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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Introduction to Probability and Statistics Twelfth Edition Robert J. Beaver Barbara M. Beaver William Mendenhall Presentation designed and written by: Barbara M. Beaver

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Introduction to Probability and Statistics Twelfth Edition Chapter 1 Describing Data with Graphs Some graphic screen captures from Seeing Statistics ® Some images © 2001-(current year) www.arttoday.com

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. What is Statistics? What does a statistician do? Player Games Minutes Points Rebounds FG% Bob 34 32.724 7.6.552 Andy 36 31.521 8.4.465 Larry 30 33.018 5.6.493 Michael 31 35.129 6.1.422 Player Games Minutes Points Rebounds FG% Bob 34 32.724 7.6.552 Andy 36 31.521 8.4.465 Larry 30 33.018 5.6.493 Michael 31 35.129 6.1.422

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Job of a Statistician Collects numbers or data Systematically organizes or arranges the data Analyzes the data…extracts relevant information to provide a complete numerical description Infers general conclusions about the problem using this numerical description

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Uses of Statistics Statistics is a theoretical discipline in its own right Statistics is a tool for researchers in other fields Used to draw general conclusions in a large variety of applications

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Common Problem Decision or prediction about a large body of measurements which cannot be totally enumerated. POPULATION: The set of all measurements of interest to the experimenter.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Examples Light bulbs (to enumerate population is destructive) Forecasting the winner of an election (population too big; people change their minds)

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Solution Collect a smaller set of measurements that will (hopefully) be representative of the larger set. SAMPLE: A subset of measurements selected from the population of interest.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. “Sample” and “Population” Distinguish between set of objects on which we take measurements and the measurements themselves. Experimental Units: The items or objects on which measurements are taken Sample (or Population): the set of measurements taken on the experimental units.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Examples Light bulbs –Experimental unit = bulb Opinion polls –Experimental unit = person

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Descriptive Statistics Sometimes (but rarely) we can enumerate the whole population If so, we need only use DESCRIPTIVE STATISTICS: Procedures used to summarize and describe the set of measurements.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Inferential Statistics When we cannot enumerate the whole population, we use INFERENTIAL STATISTICS: Procedures used to draw conclusions or inferences about the population from information contained in the sample.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. The Objective of Inferential Statistics To make inferences about a population from information contained in a sample. The statistician’s job is to find the best way to do this.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. The Steps in Inferential Statistics Define the objective of the experiment and the population of interest Determine the design of the experiment and the sampling plan to be used Collect and analyze the data Make inferences about the population from information in the sample Determine the goodness or reliability of the inference.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Variables and Data variableA variable is a characteristic that changes or varies over time and/or for different individuals or objects under consideration. Examples:Examples: Hair color, white blood cell count, time to failure of a computer component.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Definitions experimental unitAn experimental unit is the individual or object on which a variable is measured. measurementA measurement results when a variable is actually measured on an experimental unit. data, samplepopulation.A set of measurements, called data, can be either a sample or a population.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc.Example Variable –Hair color Experimental unit –Person Typical Measurements –Brown, black, blonde, etc.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc.Example Variable –Time until a light bulb burns out Experimental unit –Light bulb Typical Measurements –1500 hours, 1535.5 hours, etc.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. How many variables have you measured? Univariate data:Univariate data: One variable is measured on a single experimental unit. Bivariate data:Bivariate data: Two variables are measured on a single experimental unit. Multivariate data:Multivariate data: More than two variables are measured on a single experimental unit.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Types of Variables Qualitative Quantitative Discrete Continuous

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Types of Variables Qualitative variablesQualitative variables measure a quality or characteristic on each experimental unit. Examples:Examples: Hair color (black, brown, blonde…) Make of car (Dodge, Honda, Ford…) Gender (male, female) State of birth (California, Arizona,….)

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Types of Variables Quantitative variablesQuantitative variables measure a numerical quantity on each experimental unit. Discrete Discrete if it can assume only a finite or countable number of values. Continuous Continuous if it can assume the infinitely many values corresponding to the points on a line interval.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Examples For each orange tree in a grove, the number of oranges is measured. –Quantitative discrete For a particular day, the number of cars entering a college campus is measured. –Quantitative discrete Time until a light bulb burns out –Quantitative continuous

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Graphing Qualitative Variables data distributionUse a data distribution to describe: –What values –What values of the variable have been measured –How often –How often each value has occurred “How often” can be measured 3 ways: –Frequency –Relative frequency = Frequency/n –Percent = 100 x Relative frequency

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Example A bag of M&Ms contains 25 candies: Raw Data:Raw Data: Statistical Table:Statistical Table: ColorTallyFrequencyRelative Frequency Percent Red33/25 =.1212% Blue66/25 =.2424% Green44/25 =.1616% Orange55/25 =.2020% Brown33/25 =.1212% Yellow44/25 =.1616% m m mm m m m m m m m m m m m m m m m m m m m mmm mm m mm mm mmm mmm mm m m m m m m m m m

