#set document(title: "2.3 Other Graphical Representations of Data", author: "Kathryn Kozak") #set page(width: 8.5in, height: auto, margin: 1in) #import "@preview/cetz:0.5.2" #set text(font: ("STIX Two Text", "Libertinus Serif", "New Computer Modern"), size: 10.5pt, lang: "en") #show math.equation: set text(font: ("STIX Two Math", "New Computer Modern Math")) #set par(justify: true, leading: 0.62em, spacing: 0.9em) #set enum(spacing: 1.1em) // room between list items so tall inline fractions don't collide #set list(spacing: 1.1em) #set table(stroke: 0.5pt + rgb("#c7ccd3")) #let BLUE = rgb("#183B6F") // brand navy — section bars + example/solution labels (white on navy 11.09:1) #let ORANGE = rgb("#A94509") // brand primary-700 — AA-safe deep orange for TEXT (5.93:1 on white; raw brand #F37021 is 2.94:1 and must never carry text) #let RED = rgb("#DC2626") // brand error-600 #let GREEN = rgb("#059669") // brand success-600 (decoration only; small green text uses green-text #007942) #show heading.where(level: 1): it => block(width: 100%, above: 0pt, below: 16pt, fill: gradient.linear(BLUE, rgb("#2C5AA0")), inset: (x: 14pt, y: 12pt), radius: 3pt, text(fill: white, weight: "bold", size: 19pt, it.body)) #show heading.where(level: 2): it => block(width: 100%, above: 18pt, below: 10pt, fill: BLUE, inset: (x: 10pt, y: 6pt), radius: 2pt, text(fill: white, weight: "bold", size: 12pt, it.body)) #show heading.where(level: 3): it => text(fill: ORANGE, weight: "bold", size: 12.5pt, it.body) #show heading.where(level: 4): it => text(fill: BLUE, weight: "bold", size: 10.5pt, it.body) #let examplebox(label, title, body) = block(width: 100%, breakable: true, fill: rgb("#EFF1F5"), stroke: 0.5pt + rgb("#CFDDF0"), radius: 4pt, inset: 10pt, above: 12pt, below: 12pt)[ #block(below: 6pt)[#box(fill: BLUE, inset: (x: 6pt, y: 2pt), radius: 2pt, text(fill: white, weight: "bold", size: 8.5pt, label)) #h(0.4em) #strong[#title]] #body] // rail = decorative left rule (raw brand token); labelcolor = AA-safe label text shade #let notebox(label, rail, labelcolor, tint, body) = block(width: 100%, breakable: true, fill: tint, stroke: (left: 3pt + rail), inset: (left: 10pt, rest: 8pt), radius: (right: 4pt), above: 11pt, below: 11pt)[ #text(fill: labelcolor, weight: "bold", size: 7.5pt, tracking: 0.5pt)[#upper(label)] #linebreak() #body] #let solutionbox(body) = block(above: 4pt, below: 8pt)[ #text(fill: BLUE, weight: "bold", size: 8.5pt)[Solution] #linebreak() #body] #let figph(msg) = block(width: 100%, height: 60pt, fill: rgb("#f6f7f9"), stroke: (paint: rgb("#c7ccd3"), dash: "dashed"), radius: 4pt, inset: 10pt)[ #align(center + horizon, text(fill: rgb("#889"), style: "italic", size: 9pt, msg))] // Standardize inlined figure sizes: measure the natural CeTZ canvas, then scale to a // consistent envelope (aspect-aware; see build_typst.py FIG_* constants). Unlike the // print preamble, dimensions are FLOORED: in an editor a user can trim a figure to a // degenerate 1-D shape (a bare line), and w/h or tw/w would then divide by zero. #let _STD_W = 3.5 #let _WIDE_W = 5.6 #let _MAX_H = 3.4 #let _ASPECT_WIDE = 2.2 #let _UPSCALE_MAX = 1.15 #let stdfig(body) = context { let m = measure(body) let w = calc.max(m.width / 1in, 0.01) let h = calc.max(m.height / 1in, 0.01) let tw = if w / h > _ASPECT_WIDE { _WIDE_W } else { _STD_W } let s = calc.min(tw / w, _MAX_H / h, _UPSCALE_MAX) align(center, box(scale(x: s * 100%, y: s * 100%, reflow: true, body))) } #show figure: set block(breakable: false) #set figure(gap: 8pt) #show figure.caption: set text(size: 8.5pt, fill: rgb("#555")) == 2.3#h(0.6em)Other Graphical Representations of Data There are many other types of graphs. Some of the more common ones are the frequency polygon, the dot plot, the stem plot, scatter plot, and a time-series plot. There are also many different graphs that have emerged lately for qualitative data. Many are found in publications and websites. The following is a description of the stem plot, the scatter plot, and the time-series plot. === Stem Plots Stem plots