy to work with. Note that if we round down the class width, then some of the
observations may be left out of our count and not belong to any class. Consequently,
the total frequency will be less than n. The frequency distribution table for the data in
this example is shown as Table 18.9.
Table 18.9 Frequency distribution table for the survival time of parts.
Class Tally Frequency Relative Cumulative
frequency frequency
[30–54) ///// 5 5/50 5
[54–78) ///// ///// 10 11/50 16
[78–102) ///// //// 9 8/50 24
[102–126) ///// // 7 7/50 31
[126–150) ///// / 6 6/50 37
[150–174) ///// / 6 6/50 43
[174–198] ///// // 7 7/50 50
Total 50 1
Part IV.A.4
Continued