Introduction to Probability and Statistics Thirteenth Edition Chapter

Introduction to Probability and Statistics Thirteenth Edition Chapter 1 Describing Data with Graphs

Variables and Data A variable is a characteristic that changes or varies over time and/or for different individuals or objects under consideration. Examples: Hair color, white blood cell count, time to failure of a computer component.

Definitions An experimental unit is the individual or object on which a variable is measured. A measurement results when a variable is actually measured on an experimental unit. A set of measurements, called data, can be either a sample or a population.

Example Variable –Hair color Experimental unit –Person Typical Measurements –Brown, black, blonde, etc.

Example Variable –Time until a light bulb burns out Experimental unit –Light bulb Typical Measurements –1500 hours, 1535.5 hours, etc.

How many variables have you measured? Univariate data: One variable is measured on a single experimental unit. Bivariate data: Two variables are measured on a single experimental unit. Multivariate data: More than two variables are measured on a single experimental unit.

Types of Variables Qualitative Quantitative Discrete Continuous

Types of Variables Qualitative variables measure a quality or characteristic on each experimental unit. Examples: Hair color (black, brown, blonde ) Make of car (Dodge, Honda, Ford ) Gender (male, female) State of birth (California, Arizona, .)

Types of Variables Quantitative variables measure a numerical quantity on each experimental unit. Discrete if it can assume only a finite or countable number of values. Continuous if it can assume the infinitely many values corresponding to the points on a line interval.

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

Graphing Qualitative Variables Use a data distribution to describe: – What values of the variable have been measured – How often each value has occurred “How often” can be measured 3 ways: – Frequency – Relative frequency Frequency/n – Percent 100 x Relative frequency

Example A bag of M&Ms contains 25 candies: m m m m m m m m m m m Raw Data: m m m Statistical Table: m m m m m m m m m m m Color Tally Frequency Relative Percent Frequency Red mmm 3 3/25 .12 12% Blue mmmmmm 6 6/25 .24 24% Green mm mm 4 4/25 .16 16% mmmmm 5 5/25 .20 20% Orange Brown mm m 3 3/25 .12 12% Yellow mmmm 4 4/25 .16 16%

6 Graphs Frequency 5 4 3 Bar Chart 2 1 0 Brown Yellow Red Blue Orange Green Color Brown 12.0% Green 16.0% Pie Chart Yellow 16.0% Orange 20.0% Red 12.0% Blue 24.0%

Graphing Quantitative Variables A single quantitative variable measured for different population segments or for different categories of classification can be graphed using a pie or bar chart. chart Cost of a Big Mac ( ) AABig BigMac Mac hamburger hamburgercosts costs 4.90 4.90in inSwitzerland, Switzerland, 2.90 2.90in inthe theU.S. U.S.and and 1.86 1.86in inSouth South Africa. Africa. 5 4 3 2 1 0 Switzerland U.S. Country South Africa

A single quantitative variable measured over time is called a time series. series It can be graphed using a line or bar chart. chart CPI: All Urban Consumers-Seasonally Adjusted Sept Oct Nov Dec Jan Feb Mar 178.10 177.60 177.50 177.30 177.60 178.00 178.60

Dotplots The simplest graph for quantitative data Plots the measurements as points on a horizontal axis, stacking the points that duplicate existing points. Example: The set 4, 5, 5, 7, 6 4 5 6 7

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

Example The prices ( ) of 18 brands of walking shoes: 90 70 70 70 75 70 65 74 70 95 75 70 68 65 4 0 5 Reorder 4 68 40 60 65 0 5 6 580855 6 055588 7 0 00050405 7 5 00000045 8 8 9 05 9 05

Interpreting Graphs: Location and Spread Where Where is is the the data data centered centered on on the the horizontal horizontal axis, axis, and and how how does does itit spread spread out out from from the the center? center?

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

Interpreting Graphs: Outliers No Outliers Outlier Are there any strange or unusual measurements that stand out in the data set?

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. 1.991 1.891 1.991 1.988 1.993 1.989 1.990 1.988 1.988 1.993 1.991 1.989 1.989 1.993 1.990 1.994

Relative Frequency Histograms A 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

Relative Frequency Histograms Divide the range of the data into 5-12 subintervals of equal length. Calculate the approximate width of the subinterval as Range/number of subintervals. Round the approximate width up to a convenient value. Use the method of left inclusion, inclusion including the left endpoint, but not the right in your tally. Create a statistical table including the subintervals, their frequencies and relative frequencies.

Relative Frequency Histograms Draw the relative frequency histogram, histogram plotting the subintervals on the horizontal axis and the relative frequencies on the vertical axis. The height of the bar represents – The proportion of measurements falling in that class or subinterval. – The probability that a single measurement, drawn at random from the set, will belong to that class or subinterval.

Example The ages of 50 tenured faculty at a state university. 34 42 34 43 48 31 59 50 70 36 34 30 63 48 66 43 52 43 40 32 52 26 59 44 35 58 36 58 50 37 43 53 43 52 44 62 49 34 48 53 39 45 41 35 36 62 34 38 28 53 We choose to use 6 intervals. Minimum class width (70 – 26)/6 7.33 Convenient class width 8 Use 6 classes of length 8, starting at 25.

Age Tally Frequency Relative Frequency Percent 25 to 33 1111 5 5/50 .10 10% 33 to 41 1111 1111 1111 14 14/50 .28 28% 41 to 49 1111 1111 111 13 13/50 .26 26% 49 to 57 1111 1111 9 9/50 .18 18% 57 to 65 1111 11 7 7/50 .14 14% 65 to 73 11 2 2/50 .04 4% 14/50 Relative frequency 12/50 10/50 8/50 6/50 4/50 2/50 0 25 33 41 49 Ages 57 65 73

12/50 Relative frequency Describing the Distribution 14/50 10/50 8/50 6/50 4/50 2/50 0 Shape? Skewed right 25 33 41 49 57 65 73 Ages Outliers? No. What proportion of the tenured faculty are younger than 41? (14 5)/50 19/50 .38 What is the probability that a randomly selected faculty member is 49 or older? (8 7 2)/50 17/50 .34

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

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