Lecture 3 Graphs.ppt
- 1. Business statistics LECTURE 3 Descriptive Charts and Graphs Instructor: Araiz Murad Dahir.
- 2. Slide 2-2 Ungrouped vs Grouped Data • Ungrouped data • have not been summarized in any way • are also called raw data • Grouped data • have been organized into a frequency distribution
- 3. Slide 2-3 Example of Ungrouped Data 42 30 53 50 52 30 55 49 61 74 26 58 40 40 28 36 30 33 31 37 32 37 30 32 23 32 58 43 30 29 34 50 47 31 35 26 64 46 40 43 57 30 49 40 25 50 52 32 60 54 Ages of a Sample of Managers from Urban Child Care Centers in the United States
- 4. Slide 2-4 Frequency Distribution of Child Care Manager’s Ages Class Interval Frequency 20-under 30 6 30-under 40 18 40-under 50 11 50-under 60 11 60-under 70 3 70-under 80 1
- 5. Slide 2-5 Data Range 42 30 53 50 52 30 55 49 61 74 26 58 40 40 28 36 30 33 31 37 32 37 30 32 23 32 58 43 30 29 34 50 47 31 35 26 64 46 40 43 57 30 49 40 25 50 52 32 60 54 Smallest Largest Range = Largest - Smallest = 74 - 23 = 51
- 6. Slide 2-6 Relative Frequency Relative Class Interval Frequency Frequency 20-under 30 6 .12 30-under 40 18 .36 40-under 50 11 .22 50-under 60 11 .22 60-under 70 3 .06 70-under 80 1 .02 Total 50 1.00 6 50 18 50
- 7. Slide 2-7 Cumulative Frequency Cumulative Class Interval Frequency CF 20-under 30 6 6 30-under 40 18 24 40-under 50 11 35 50-under 60 11 46 60-under 70 3 49 70-under 80 1 50 Total 50 18 + 6 11 + 24
- 8. Slide 2-8 Relative Frequencies, and Cumulative Frequencies Relative Cumulative Class IntervalFrequency Frequency Frequency 20-under 30 6 .12 6 30-under 40 18 .36 24 40-under 50 11 .22 35 50-under 60 11 .22 46 60-under 70 3 .06 49 70-under 80 1 .02 50 Total 50 1.00
- 9. Slide 2-9 Cumulative Relative Frequencies Cumulative Relative Cumulative Relative Class Interval Frequency Frequency Frequency Frequency 20-under 30 6 .12 6 .12 30-under 40 18 .36 24 .48 40-under 50 11 .22 35 .70 50-under 60 11 .22 46 .92 60-under 70 3 .06 49 .98 70-under 80 1 .02 50 1.00 Total 50 1.00
- 10. Common Statistical Graphs • Histogram -- vertical bar chart of frequencies • Line Graph. • Time Series Graph. • Pie Chart -- proportional representation for categories of a whole
- 11. Histogram Class Interval Frequency 20-under 30 6 30-under 40 18 40-under 50 11 50-under 60 11 60-under 70 3 70-under 80 1 0 10 20 0 10 20 30 40 50 60 70 80 Years Frequency
- 12. Slide 2-12 Histogram Construction Class Interval Frequency 20-under 30 6 30-under 40 18 40-under 50 11 50-under 60 11 60-under 70 3 70-under 80 1 0 10 20 0 10 20 30 40 50 60 70 80 Years Frequency
- 13. QUESTION • Make Histogram of SUSPECTED NUCLEAR WEAPONS. • 1. USA 15500 • 2. RUSSIA 15000 • 3. FRANCE 482 • 4. CHINA 410 • 5. UK 200 • 6. ISRAEL 100 Slide 2-13
- 14. Suspected Nuclear Weapons 15500 15000 482 410 200 100 60 20 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 U n i t e d S t a t e s R u s s i a F r a n c e C h i n a U n i t e d K i n g d o m I s r a e l I n d i a P a k i s t a n Source: Center for Defense Information
- 15. Slide 2-15 Line Graph - A line graph, also known as a line chart, is a type of chart used to visualize the value of something over time. - For example, a finance department may plot the change in the amount of cash the company has on hand over time. - Data points are plotted and connected by a line in a "dot-to-dot" fashion.
