1. Subbulakshmi Murugappan H/P: 016-4054017 subbulakshmi@unimap.edu.my

2.  Basic Statistics  Statistics in Engineering  Collecting Engineering Data  Data Summary and Presentation  Probability Distributions - Discrete Probability Distribution - Continuous Probability Distribution  Sampling Distributions of the Mean and Proportion

3.  Statistics is the area of science that deals with collection, organization, analysis, and interpretation of data.  A collection of numerical information is called statistics.

4.  Because many aspects of engineering practice involve working with data, obviously some knowledge of statistics is important to an engineer.

5.  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.

6.  Population - Entire collection of individuals which are characteristic being studied.  Sample - A portion, or part of the population interest.  Variable - Characteristics which make different values.  Observation -Value of variable for an element.  Data Set -A collection of observation on one or more variables.

7.  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

8.  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.

9.  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.

10. 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).

11. 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.

12. Types of Graph Qualitative Data

13. Example: frequency table  Bar Chart: used to display the frequency distribution in the graphical form. Observation Frequency Malay33 Chinese9 Indian6 Others2

14.  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

15. 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.

16.  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.

17. Example: Frequency Distribution CGPA (Class)Frequency 2.50 - 2.75 2 2.75 - 3.00 10 3.00 - 3.25 15 3.25 - 3.50 13 3.50 - 3.75 7 3.75 - 4.00 3  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.

18.  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.

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` CGPA.  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. CGPA (Class)Frequency 2.50 - 2.752 2.75 - 3.0010 3.00 - 3.2515 3.25 - 3.5013 3.50 - 3.757 3.75 - 4.003 Total50 Table 1.1:The Fequency Distribution of the Students’ CGPA

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. Weekly Earnings (dollars) Number of Employees, f 801-10009 1001-120022 1201-140039 1401-160015 1601-18009 1801-20006 Variable Frequency column Third class (Interval Class) Lower Limit of the sixth class Frequency of the third class. Upper limit of the sixth class Table 1.2 : Weekly Earnings of 100 Employees of a Company

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 = - Finding Class Width For Interval Class Approximate class width = (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: Given a raw data as below: 2727272827242528 2628262831302626 a) How many classes that you recommend? b) What is the class interval? c) What is the lower boundary for the first class? d) Build a frequency distribution table.

23. 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 Weekly Earnings (dollars) (Class Limit) Number of Employees, f Class Boundaries Class Width Cumulative Frequency 801-10009800.5 – 1000.52009 1001-1200221000.5 – 1200.52009 + 22 = 31 1201-1400391200.5 – 1400.520031 + 39 = 70 1401-1600151400.5 – 1600.520070 + 15 = 85 1601-180091600.5 – 1800.520085 + 9 = 94 1801-200061800.5 – 2000.520094 + 6 = 100

24. 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

25.  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:

26.  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:

28. Cumulative Frequency Table 1.4 : Example 1.12

32.  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)

33.  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

34.  7, 6, 8, 5, 9,4, 7, 7, 6, 6  Range = 9-4=5  Mean  Variance  Standard Deviation

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

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

37.  The data below represent the waiting time (in minutes) taken by 30 customers at one local bank. 2531203022323728 2923352529352927 2332313224352135 352233243943 a)Construct a frequency table. b) Find the mean, mode, median, variance and standard deviation.. c) Construct a histogram.

38.  The Apollo space program lasted from 1967 until 1972 and included 13 missions. The missions lasted from as little as 7 hours to as long as 301 hours. The duration of each flight is as below. Find mean, median, standard deviation. (Counted as a population) 91952413012162607244 19214710295142