1. Basic Statistics  Statistics in Engineering (collect, organize, analyze, interpret)  Collecting Engineering Data  Data Presentation and Summary  Types of Data  Graphical Data Presentation  Numerical Data Presentation  Probability Distributions  Discrete Probability Distribution  Continuous Probability Distribution

2. Collecting Engineering Data  Direct observation  Experiments  better way to produce data  Surveys  depends on the response rate  Personal Interview:  higher expected response rate and  fewer incorrect respondents

3.  Population : the entire collection of objects or outcomes about which data are collected.  Sample : subset of the population containing the observed objects or the outcomes.  Parameter : Summary measure about population,.  Statistics : Summary measure about sample,. Population vs Sample Parameter vs Statistics

4.  Statistics can be divided into two.  1) Descriptive statistics : describe basic features of data by providing simple summaries about the sample and measures in a form of suitable graphical or numerical analysis. Graphical representatives :  stem-and-leaf plot  line chart  histogram  boxplot. Numerical analyses :  measure of central tendency  measure of dispersion  measure of position.  2) Inferential statistics : draw a conclusion about sample data that would represent an actual population.

5. Types of Data Qualitative/ Categorical DataQuantitative/ Numeric Data i.Deals with descriptions. ii.Data can be observed but not measured. i.Deals with numbers. ii.Data which can be measured. i.Defect or no defect ii.Gender iii.Ethnic group iv.Colors v.Textures i.Income ii.CGPA iii.Diameter iv.Weight v.cost The most popular charts for qualitative data :  bar chart/column chart  pie chart  line chart. The most popular charts for qualitative data :  histogram  frequency polygon  ogive  box plot  stem and leaf plot Qualitative vs Quantitative

6. Discrete vs Continuous  Quantitative variables can be further classified as discrete or continuous.  Discrete variables are usually obtained by counting. There are a finite or countable number of choices available with discrete data. You can't have 2.63 people in the room.  Continuous variables are usually obtained by measuring. Length, weight, and time are all examples of continous variables.

7.  Ungrouped/raw data - Data that has not been organized into groups.  Grouped data - Data that has been organized into groups (into a frequency distribution).  Frequency distribution: A grouping of data into mutually exclusive classes showing the number of observations in each class. Grouped Vs Ungrouped Data Ungrouped dataGroup data 1.0, 1.1, 1.2, 1.0, 1.1, 1.3, 1.2, 1.1, 1.0, 1.2, 1.3, 1.4, 1.2, 1.2, 1.1, 1.0, 1.0, 1.2, 1.3, 1.4, 1.0 Class boundariesFrequency 0.95 – 1.1510 1.15 – 1.359 1.35 – 1.552

8.  About 50 UniMAP students were asked about their background and the results are as follows. Display your data in suitable form. RespondentGender Ethnic Group Family Income CGPARespondentGender Ethnic Group Family Income CGPA 11110003.00262119002.82 21116003.37272110003.02 31180003.59282115003.47 41413602.50292320003.60 5138003.19302280003.41 61112502.96312120003.23 71212003.65322120003.25 81130003.04331210003.39 91245002.80341115703.20 101130003.39351170003.01 112123803.16362110002.98 12218003.67371130003.45 132320003.40382110003.13 141120003.10391419803.30 152110003.31402112002.60 162235003.80412126702.89 172116003.16421120002.90 182118032.84431335963.70 191230003.35441120003.11 202114003.20451250003.34 212130003.00461115003.82 222140002.80471225003.61 231247802.78481125003.25 242143002.90491125003.85 251325003.02501315003.67 Code used: Gender: 1 = male, 2 = female Ethnic group: 1 = Malay, 2 = Chinese, 3 = Indian, 4 = others Example:

9. Frequency table  Bar Chart: used to display the frequency distribution in graphical form. Observation Frequency Malay33 Chinese9 Indian6 Others2 graphical presentation of qualitative data

10.  Pie Chart: used to display the frequency distribution. It displays the ratio of the observations  Line chart: used to display the trend of observations. It is a very popular display for the data which represent time. JanFebMarAprMayJunJulAugSepOctNovDec 1075 3972603161421149

11.  Histogram: Looks like the bar chart except that the horizontal axis represent the data which is quantitative in nature. There is no gap between the bars. graphical presentation of quantitative data

12.  Frequency Polygon: looks like the line chart except that the horizontal axis represent the class mark of the data which is quantitative in nature.  Ogive: line graph with the horizontal axis represent the upper limit of the class interval while the vertical axis represent the cummulative frequencies.

