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Chapter01

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  • 1. Chapter 1 The Role of Statistics
  • 2. Three Reasons to Study Statistics <ul><li>Being an informed “Information Consumer” </li></ul><ul><ul><li>Extract information from charts and graphs </li></ul></ul><ul><ul><li>Follow numerical arguments </li></ul></ul><ul><ul><li>Know the basics of how data should be gathered, summarized and analyzed to draw statistical conclusions </li></ul></ul>
  • 3. Three Reasons to Study Statistics <ul><li>Understanding and Making Decisions </li></ul><ul><ul><li>Decide if existing information is adequate </li></ul></ul><ul><ul><li>Collect more information in an appropriate way </li></ul></ul><ul><ul><li>Summarize the available data effectively </li></ul></ul><ul><ul><li>Analyze the available data </li></ul></ul><ul><ul><li>Draw conclusions, make decisions, and assess the risks of an incorrect decision </li></ul></ul>
  • 4. Three Reasons to Study Statistics <ul><li>Evaluate Decisions That Affect Your Life </li></ul><ul><ul><li>Help understand the validity and appropriateness of processes and decisions that effect your life </li></ul></ul>
  • 5. What is Statistics? <ul><li>Statistics is the science of </li></ul><ul><ul><li>Collecting data </li></ul></ul><ul><ul><li>Analyzing data </li></ul></ul><ul><ul><li>Drawing conclusions from data </li></ul></ul>
  • 6. Major Branches of Statistics <ul><li>Descriptive Statistics </li></ul><ul><ul><li>Organizing, Summarizing Information </li></ul></ul><ul><ul><li>Graphical techniques </li></ul></ul><ul><ul><li>Numerical techniques </li></ul></ul><ul><li>Inferential Statistics </li></ul><ul><ul><li>Estimation </li></ul></ul><ul><ul><li>Decision making </li></ul></ul>
  • 7. Important Terms <ul><li>Population – The entire collection of individuals or objects about which information is desired is called the population. </li></ul><ul><li>Collection of data from every member of the population, </li></ul><ul><ul><li>allows a question about the population to be definitively answered. </li></ul></ul><ul><ul><li>may be expensive, impractical, or sometimes impossible. </li></ul></ul>
  • 8. Important Terms <ul><li>Sample – A sample is a subset of the population, selected for study in some prescribed manner. </li></ul><ul><li>Using sample data rather than population data is </li></ul><ul><ul><li>more practical than a census. </li></ul></ul><ul><ul><li>Gives variable results with some possibility of a wrong conclusion being adopted. </li></ul></ul>
  • 9. Discussion on Important Terms <ul><li>Generally it not reasonable, feasible or even possible to survey a population so that descriptions and decisions about the population are made based on using a sample. </li></ul><ul><li>The study of statistics deals with understanding how to obtain samples and work with the sample data to make statistically justified decisions. </li></ul>
  • 10. Important Terms <ul><li>Variable – A variable is any characteristic whose value may change from one individual to another </li></ul><ul><li>Examples: </li></ul><ul><ul><li>Brand of television </li></ul></ul><ul><ul><li>Height of a building </li></ul></ul><ul><ul><li>Number of students in a class </li></ul></ul>
  • 11. Important Terms <ul><li>Data results from making observations either on a single variable or simultaneously on two or more variables. </li></ul><ul><li>A univariate data set consists of observations on a single variable made on individuals in a sample or population. </li></ul>
  • 12. Important Terms <ul><li>A bivariate data set consists of observations on two variables made on individuals in a sample or population. </li></ul><ul><li>A multivariate data set consists of observations on two or more variables made on individuals in a sample or population. </li></ul>
  • 13. Data Sets <ul><ul><li>A univariate data set is categorical (or qualitative ) if the individual observations are categorical responses. </li></ul></ul><ul><ul><li>A univariate data set is numerical (or quantitative ) if the individual observations are numerical responses where numerical operations generally have meaning . </li></ul></ul>