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Graphs Bar Chart Pie Chart

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Graphing Quantitative Variables pie bar chartA single quantitative variable measured for different population segments or for different categories of classification can be graphed using a pie or bar chart. A Big Mac hamburger costs $4.90 in Switzerland, $2.90 in the U.S. and $1.86 in South Africa.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. time series linebar chartA single quantitative variable measured over time is called a time series. It can be graphed using a line or bar chart. SeptemberOctoberNovemberDecemberJanuaryFebruaryMarch 178.10177.60177.50177.30177.60178.00178.60 CPI: All Urban Consumers-Seasonally Adjusted BUREAU OF LABOR STATISTICS

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc.Dotplots The simplest graph for quantitative data Plots the measurements as points on a horizontal axis, stacking the points that duplicate existing points. Example:Example: The set 4, 5, 5, 7, 6 45674567 APPLET MY

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Stem and Leaf Plots A simple graph for quantitative data Uses the actual numerical values of each data point. –Divide each measurement into two parts: the stem and the leaf. –List the stems in a column, with a vertical line to their right. –For each measurement, record the leaf portion in the same row as its matching stem. –Order the leaves from lowest to highest in each stem. –Provide a key to your coding. –Divide each measurement into two parts: the stem and the leaf. –List the stems in a column, with a vertical line to their right. –For each measurement, record the leaf portion in the same row as its matching stem. –Order the leaves from lowest to highest in each stem. –Provide a key to your coding.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Example The prices ($) of 18 brands of walking shoes: 907070707570656860 747095757068654065 40 5 65 8 0 8 5 5 70 0 0 5 0 4 0 5 0 8 90 5 40 5 60 5 5 5 8 8 70 0 0 0 0 0 4 5 5 8 90 5 Reorder

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Interpreting Graphs: Location and Spread Where is the data centered on the horizontal axis, and how does it spread out from the center?

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Interpreting Graphs: Shapes Mound shaped and symmetric (mirror images) Skewed right: a few unusually large measurements Skewed left: a few unusually small measurements Bimodal: two local peaks

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Interpreting Graphs: Outliers Are there any strange or unusual measurements that stand out in the data set? Outlier No Outliers

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Example A quality control process measures the diameter of a gear being made by a machine (cm). The technician records 15 diameters, but inadvertently makes a typing mistake on the second entry. 1.9911.8911.9911.9881.993 1.9891.9901.988 1.9881.9931.9911.9891.9891.9931.9901.994

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Relative Frequency Histograms relative frequency histogramA relative frequency histogram for a quantitative data set is a bar graph in which the height of the bar shows “how often” (measured as a proportion or relative frequency) measurements fall in a particular class or subinterval. Create intervals Stack and draw bars

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Relative Frequency Histograms 5-12 subintervalsDivide the range of the data into 5-12 subintervals of equal length. approximate widthCalculate the approximate width of the subinterval as Range/number of subintervals. Round the approximate width up to a convenient value. left inclusionUse the method of left inclusion, including the left endpoint, but not the right in your tally. statistical tableCreate a statistical table including the subintervals, their frequencies and relative frequencies.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Relative Frequency Histograms relative frequency histogramDraw the relative frequency histogram, plotting the subintervals on the horizontal axis and the relative frequencies on the vertical axis. The height of the bar represents proportion –The proportion of measurements falling in that class or subinterval. probability –The probability that a single measurement, drawn at random from the set, will belong to that class or subinterval.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc.Example The ages of 50 tenured faculty at a state university. 34 48 70 63 52 52 35 50 37 43 53 43 52 44 42 31 36 48 43 26 58 62 49 34 48 53 3945 34 59 34 66 40 59 36 41 35 36 62 34 38 28 43 50 30 43 32 44 58 53 6We choose to use 6 intervals. =(70 – 26)/6 = 7.33Minimum class width = (70 – 26)/6 = 7.33 = 8Convenient class width = 8 6825.Use 6 classes of length 8, starting at 25.

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. AgeTallyFrequencyRelative Frequency Percent 25 to < 33111155/50 =.1010% 33 to < 411111 1111 11111414/50 =.2828% 41 to < 491111 1111 1111313/50 =.2626% 49 to < 571111 99/50 =.1818% 57 to < 651111 1177/50 =.1414% 65 to < 731122/50 =.044%

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Shape? Outliers? What proportion of the tenured faculty are younger than 41? What is the probability that a randomly selected faculty member is 49 or older? Skewed right No. (14 + 5)/50 = 19/50 =.38 (8 + 7 + 2)/50 = 17/50 =.34 Describing the Distribution

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Key Concepts I. How Data Are Generated 1. Experimental units, variables, measurements 2. Samples and populations 3. Univariate, bivariate, and multivariate data II. Types of Variables 1. Qualitative or categorical 2. Quantitative a. Discrete b. Continuous III. Graphs for Univariate Data Distributions 1. Qualitative or categorical data a. Pie charts b. Bar charts

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Copyright ©2006 Brooks/Cole A division of Thomson Learning, Inc. Key Concepts 2. Quantitative data a. Pie and bar charts b. Line charts c. Dotplots d. Stem and leaf plots e. Relative frequency histograms 3. Describing data distributions a. Shapes—symmetric, skewed left, skewed right, unimodal, bimodal b. Proportion of measurements in certain intervals c. Outliers

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