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Probability & Statistics for P-8 Teachers

Probability & Statistics for P-8 Teachers. Chapter 2 Frequency Distributions and Graphs. What is Statistics?. Most people become familiar with probability and statistics through radio, television, newspapers, and magazines. For example, the following statements were found in newspapers.

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Probability & Statistics for P-8 Teachers

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  1. Probability & Statistics for P-8 Teachers Chapter 2 Frequency Distributions and Graphs

  2. What is Statistics? Most people become familiar with probability and statistics through radio, television, newspapers, and magazines. For example, the following statements were found in newspapers. • The U.S. is home to a quarter of the world’s cars • The mean NFL salary is $1.8 million in 2009 • The league minimum is $295,000 • The median salary is $770,000 • The average cost of a wedding is $19,581 • There are only thirteen blimps in the world. • Women who eat fish once a week are 29% less likely to develop heart disease

  3. Various uses of Statistics Is Statistics important in our life? • Statistics is used in almost all fields of human endeavor. • In sports, • number of yards a running back gains during a football game, or the number of hits a baseball player gets in a season. • In public health • concerned with the number of residents who contract a new strain of flu virus during a certain year. • In education • are new methods of teaching are better than old ones? • Furthermore, statistics is used to analyze the results of surveys and as a tool in scientific research to make decisions based on controlled experiments. • Other uses of statistics include operations research, quality control, estimation, and prediction.

  4. Statistics is the science of conducting studies to collect, organize, summarize, analyze, present, interpret and draw conclusions from data. Any values (observations or measurements) that have been collected

  5. What Is Statistics? • Statistics is a collection of tools and methods, designed to help us understand the world. • Statistics are calculations and interpretations made from data. • Statistics helps us to understand the real, imperfect world in which we live.

  6. What Is Statistics?

  7. Think, Show, Tell • There are three simple steps to doing Statistics right: first. Know where you’re headed and why. is about the mechanics of calculating statistics and graphical displays, which are important (but are not the most important part of Statistics). what you’ve learned. You must explain your results so that someone else can understand your conclusions.

  8. Terminology • A variable is a characteristic or attribute that can assume different values. • The values that a variable can assume are called data. • A population consists of all subjects (human or otherwise) that are studied. • A sample is a subset of the population.

  9. Population vs sample

  10. Descriptive and Inferential Statistics • Descriptive statistics consists of the collection, organization, summarization, and presentation of data. • Inferential statistics consists of generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions.

  11. Definitions Variable:A characteristic that varies from one person or thing to another. Qualitative variable:A non-numerically valued variable. Quantitative variable:A numerically valued variable. Discrete variable:A quantitative variable whose possible values can be listed. Continuous variable:A quantitative variable whose possible values form some interval of numbers.

  12. A discrete variable is a quantitative variable that either has a finite number of possible values or a countable number of possible values. The term “countable” means the values result from counting such as 0, 1, 2, 3, and so on. A continuous variable is a quantitative variable that has an infinite number of possible values it can take on and can be measured to any desired level of accuracy.

  13. Organizing Data • Data collected in original form is called raw data. • A frequency distribution is the organization of raw data in table form, using classes and frequencies. • Qualitative data can be placed in categories and organized in categorical frequency distributions.

  14. Categorical Frequency Distribution A random sample of twenty-five patients in a hospital were given a blood test to determine their blood type. Raw Data: Construct a frequency distribution for the data.

  15. Categorical Frequency Distribution Raw Data: IIII IIII II IIII IIII IIII 5 7 9 4 20 28 36 16

  16. Relative Frequency Distribution The percentage of a class is called the relative frequency of the class A table that provides all classes and their relative frequencies is called a relative-frequency distribution. Note that the relative frequencies sum to 1 (100%). IIII IIII II IIII IIII IIII 5 7 9 4 20 28 36 16 5/25 7/25 9/25 4/25 100

  17. Grouped Frequency Distribution • Grouped frequency distributions are used when the range of the data is large. • The smallest and largest possible data values in a class are the lower and upper class limits. Class boundaries separate the classes. • To find a class boundary, average the upper class limit of one class and the lower class limit of the next class.

  18. Grouped Frequency Distribution • Theclass width can be calculated by subtracting • successive lower class limits (or boundaries) • successive upper class limits (or boundaries) • upper and lower class boundaries • The class midpoint Xmcan be calculated by averaging • upper and lower class limits (or boundaries)

  19. Grouped Frequency Distribution Rules for Classes in Grouped Frequency Distributions • There should be 5-20 classes. • The class width should be an odd number. • The classes must be mutually exclusive. • The classes must be continuous. • The classes must be exhaustive. • The classes must be equal in width (except in open-ended distributions).

  20. Grouped Frequency Distribution 112 100 127 120 134 118 105 110 109 112 110 118 117 116 118 122 114 114 105 109 107 112 114 115 118 117 118 122 106 110 116 108 110 121 113 120 119 111 104 111 120 113 120 117 105 110 118 112 114 114 The following data represent the record high temperatures for each of the 50 states. Construct a grouped frequency distribution for the data using 7 classes.

  21. Constructing a Grouped Frequency Distribution STEP 1 Determine the classes. Find the class width by dividing the range by the number of classes 7. Range = High – Low = 134 – 100 = 34 Width = Range/7 = 34/7 = 5 Rounding Rule: Always round up if a remainder.

