2. Chapter 1: DESCRIPTIVE STATISTICS – PART I2  Statistics is the science of learning from data exhibiting random fluctuation.  Descriptive statistics:  Collecting data  Presenting data  Describing data  Inferential statistics:  Drawing conclusions and/or making decisions concerning a population based only on sample data  Based on probability theory

3.  What are data?  Data can be numbers, record names, or other labels.  Data are useless without their context…  To provide context we need Who, What (and in what units), When, Where, and How of the data.  In civil engineering we meet most often numerical data.  Presentation tools for numerical data (one sample):  Histogram  Boxplot Chapter 1: DESCRIPTIVE STATISTICS – PART I3

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5.  Other examples of histograms: Example 1.1, part a) on my personal website: http://mat.fsv.cvut.cz/Zamestnanci/nHomePage.asp x?hala  How to construct a boxplot? Will be discussed later (the use of numerical measures is necessary). Chapter 1: DESCRIPTIVE STATISTICS – PART I5

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13. Boxplot: Chapter 1: DESCRIPTIVE STATISTICS – PART I13

14. Refer to Examples 1.2 and 1.3: We found in Example 1.3 that value 28 is an outlier. Assume that this value is an erroneous measurement and exclude it from the sample. a)Compute basic summary measures for the reduced sample of 15 observations. b)Construct the boxplot for the reduced sample. c)Compare the results for both samples. Answers are available on my personal website. Chapter 1: DESCRIPTIVE STATISTICS – PART I14

15.  „Normally“ distributed data:  Histogram has almost symmetric shape; it can be fitted well by Gaussian curve – see Chapter 5.  Median and mean are almost equal.  Boxplot is almost perfectly symmetric; there are no outliers.  Skewness and kurtosis are very close to zero. Chapter 1: DESCRIPTIVE STATISTICS – PART I15

16.  Examples:  Histogram of compressive strength of concrete on page 4.  Boxplot constructed in Example 1.4 (15 samples of building material – reduced data set). Comment: Skewness computed for the data in Example 1.4 is negative and equals approx. -0.416. It shows that there the data are actually gentle left skewed - see later. (You will not be asked to compute skewness in the exam.) Chapter 1: DESCRIPTIVE STATISTICS – PART I16

17. We meet in applications very often left or right skewed data. Coefficient of skewness is  negative for left-skewed data  positive for right-skewed data Chapter 1: DESCRIPTIVE STATISTICS – PART I17 Mean = Median = Mode Mean < Median < Mode Right-Skewed Left-Skewed Symmetric (Longer tail extends to left) (Longer tail extends to right) Mode < Median < Mean

18.  Examples of right-skewed distributions:  Example 1.2 (16 samples of building material – original data set) Comment: Skewness for this sample equals approx. 2.879.  Earthquakes magnitudes: Chapter 1: DESCRIPTIVE STATISTICS – PART I18

19.  An example of Boxplot for right-skewed data: Chapter 1: DESCRIPTIVE STATISTICS – PART I19

20. Examples of left-skewed distributions:  All three variables in Example 1.1 (Excel file Example 1.1_data and answers).  Grade distribution in a class of 80 students: Additional questions:  What is the range for the marks of 20 best students?  Which value cuts off the marks of 25 % worst students?  Are there any outliers? Discuss.  Can we say anything about average mark in this exam? Chapter 1: DESCRIPTIVE STATISTICS – PART I20

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22. Example 1.5 A researcher observed using a microscope the number of gold particles in a thin coating of gold solution. He completed 517 observations in regular time intervals. The results are listed in the table: Compute the mode, median, and quartiles. Compute the mean and standard deviation, too. Comment on the data distribution. Chapter 1: DESCRIPTIVE STATISTICS – PART I22 Number of particles01234567 Frequency1121681306832511

23. Chapter 1: DESCRIPTIVE STATISTICS – PART I23 Number of particles01234567 Frequency1121681306832511 Cumulative frequency112280410478510515516517

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