1. Chapter 1 The Role of Statistics

2. Three Reasons to Study Statistics 1.Being an informed “Information Consumer” Extract information from charts and graphs Follow numerical arguments Know the basics of how data should be gathered, summarized and analyzed to draw statistical conclusions

3. Three Reasons to Study Statistics 2.Understanding and Making Decisions Decide if existing information is adequate Collect more information in an appropriate way Summarize the available data effectively Analyze the available data Draw conclusions, make decisions, and assess the risks of an incorrect decision

4. Three Reasons to Study Statistics 3.Evaluate Decisions That Affect Your Life Help understand the validity and appropriateness of processes and decisions that effect your life

5. What is Statistics? Statistics is the science of Collecting data Analyzing data Drawing conclusions from data

6. Descriptive Statistics The methods of organizing & summarizing data Graphical techniques Numerical techniques

7. Inferential Statistics Involves making generalizations from a sample to a population Estimation Decision making

8. Population The entire collection of individuals or objects about which information is desired

9. Sample A subset of the population, selected for study in some prescribed manner

10. Discussion on Important Terms 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. The study of statistics deals with understanding how to obtain samples and work with the sample data to make statistically justified decisions.

11. Variable any characteristic whose value may change from one individual to another

12. Data observations on single variable or simultaneously on two or more variables

13. Types of variables

14. Categorical variables or qualitative identifies basic differentiating characteristics of the population

15. Numerical variables or quantitative observations or measurements take on numerical values makes sense to average these values two types - discrete & continuous

16. Discrete (numerical) listable set of values usually counts of items

17. Examples of Discrete Data  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 13 22 31 18 41 27 32  The number of textbooks bought by students at a given school during a semester for a sample of 16 students 5 3 6 8 6 1 3 6 12 3 5 7 6 7 5 4

18. Continuous (numerical) data can take on any values in the domain of the variable usually measurements of something

19. Examples of Continuous Data  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. 72.1” 64.3” 68.2” 74.1” 66.3” 61.2” 68.3” 71.1” 65.9” 70.8” 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.

20. Examples of Continuous Data The crushing strength of a sample of four jacks used to support trailers. 7834 lb 8248 lb 9817 lb 8141 lb 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. 23.1 26.4 29.8 25.0 25.9 22.6 24.3

21. Classification by the number of variables Univariate - data that describes a single characteristic of the population Bivariate - data that describes two characteristics of the population Multivariate - data that describes more than two characteristics (beyond the scope of this course

22. Identify the following: gender age hair color smoker systolic blood pressure number of girls in class categorical numerical categorical numerical

23. Frequency Distributions A frequency distribution for categorical data is a table that displays the possible categories along with the associated frequencies or relative frequencies. The frequency for a particular category is the number of times the category appears in the data set.

24. Frequency Distributions 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 When the table includes relative frequencies, it is sometimes referred to as a relative frequency distribution. frequency relative frequency number of observations in the data set 

25. 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 2010 and will be utilized throughout this slide show in the examples.

26. Classroom Data Example continued

27. 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

28. Bar Chart – Procedure 1.Draw a horizontal line, and write the category names or labels below the line at regularly spaced intervals. 2.Draw a vertical line, and label the scale using either frequency (or relative frequency). 3.Place a rectangular bar above each category label. The height is the frequency (or relative frequency) and all bars have the same width.

29. Bar Chart – Example (frequency)

30. Bar Chart – (Relative Frequency)

31. Another Example

32. Dotplots - Procedure 1.Draw a horizontal line and mark it with an appropriate measurement scale. 2.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.

33. Dotplots - Example Using the weights of the 79 students

34. To compare the weights of the males and females we put the dotplots on top of each other, using the same scales. Dotplots – Example continued

35. Types of Distributions 4 common types

36. Symmetrical refers to data in which both sides are (more or less) the same when the graph is folded vertically down the middle bell-shaped is a special type has a center mound with two sloping tails

37. Uniform refers to data in which every class has equal or approximately equal frequency

38. Skewed (left or right) refers to data in which one side (tail) is longer than the other side the direction of skewness is on the side of the longer tail

39. Bimodal (multi-modal) refers to data in which two (or more) classes have the largest frequency & are separated by at least one other class

40. How to describe a graph Dotplots Stem & leaf plots Histograms Boxplots

41. 1. Center discuss where the middle of the data falls three types of central tendency mean, median, & mode

42. 2. Spread discuss how spread out the data is refers to the variability of the data Range, standard deviation, IQR

43. 3. Type of distribution refers to the overall shape of the distribution symmetrical, uniform, skewed, or bimodal

44. 4. Unusual occurrences outliers - value that lies away from the rest of the data gaps clusters anything else unusual

45. 5. In context You must write your answer in reference to the specifics in the problem, using correct statistical vocabulary and using complete sentences!