1 The Islamic University of Gaza Civil Engineering Department Statistics ECIV 2305 ‏ Chapter 6 – Descriptive Statistics

2 6.1 Experimentation  Population consist of all possible observations available for a particular probability distribution.  A sample: is a particular subset of the population that an experiment measures and uses to investigate the unknown probability distribution.

3  A random sample Is one in which the elements of the sample are chosen at random from the population and this procedure is often used to ensure that the sample is representative of the population.

4 The data observations x 1, x 2, x 3, ……, x n can be grouped into: 1. Categorical data: mechanical, electrical, misuse 2. Numerical data : integers or real numbers

5 6.2 Data Presentation A. Categorical Data: 1. Bar Charts

6 2. Pareto Chart The categories are arranged in order of decreasing frequency.

7 3. Pie Charts A graph depicting data as slices of a pie n = data set of n observations r = observations of specific category. Slice = (r/n)*360

Histograms  Histograms are used to present numerical data rather than categorical data. Example: Consider the following data, construct the frequency histogram

9  Number of classes =  Class width = Solution Class #Class intervalFrequencyRelative frequency

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Steam and leaf plot The steam and leaf plot is similar to histogram, the data is split into a stem (the first digit) and leaf is the second digit.

12 Problem For the Following set of data, using five classes Find:  The frequency distribution table  The relative frequency distribution table  The Frequency Histogram  Present the data as stem and leaf plot

Outliers  Outliers can be defined as the data points that appear to be separate from the rest of data.  It is usually sensible to be removed from the data. outlier

14 Sample Quantiles  The sample median is the 50 th percentile.  The upper quartile is the 75 th percentile.  The lower quartile is the 25 th percentile,  The sample inter-quartile range denotes the difference between the upper and lower sample quartile.

15 Example  Consider the following data: Find:  The sample media.  The upper quartile.  The lower quartile.  The sample inter-quartile range.

16 Solution: 1.The upper quartile: The 15 th largest value = 4.6 The 16 th largest value = 4.7 The 75 th percentile = 2.The lower quartile: The 5 th largest value = 2.6 The 6 th largest value = 2.8 The 75 th percentile = 3.The inter-quartile range = – 2.65

17  Pox plot is a schematic presentation of the sample median, the upper and lower sample quartile, the largest and smaller data observation. Box Plots median Lower quartile Upper quartile Smaller observation largest observation

Sample statistics Provide numerical summary measures of data set:  Sample mean or arithmetic average ( )

19 Median Is the value of the middle of the (ordered) data points. Notes: If data set of 31 observations, the sample median is the 16 th largest data point.

20  Is obtained by deleting of the largest and some of the smallest data observations. Usually a 10% trimmed mean is employed ( top 10% and bottom 10%). For example, 10% trimmed of 50 data points, then the largest 5 and smallest 5 data observations are removed and the mean is taken for the remaining 40 data observations. Sample trimmed mean

21 Sample mode  Is used to denote the category or data value that contains the largest number of data observation or value with largest frequency.  Sample variance:  Standard Deviation:

22 Example Consider the following data: Find:  The sample mean  The sample median.  The 10 % trimmed mean.  The sample mode.  The sample variance.

23 Solution  The sample mean:  The sample median is the average of the 10 th and 11 th observation  The 10 % trimmed mean: Top 10% observation = 0.1*20 = 2 observations, and top 10% observation = 0.1*20 = 2 observations

24  The sample mode =  The sample variance.

25 xx-x'(x-x')