13. Data Summary  Summary statistics are used to summarize a set of observations. a)Measures of Central Tendency  Mean  Median  Mode b)Measures of Dispersion  Range  Variance  Standard deviation c)Measures of Position  Z scores  Percentiles  Quartiles  Outliers

14. a)Measures of Central Tendency Mean  Mean of a sample is the sum of the sample data divided by the total number sample.  Mean for ungrouped data is given by:  Mean for group data is given by:

15. Example 2 (Ungrouped data): Mean for the sets of data 3,5,2,6,5,9,5,2,8,6 Solution :

16. Median of ungrouped data:  The median depends on the number of observations in the data, n. If n is odd, then the median is the (n+1)/2 th observation of the ordered observations.  But if n is even, then the median is the arithmetic mean of the n/2 th observation and the (n+1)/2 th observation. Median of grouped data:

17. Example 4 (Ungrouped data): n is odd Find the median for data 4,6,3,1,2,5,7 ( n = 7) Rearrange the data : 1,2,3,4,5,6,7 (median = (7+1)/2=4 th place) Median = 4 n is even Find the median for data 4,6,3,2,5,7 (n = 6) Rearrange the data : 2,3,4,5,6,7 Median = (4+5)/2 = 4.5

18. Mode Mode of ungrouped data:  The value with the highest frequency in a data set.  It is important to note that there can be more than one mode and if no number occurs more than once in the set, then there is no mode for that set of numbers Find the mode for the sets of data 3, 5, 2, 6, 5, 9, 5, 2, 8, 6 Mode = number occurring most frequently = 5

19. b)Measures of Dispersion  Range = Largest value – smallest value  Variance= measures the variability (differences) existing in a set of data. The variance for the ungrouped data: For sample For population

20. The variance for the grouped data:  For sample or  For population or

21.  A large variance means that the individual scores (data) of the sample deviate a lot from the mean.  A small variance indicates the scores (data) deviate little from the mean. Standard deviation: the positive square root of the variance is the standard deviation

22. Example 8 (Ungrouped data) Find the variance and standard deviation of the sample data : 3, 5, 2, 6, 5, 9, 5, 2, 8, 6

23. Exercise 4 (submit on Thursday) The following data give the sample number of iPads sold by a mail order company on each of 30 days. (Hint : 5 number of classes) a)Construct a frequency distribution table. b)Find the mean, variance and standard deviation, mode and median. c)Construct a histogram. 825111529221051721 2213261618129262016 2314192320162792114

24. Rules of Data Dispersion By using the mean and standard deviation, we can find the percentage of total observations that fall within the given interval about the mean. Empirical Rule Applicable for a symmetric bell shaped distribution / normal distribution. There are 3 rules: i. 68% of the data will lie within one standard deviation of the mean, ii. 95% of the data will lie within two standard deviation of the mean, iii. 99.7% of the data will lie within three standard deviation of the mean,

25. Example 10 The age distribution of a sample of 5000 persons is bell shaped with a mean of 40 yrs and a standard deviation of 12 yrs. Determine the approximate percentage of people who are 16 to 64 yrs old. Solution: Approximately 68% of the measurements will fall between 28 and 52, approximately 95% of the measurements will fall between 16 and 64 and approximately 99.7% to fall into the interval 4 and 76.

26. c)Measures of Position  To describe the relative position of a certain data value within the entire set of data.  z scores  Percentiles  Quartiles  Outliers

27. Quartiles  Divide data sets into four equal parts where each part account about 25% of data distribution. Minimum value Q1Q2Q3 Maximum value 25% of data 25% of data 25% of data 25% of data

28. Find Q1, Q2, and Q3 for the following data 15, 13, 6, 5, 12, 50, 22, 18 Step 1: Arrange the data in order 5, 6, 12, 13, 15, 18, 22, 50 Step 2: Find the median (Q2) 5, 6, 12, 13, 15, 18, 22, 50 ↑ Q2=(13+15)/2=14 Step 3 Find the median of the data values less than 14. 5, 6, 12, 13 ↑ Q1 = (6+12)/2=9 Step 4 Find the median of the data values greater than 14 15, 18, 22, 50 ↑ Q3=(18+22)/2=20 Hence, Q1 =9, Q2 =14, and Q3 =20

29. Example: 5, 8, 4, 4, 6, 3, 8 (n=7) 1. Arrange the data in order form: 3, 4, 4, 5, 6, 8, 8 2. Q1: Find the median of the data values less than 5. 3, 4, 4 Q1: Find the median of the data values greater than 5. 6,8,8 Therefore,

30. The following data represent the number of inches of rain in Chicago during the month of April for 10 randomly years. 2.473.973.944.11 5.22 1.144.023.411.850.97 Determine the quartiles. Exercise:

31. The following data represent the number of inches of rain in Chicago during the month of April for 10 randomly years. 2.473.973.944.11 5.22 1.144.023.411.850.97 Determine the quartiles. Answer: Exercise:

32. Outliers  Extreme observations  Can occur because of the error in measurement of a variable, during data entry or errors in sampling.