are a quick and easy way to look at small samples of numerical data. You can look for any patterns or any strange data values. It is easy to compare two samples using stem plots. The first step is to divide each number into 2 parts, the stem (such as the leftmost digit) and the leaf (such as the rightmost digit). There are no set rules, you just have to look at the data and see what makes sense. #examplebox("Example 1")[stem plot for grade distribution][ The following are the percentage grades of 25 students from a statistics course. Draw a stem plot of the data. #figure(table( columns: 10, align: left, inset: 6pt, table.header([62], [87], [81], [69], [87], [62], [45], [95], [76], [76]), [62], [71], [65], [67], [72], [80], [40], [77], [87], [58], [84], [73], [93], [64], [89], [], [], [], [], [], )) #solutionbox[ Divide each number so that the tens digit is the stem and the ones digit is the leaf. 62 becomes 6|2. Make a vertical chart with the stems on the left of a vertical bar. Be sure to fill in any missing stems. In other words, the stems should have equal spacing (for example, count by ones or count by tens). The #emph[Graph 2.3.1] shows the stems for this example. #figure(figph[The empty stem column for a stem-and-leaf plot, listing the stems 4 through 9 beside a vertical rule before any leaves are added.], alt: "The empty stem column for a stem-and-leaf plot, listing the stems 4 through 9 beside a vertical rule before any leaves are added.", caption: [Figure #math.equation(block: false, alt: "1")[$1$]: Stem Plot for Test Grades Step 1]) Now go through the list of data and add the leaves. Put each leaf next to its corresponding stem. Don’t worry about order yet just get all the leaves down. When the data value 62 is placed on the plot it looks like the plot in #emph[Graph 2.3.2]. #figure(figph[A stem-and-leaf plot with stems 4 through 9 and a single leaf 2 on stem 6, representing the value 62.], alt: "A stem-and-leaf plot with stems 4 through 9 and a single leaf 2 on stem 6, representing the value 62.", caption: [Figure #math.equation(block: false, alt: "2")[$2$]: Stem Plot for Test Grades Step 2]) When the data value 87 is placed on the plot it looks like the plot in #emph[Graph 2.3.3]. #figure(figph[A stem-and-leaf plot showing the values 62 and 87, with stems 4 through 9 and leaves 2 at stem 6 and 7 at stem 8.], alt: "A stem-and-leaf plot showing the values 62 and 87, with stems 4 through 9 and leaves 2 at stem 6 and 7 at stem 8.", caption: [Figure #math.equation(block: false, alt: "3")[$3$]: Stem Plot for Test Grades Step 3]) Filling in the rest of the leaves to obtain the plot in #emph[Graph 2.3.4]. #figure(figph[A stem-and-leaf plot of 25 values ranging from 40 to 95, with the leaves still unsorted and most observations in the 60s through 80s.], alt: "A stem-and-leaf plot of 25 values ranging from 40 to 95, with the leaves still unsorted and most observations in the 60s through 80s.", caption: [Figure #math.equation(block: false, alt: "4")[$4$]: Stem Plot for Test Grades Step 4]) Now you have to add labels and make the graph look pretty. You need to add a label and sort the leaves into increasing order. You also need to tell people what the stems and leaves mean by inserting a legend.#strong[Be careful to line the leaves up in columns]. You need to be able to compare the lengths of the rows when you interpret the graph. The final stem plot for the test grade data is in #emph[Graph 2.3.5.] #figure(figph[A stem-and-leaf plot of 25 test scores ranging from 40 to 95, with most scores in the 60s through 80s.], alt: "A stem-and-leaf plot of 25 test scores ranging from 40 to 95, with most scores in the 60s through 80s.", caption: [Figure #math.equation(block: false, alt: "5")[$5$]: Stem Plot for Test Grades]) Now you can interpret the stem-and-leaf display. The data is bimodal and somewhat symmetric. There are no gaps in the data. The center of the distribution is around 70. ] ] You can create a stem and leaf plot on R. the command is: stem(variable) – creates a stem and