1. STATISTICS

2. Statistics * Statistics is the area of science that deals with collection, organization, analysis, and interpretation of data. * A collection of numerical information is called statistics. * Because many aspects of engineering practice involve working with data, obviously some knowledge of statistics is important to an engineer.

3. * the methods of statistics allow scientists and engineers to design valid experiments and to draw reliable conclusions from the data they produce Specifically, statistical techniques can be a powerful aid in designing new products and systems, improving existing designs, and improving production process.

4. Basic Terms in Statistics * Population - Entire collection of individuals which are characteristic being studied. * Sample - A portion, or part of the population interest. * Variable - Characteristics of the individuals within the population. * Observation -Value of variable for an element. * Data Set -A collection of observation on one or more variables.

5. Collecting Data * Direct observation The simplest method of obtaining data. Advantage: relatively inexpensive Disadvantage: difficult to produce useful information since it does not consider all aspects regarding the issues. * Experiments More expensive methods but better way to produce data Data produced are called experimental

6.  Surveys Most familiar methods of data collection Depends on the response rate  Personal Interview Has the advantage of having higher expected response rate Fewer incorrect respondents.

7. Grouped Data Vs Ungrouped Data * Grouped data - Data that has been organized into groups (into a frequency distribution). * Ungrouped data - Data that has not been organized into groups. Also called as raw data.

9. Graphical Data Presentation * Data can be summarized or presented in two ways: 1. Tabular 2. Charts/graphs. * The presentations usually depends on the type (nature) of data whether the data is in qualitative (such as gender and ethnic group) or quantitative (such as income and CGPA).

10. Data Presentation of Qualitative Data * Tabular presentation for qualitative data is usually in the form of frequency table that is a table represents the number of times the observation occurs in the data. *Qualitative :- characteristic being studied is nonnumeric. Examples:- gender, religious affiliation or eye color. * The most popular charts for qualitative data are: 1. bar chart/column chart; 2. pie chart; and 3. line chart.

11. Types of Graph Qualitative Data

12. Example 1.1: frequency table * Bar Chart: used to display the frequency distribution in the graphical form. Example 1.2: Observation Frequency Malay33 Chinese9 Indian6 Others2

13. * Pie Chart: used to display the frequency distribution. It displays the ratio of the observations Example 1.3 : * Line chart: used to display the trend of observations. It is a very popular display for the data which represent time. Example 1.4 JanFebMarAprMayJunJulAugSepOctNovDec 1075 3972603161421149

14. Data Presentation Of Quantitative Data * Tabular presentation for quantitative data is usually in the form of frequency distribution that is a table represent the frequency of the observation that fall inside some specific classes (intervals). *Quantitative : variable studied are numerically. Examples:- balanced in accounts, ages of students, the life of an automobiles batteries such as 42 months). * Frequency distribution: A grouping of data into mutually exclusive classes showing the number of observations in each class.

15. * There are few graphs available for the graphical presentation of the quantitative data. The most popular graphs are: 1. histogram; 2. frequency polygon; and 3. ogive.

16. Example 1.5: Frequency Distribution Weight (Rounded decimal point)Frequency 60-625 63-6518 66-6842 69-7127 72-748 * 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. Example 1.6:

17. * Frequency Polygon: looks like the line chart except that the horizontal axis represent the class mark of the data which is quantitative in nature. Example 1.7 : * Ogive: line graph with the horizontal axis represent the upper limit of the class interval while the vertical axis represent the cummulative frequencies. Example 1.8 :

19. Constructing Frequency Distribution * When summarizing large quantities of raw data, it is often useful to distribute the data into classes. Table 1.1 shows that the number of classes for Students` weight. * A frequency distribution for quantitative data lists all the classes and the number of values that belong to each class. * Data presented in the form of a frequency distribution are called grouped data. WeightFrequency 60-625 63-6518 66-6842 69-7127 72-748 Total100 Table 1.1: Weight of 100 male students in XYZ university

20. * For quantitative data, an interval that includes all the values that fall within two numbers; the lower and upper class which is called class. * Class is in first column for frequency distribution table. *Classes always represent a variable, non-overlapping; each value is belong to one and only one class. * The numbers listed in second column are called frequencies, which gives the number of values that belong to different classes. Frequencies denoted by f. WeightFrequency 60-625 63-6518 66-6842 69-7127 72-748 Total100 Variable Frequency column Third class (Interval Class) Lower Limit of the fifth class Frequency of the third class. Upper limit of the fifthclass Table 1.2 : Weight of 100 male students in XYZ university

21. * The class boundary is given by the midpoint of the upper limit of one class and the lower limit of the next class. * The difference between the two boundaries of a class gives the class width; also called class size. Formula: - Class Midpoint or Mark Class midpoint or mark = (Lower Limit + Upper Limit)/2 - Finding The Number of Classes Number of classes, c = - Finding Class Width For Interval Class (Sturge`s Rule) class width, i = (Largest value – Smallest value)/Number of classes * Any convenient number that is equal to or less than the smallest values in the data set can be used as the lower limit of the first class.