- 16. Slide 2-16 - More than one line may be plotted in the same axis as a form of comparison. - For example, you could create a line graph comparing Fatal accidents by vehicle types. In this case each line would have a different color, identified in a legend. Line Graph
- 17. Slide 2-17
- 18. Slide 2-18 - A time series graph is a line graph of repeated measurements taken over regular time intervals. - Time is always shown on the horizontal axis. - On time series graphs data points are drawn at regular intervals and the points joined, usually with straight lines. - Time series graphs help to show trends or patterns. Line Graph type: Time series graph
- 19. Slide 2-19 Pakistan - GDP growth (annual %)
- 20. Slide 2-20
- 21. Slide 2-21
- 22. Slide 2-22 Pie Chart: Complaints by Shalimar express Passengers COMPLAINT NUMBER PROPORTION DEGREES Stations, etc. 28,000 .40 144.0 Train Performance 14,700 .21 75.6 Equipment 10,500 .15 50.4 Personnel 9,800 .14 50.6 Schedules, etc. 7,000 .10 36.0 Total 70,000 1.00 360.0
- 23. © 2002 Thomson / South-Western Slide 2-23 Complaints by Amtrak Passengers Stations, Etc. 40% Train Performance 21% Equipment 15% Personnel 14% Schedules, Etc. 10%
- 24. Slide 2-24 Second Quarter Truck Production in the U.S. (Hypothetical values) 2d Quarter Truck Production Company A B C D E Totals 357,411 354,936 160,997 34,099 12,747 920,190
- 25. Slide 2-25 39% 39% 17% 4% 1% A B C D E Second Quarter U.S. Truck Production
- 26. Slide 2-26 Pie Chart Calculations for Truck Producer Company A 2d Quarter Truck Production Proportion Degrees Company A B C D E Totals 357,411 354,936 160,997 34,099 12,747 920,190 .388 .386 .175 .037 .014 1.000 140 139 63 13 5 360 357,411 920,190 = .388 360 =
- 27. Slide 1-27 Levels of Data Measurement • Nominal - Lowest level of measurement • Ordinal • Interval • Ratio - Highest level of measurement
- 28. Slide 1-28 Nominal Level Data • Numbers are used to classify or categorize Example: Employment Classification – 1 for Educator – 2 for Construction Worker – 3 for Manufacturing Worker Example: Ethnicity – 1 for African-American – 2 for Anglo-American – 3 for Hispanic-American – 4 for Oriental-American
- 29. Slide 1-29 Ordinal Level Data • Numbers are used to indicate rank or order – Relative magnitude of numbers is meaningful – Differences between numbers are not comparable Example: Taste test ranking of three brands of soft drink Example: Position within an organization – 1 for President – 2 for Vice President – 3 for Plant Manager – 4 for Department Supervisor – 5 for Employee
- 30. © 2002 Thomson / South-Western Slide 1-30 Example of Ordinal Measurement f i n i s h 1 2 3 4 5 6
- 31. © 2002 Thomson / South-Western Slide 1-31 Ordinal Data Faculty and staff should receive preferential treatment for parking space. 1 2 3 4 5 Strongly Agree Agree Strongly Disagree Disagree Neutral
- 32. © 2002 Thomson / South-Western Slide 1-32 Interval Level Data • Distances between consecutive integers are equal – Relative magnitude of numbers is meaningful – Differences between numbers are comparable – Location of origin, zero, is arbitrary Examples: Fahrenheit Temperature, Calendar Time, Monetary Units
- 33. © 2002 Thomson / South-Western Slide 1-33 Ratio Level Data • Highest level of measurement – Relative magnitude of numbers is meaningful – Differences between numbers are comparable Examples: Height, Weight, and Volume Monetary Variables, such as Revenues, and Expenses Financial ratios, such as P/E Ratio, Inventory Turnover
- 34. © 2002 Thomson / South-Western Slide 1-34 Usage Potential of Various Levels of Data Nominal Ordinal Interval Ratio
- 35. © 2002 Thomson / South-Western Slide 1-35 Qualitative vs Quantitative Data Qualitative Data is data of the nominal or ordinal level that classifies by a label or category. The labels may be numeric or nonnumeric. Quantitative Data is data of the interval or ratio level that measures on a naturally occurring numeric scale.
- 36. © 2002 Thomson / South-Western Slide 1-36 Discrete and Continuous Data Discrete Data is numeric data in which the values can come only from a list of specific values. Discrete data results from a counting process. Continuos Data is numeric data that can take on values at every point over a given interval. Continuous data result from a measuring process.
- 37. © 2002 Thomson / South-Western Slide 1-37 Summary of Data Classifications Data nal Ordinal Interl Ratio Qualitative (Categorical) Quantitative Nonnumeric Numeric Discrete Numeric Discrete or Continuous Data Nominal Ordinal Interval Ratio Quantitative Qualitative Numeric Numeric Discrete Nonnumeric Discrete or Continuous