  22. Constructing a Grouped Frequency Distribution • For convenience sake, we will choose the lowest data value, 100, for the first lower class limit. • The subsequent lower class limits are found by adding the width to the previous lower class limits. Class Limits 100 - 105 - 110 - 115 - 120 - 125 - 130 - • The first upper class limit is one less than the next lower class limit. • The subsequent upper class limits are found by adding the width to the previous upper class limits. 104 109 114 119 124 129 134

  23. Constructing a Grouped Frequency Distribution • The class boundary is midway between an upper class limit and a subsequent lower class limit. 104,104.5,105 99.5 - 104.5 104.5 - 109.5 109.5 - 114.5 114.5 - 119.5 119.5 - 124.5 124.5 - 129.5 129.5 - 134.5

  24. Constructing a Grouped Frequency Distribution STEP 2 Tally the data. STEP 3 Find the frequencies. 99.5 - 104.5 104.5 - 109.5 109.5 - 114.5 114.5 - 119.5 119.5 - 124.5 124.5 - 129.5 129.5 - 134.5 2 8 18 13 7 1 1

  25. Cumulative Frequency Distribution Sometimes it is helpful to keep a running total of the frequencies. This is called a cumulative frequency distribution 99.5 - 104.5 104.5 - 109.5 109.5 - 114.5 114.5 - 119.5 119.5 - 124.5 124.5 - 129.5 129.5 - 134.5 2 8 18 13 7 1 1 2 10 28 41 48 49 50

  26. Data Analysis • There are three simple rules of data analysis: • Make a picture—things may be revealed that are not obvious in the raw data. These will be things to think about. • Make a picture—important features of and patterns in the data will show up. You may also see things that you did not expect. • Make a picture—the best way to tell others about your data is with a well-chosen picture.

  27. Let’s Draw Pictures Most Common Graphs in Research • Histogram • Frequency Polygon • Cumulative Frequency Polygon (Ogive) • Bar Graph • Pareto Chart • Time-Series Graph • Pie Graph • Dot Plot • Stem and Leaf Plot • Scatter Plot

  28. Histograms The histogram is a graph that displays the data by using vertical bars of various heights to represent the frequencies of the classes. The class boundaries are represented on the horizontal axis.

  29. Histograms Construct a histogram to represent the data for the record high temperatures for each of the 50 states Histograms use class boundaries and frequencies of the classes. 99.5 - 104.5 104.5 - 109.5 109.5 - 114.5 114.5 - 119.5 119.5 - 124.5 124.5 - 129.5 129.5 - 134.5 2 8 18 13 7 1 1

  30. Histograms Class boundaries represent the x-axis Frequencies represent the y-axis

  31. Frequency Polygon • The frequency polygon is a graph that displays the data by using lines that connect points plotted for the frequencies at the class midpoints. The frequencies are represented by the heights of the points. • The class midpoints are represented on the horizontal axis.

  32. Frequency Polygons Construct a frequency polygon to represent the data for the record high temperatures for each of the 50 states Frequency polygons use class midpoints and frequencies of the classes. 102 107 112 117 122 127 132 2 8 18 13 7 1 1

  33. Frequency Polygons A frequency polygon is anchored on the x-axis before the first class and after the last class.

  34. Ogives(Cumulative Frequency Polygon) • The ogiveis a graph that represents the cumulative frequencies for the classes in a frequency distribution. • The upper class boundaries are represented on the horizontal axis.

  35. Ogives Construct an Ogive to represent the data for the record high temperatures for each of the 50 states Ogives use upper class boundaries and cumulative frequencies of the classes. 99.5 - 104.5 104.5 - 109.5 109.5 - 114.5 114.5 - 119.5 119.5 - 124.5 124.5 - 129.5 129.5 - 134.5 2 10 28 41 48 49 50

  36. Ogives

  37. Relative Frequencies If proportions are used instead of frequencies, the graphs are called relative frequency graphs. Relative frequency graphs are used when the proportion of data values that fall into a given class is more important than the actual number of data values that fall into that class.

  38. Construct a histogram, frequency polygon, and ogive using relative frequencies for the distribution (shown here) of the miles that 20 randomly selected runners ran during a given week.

  39. Graphs

  40. Shapes of Distributions

  41. Shapes of Distributions

  42. Other Types of GraphsBar Graphs

  43. Other Types of GraphsPareto Charts

  44. Other Types of GraphsTime Series Graphs

  45. Other Types of GraphsPie Graphs

  46. Other Types of GraphsDot Plots

  47. Other Types of GraphsStem and Leaf Plots A stem and leaf plot is a data plot that uses part of a data value as the stem and part of the data value as the leaf to form groups or classes. It has the advantage over grouped frequency distribution of retaining the actual data while showing them in graphic form.

  48. Stem and Leaf Plots At an outpatient testing center, the number of cardiograms performed each day for 20 days is shown. Construct a stem and leaf plot for the data. 25 31 20 32 13 14 43 2 57 23 36 32 33 32 44 32 52 44 51 45

  49. Stem and Leaf Plots First, we list the leading digits of the numbers in the table(0, 1,. . ., 5)in a column, as shown to the left of the vertical rule. Next, we write the final digit of each number from the table to the right of the vertical rule in the row containing the appropriate leading digit. 25 31 20 32 13 14 43 2 57 23 36 32 33 32 44 32 52 44 51 45 Unordered Stem Plot Ordered Stem Plot 0 2 1 3 4 2 5 0 3 3 1 2 6 2 3 2 2 4 3 4 4 5 5 7 2 1

  50. Scatter Plots and Correlation • A scatter plot is a graph of the ordered pairs (x, y) of numbers consisting of the independent variable x and the dependent variable y. • A scatter plot is used to determine if a relationship exists between the two variables.

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