22. Example 1.9: From Table 1.1: Class Boundary Weight (Class Interval) Class BoundaryFrequency 60-6259.5-62.55 63-6562.5-65.518 66-6865.5-68.542 69-7168.5-71.527 72-7471.5-74.58 Total100

23. Example 1.10: Given a raw data as below: 2727272827202528 2628262831302626 33 283539 a) How many classes that you recommend? b) How many class interval? c) Build a frequency distribution table. d) What is the lower boundary for the first class?

24. Cumulative Frequency Distributions * A cumulative frequency distribution gives the total number of values that fall below the upper boundary of each class. * In cumulative frequency distribution table, each class has the same lower limit but a different upper limit. Table 1.3: Class Limit, Class Boundaries, Class Width, Cumulative Frequency Weight (Class Interva;) Number of Students, f Class Boundaries Cumulative Frequency 60-62559.5-62.5 5 63-651862.5-65.5 5 + 18 = 23 66-684265.5-68.5 23 + 42 = 65 69-712768.5-71.5 65 + 27 =92 72-74871.5-74.5 92 + 8 = 100 100

25. Exercise 1 : The data below represent the waiting time (in minutes) taken by 30 customers at one local bank. 2531203022323728 2923352529352927 2332313224352135 352233243943 * Construct a frequency distribution and cumulative frequency distribution table. * Construct a histogram.

26. Measures of Central Tendency Measures of Dispersion Measures of Position

27. Data Summary Summary statistics are used to summarize a set of observations. Two basic summary statistics are measures of central tendency and measures of dispersion. Measures of Central Tendency * Mean * Median * Mode Measures of Dispersion * Range * Variance * Standard deviation Measures of Position * Z scores * Percentiles * Quartiles * Outliers

28. 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:

29. Example 1.11 (Ungrouped data): Mean for the sets of data 3,5,2,6,5,9,5,2,8,6 Solution :

30. Example 1.12 (Grouped Data): Use the frequency distribution of weights 100 male students in XYZ university, to find the mean. WeightFrequency 60-62 63-65 66-68 69-71 72-74 5 18 42 27 8

31. Solution : Weight (Class Interval Frequency, fClass Mark, x fx 60-62 63-65 66-68 69-71 72-74 5 18 42 27 8

32. * 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 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:

33. Example 1.13 (Ungrouped data): The median for data 4,6,3,1,2,5,7 is 4 Rearrange the data : 1,2,3,4,5,6,7 median

34. Example 1.14 (Grouped Data): The sample median for frequency distribution as in example 1.12 Solution: Weight (Class Interval Frequency, f Class Mark, x fxCumulative Frequency, F Class Boundary 60-62 63-65 66-68 69-71 72-74 5 18 42 27 8 61 64 67 70 73 305 1152 2814 1890 584

36. * Mode for grouped data

37. Example 1.15 (Ungrouped data) Find the mode for the sets of data 3, 5, 2, 6, 5, 9, 5, 2, 8, 6 Mode = number occurring most frequently = 5 Example 1.16 Find the mode of the sample data below Solution: Weight (Class Interval Frequency, f Class Mark, x fxCumulative Frequency, F Class Boundary 60-62 63-65 66-68 69-71 72-74 5 18 42 27 8 61 64 67 70 73 305 1152 2814 1890 584 5 23 65 92 100 59.5-62.5 62.5-65.5 65.5-68.5 68.5-71.5 71.5-74.5 Total1006745 Mode class

38. 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) The variance for the grouped data: * or (for sample) * or (for population)

39. * 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.  The positive square root of the variance is the standard deviation

40. Example 1.17 (Ungrouped data) Find the variance and standard deviation of the sample data : 3, 5, 2, 6, 5, 9, 5, 2, 8, 6

41. Example 1.18 (Grouped data) Find the variance and standard deviation of the sample data below: Weight (Class Interval Frequency, f Class Mark, x fxCumulative Frequency, F Class Boundary 60-62 63-65 66-68 69-71 72-74 5 18 42 27 8 61 64 67 70 73 305 1152 2814 1890 584 5 23 65 92 100 59.5-62.5 62.5-65.5 65.5-68.5 68.5-71.5 71.5-74.5 Total1006745

42. Exercise 2: The defects from machine A for a sample of products were organized into the following: What is the mean, median, mode, variance and standard deviation. Defects (Class Interval) Number of products get defect, f (frequency) 2-61 7-114 12-1610 17-213 22-262

43. Exercise 3: 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