33. Checking for outliers by using Quartiles Step 1: Determine the first and third quartiles of data. Step 2: Compute the interquartile range (IQR),. Step 3: Determine the fences. Fences serve as cut off points for determining outliers. needed for identifying extreme values in the tails of the distribution: Step 4: If data value is less than the lower fence or greater than the upper fence, considered outlier. A point beyond an outer fence is considered extreme outlier.

34. Example 2.473.973.944.11 5.22 1.144.023.411.850.97 Determine whether there are outliers in data set.

35. Arrange data in ascending form: 0.97, 1.14, 1.85, 2.47, 3.41, 3.94, 3.97, 4.02, 4.11, 5.22 0.97, 1.14, 1.85, 2.47, 3.413.94, 3.97, 4.02, 4.11, 5.22 Since all the data are not less than -3.0675 and not greater than 8.2725, then there are no outliers in the data Follow the steps to find quartiles

36. Boxplot (Graphical presentation for quantitative data)  The five-number summary can be used to create a simple graph called a boxplot. MinimumQ1MedianQ3Maximum  Form the boxplot, you can quickly detect any skewness in the shape of the distribution and see whether there are any outliers in the data set. Lower fence Upper fence Outlier

37. The Five Number Summary  Compute the five-number summary and construct the box plot of the data 2.473.973.944.11 5.22 1.144.023.411.850.97

38. - The distribution is skewed to the left

39. Interpreting Boxplot - symmetric - L eft skewed or negatively s kewed: the tail is skewed to the left - Right skewed or positively s kewed: the tail is skewed to the right

40. Characteristics Of Skewed Distributions Mean/Median Versus Skewness Mean < Median < Mode Mean > Median > Mode Mean = Median = Mode

41. STEM-AND-LEAF  Another technique that is used to present quantitative data is the stem-and-leaf plot.  An advantage of a stem-and-leaf over a frequency distribution is that by preparing stem-and-leaf, we do not lose information on individual observations.  A stem-and-leaf only for quantitative data.  In a stem-and-leaf display of quantitative data, each value is divided into two portions; a stem and leaf. The leaves for each stem are shown separately in a display. 41

42. Stem-and-leaf plot display a set of data usually large data set. Stem and leaf plots emphasize place value. Stem is for the largest place value(s) of a number and leaf is the smallest place value of a number in your data set.  Step 1: Find the least and the greatest number in the set of data  Step 2: Make two columns with titles STEM and LEAF.  Step 3: Write the digits that form the stem in the STEM column  Step 4:Write the digits that form the leaf for each number in the LEAF column across from the STEM of the number. 42

43. Example:: The following are the scores of 30 college students on a statistics test. 75 52 80 96 65 79 71 87 93 95 69 72 81 61 76 86 79 68 50 92 83 84 77 64 71 87 72 92 57 98  For the score of the first student, which is 75, 7 is the stem and 5 is the leaf. For the score of the second student, which 52, 5 is the stem and 2 is the leaf.  Observed from data, the stems for all scores are 5,6,7,8 and 9 because all scores lie in the range 50 to 98.  After we have listed the stems, we read the leaves for all scores and record them next to the corresponding stems at the right side of the vertical line.

44. Now we read all the scores and write the leaves on the right side of the vertical line in the rows of corresponding stems. By looking at the stem-and-leaf display of test scores, we can observed how the data values are distributed. For example, the stem 7 has the highest frequency, followed by stems 8,9,6 and 5. The leaf for each stem of the stem-and-leaf display of test scores are rank in increasing order and presented as below : StemLeaf 50 2 7 61 4 5 8 9 71 1 2 2 5 6 7 9 9 80 1 3 4 6 7 7 92 2 3 5 6 8 * Analyze – There are 9 out of 30 college students score between 71 and 79. Ranked stem-and-leaf display of test scores. 44 The distribution of data seems skewed to the left tail

45.  Define statistics and its application in engineering.  Explain the concept of population and sample.  Compute and interpret the measures of central tendency (MCT), measures of dispersion (MD) and measures of position (MP).  Construct and interpret several graphical presentation (histogram, box plot, stem and leaf plot).  Explain how graphical presentation are used to compare two or more sets of data.  Compare MCT, MD and MP for two or more sets of data. What you MUST know?