  • 14. Examples of categorical Data <ul><li>The brand of TV owned by the six people that work in a small office </li></ul><ul><ul><li>RCA Magnavox Zenith Phillips </li></ul></ul><ul><ul><li>GE RCA … </li></ul></ul><ul><li>The hometowns of the 6 students in the first row of seats </li></ul><ul><ul><ul><li>Mendon Victor Bloomfield </li></ul></ul></ul><ul><ul><ul><li>Victor Pittsford Bloomfield </li></ul></ul></ul><ul><li>The zipcodes * (of the hometowns) of the 6 students in the first row of seats. </li></ul><ul><li>*Since numerical operations with zipcodes make no sense, the zipcodes are categorical rather than numeric. </li></ul>
  • 15. Types of Numerical Data <ul><li>Numerical data is discrete if the possible values are isolated points on the number line. </li></ul><ul><li>Numerical data is continuous if the set of possible values form an entire interval on the number line. </li></ul>
  • 16. Examples of Discrete Data <ul><li>The number of costumers served at a diner lunch counter over a one hour time period is observed for a sample of seven different one hour time periods </li></ul><ul><ul><li>13 22 31 18 41 27 32 </li></ul></ul><ul><li>The number of textbooks bought by students at a given school during a semester for a sample of 16 students </li></ul><ul><ul><ul><li>5 3 6 8 6 1 3 6 12 </li></ul></ul></ul><ul><ul><ul><li>3 5 7 6 7 5 4 </li></ul></ul></ul>
  • 17. Examples of Continuous Data <ul><li>The height of students that are taking a Data Analysis at a local university is studied by measuring the heights of a sample of 10 students. </li></ul><ul><li>72.1” 64.3” 68.2” 74.1” 66.3” </li></ul><ul><li>61.2” 68.3” 71.1” 65.9” 70.8” </li></ul><ul><li>Note: Even though the heights are only measured accurately to 1 tenth of an inch, the actual height could be any value in some reasonable interval. </li></ul>
  • 18. Examples of Continuous Data <ul><li>The crushing strength of a sample of four jacks used to support trailers. </li></ul><ul><ul><li>7834 lb 8248 lb 9817 lb 8141 lb </li></ul></ul><ul><li>Gasoline mileage (miles per gallon) for a brand of car is measured by observing how far each of a sample of seven cars of this brand of car travels on ten gallons of gasoline. </li></ul><ul><ul><li>23.1 26.4 29.8 25.0 25.9 </li></ul></ul><ul><ul><li>22.6 24.3 </li></ul></ul>
  • 19. Frequency Distributions <ul><li>A frequency distribution for categorical data is a table that displays the possible categories along with the associated frequencies or relative frequencies. </li></ul><ul><li>The frequency for a particular category is the number of times the category appears in the data set. </li></ul>
  • 20. Frequency Distributions <ul><li>The relative frequency for a particular category is the fraction or proportion of the time that the category appears in the data set. It is calculated as </li></ul>When the table includes relative frequencies, it is sometimes referred to as a relative frequency distribution .
  • 21. Classroom Data Example This slide along with the next contains a data set obtained from a large section of students taking Data Analysis in the Winter of 1993 and will be utilized throughout this slide show in the examples.
  • 22. Classroom Data Example continued
  • 23. Frequency Distribution Example The data in the column labeled vision is the answer to the question, “What is your principle means of correcting your vision?” The results are tabulated below
  • 24. Bar Chart – Procedure <ul><li>Draw a horizontal line, and write the category names or labels below the line at regularly spaced intervals. </li></ul><ul><li>Draw a vertical line, and label the scale using either frequency (or relative frequency). </li></ul><ul><li>Place a rectangular bar above each category label. The height is the frequency (or relative frequency) and all bars have the same width. </li></ul>
  • 25. Bar Chart – Example (frequency)
  • 26. Bar Chart – (Relative Frequency)
  • 27. Another Example
  • 28. Dotplots - Procedure <ul><li>Draw a horizontal line and mark it with an appropriate measurement scale. </li></ul><ul><li>Locate each value in the data set along the measurement scale, and represent it by a dot. If there are two or more observations with the same value, stack the dots vertically. </li></ul>
  • 29. Dotplots - Example Using the weights of the 79 students
  • 30. Dotplots – Example continued To compare the weights of the males and females we put the dotplots on top of each other, using the same scales.

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