leaf plot, if you do not get a stem plot that shows all of the stems then use scale = a number. Adjust the number until you see all of the stems. So you would have stem(variable, scale = a number) For Example #math.equation(block: false, alt: "1")[$1$], the command would be grades\<-c(62, 87, 81, 69, 87, 62, 45, 95, 76, 76, 62, 71, 65, 67, 72, 80, 40, 77, 87, 58, 84, 73, 93, 64, 89) #linebreak() stem(grades, scale = 2) grades \<- c(62, 87, 81, 69, 87, 62, 45, 95, 76, 76, 62, 71, 65, 67, 72, 80, 40, 77, 87, 58, 84, 73, 93, 64, 89) stem(grades, scale = 2) Output: The decimal point is 1 digit(s) to the right of the | #figure(figph[A stem-and-leaf plot of 25 values ranging from 40 to 95, with most values in the 60s through 80s.], alt: "A stem-and-leaf plot of 25 values ranging from 40 to 95, with most values in the 60s through 80s.", caption: none) Now just put a title on the stem plot. === Scatter Plot Sometimes you have two different variables and you want to see if they are related in any way. A scatter plot helps you to see what the relationship would look like. A scatter plot is just a plotting of the ordered pairs. #examplebox("Example 2")[scatter plot][ Is there any relationship between elevation and high temperature on a given day? The following data are the high temperatures at various cities on a single day and the elevation of the city. #figure(table( columns: 8, align: left, inset: 6pt, table.header([Elevation #linebreak() (in feet)], [7000], [4000], [6000], [3000], [7000], [4500], [5000]), [Temperature (°F)], [50], [60], [48], [70], [55], [55], [60], )) #solutionbox[ Preliminary: State the random variables Let x = altitude y = high temperature Now plot the x values on the horizontal axis, and the y values on the vertical axis. Then set up a scale that fits the data on each axes. Once that is done, then just plot the x and y values as an ordered pair. In R, the command is: independent variable\<-c(type in data with commas in between values) #linebreak() dependent variable\<-c(type in data with commas in between values) #linebreak() plot(independent variable, dependent variable, main="type in a title you want", xlab="type in a label for the horizontal axis", ylab="type in a label for the vertical axis", ylim=c(0, number above maximum y value) For this example, that would be: #linebreak() elevation\<-c(7000, 4000, 6000, 3000, 7000, 4500, 5000) #linebreak() temperature\<-c(50, 60, 48, 70, 55, 55, 60) #linebreak() plot(elevation, temperature, main="Temperature versus Elevation", xlab="Elevation (in feet)", ylab="Temperature (in degrees F)", ylim=c(0, 80)) elevation \<- c(7000, 4000, 6000, 3000, 7000, 4500, 5000) temperature \<- c(50, 60, 48, 70, 55, 55, 60) plot(elevation, temperature, main="Temperature versus Elevation", xlab="Elevation (in feet)", ylab="Temperature (in degrees F)", ylim=c(0, 80)) #figure(figph[Scatter plot of temperature in degrees F versus elevation in feet, showing temperatures from 48 to 70 across elevations from 3000 to 7000 feet.], alt: "Scatter plot of temperature in degrees F versus elevation in feet, showing temperatures from 48 to 70 across elevations from 3000 to 7000 feet.", caption: [Figure #math.equation(block: false, alt: "6")[$6$]: Scatter Plot of Temperature versus Elevation]) Looking at the graph, it appears that there is a linear relationship between temperature and elevation. It also appears to be a negative relationship, thus as elevation increases, the temperature decreases. ] ] === Time-Series A time-series plot is a graph showing the data measurements in chronological order, the data being quantitative data. For example, a time-series plot is used to show profits over the last 5 years. To create a time-series plot, the time always goes on the horizontal axis, and the other variable goes on the vertical axis. Then plot the ordered pairs and connect the dots. The purpose of a time-series graph is to look for trends over time. Caution, you must realize that the trend may not continue. Just because you see an increase, doesn’t mean the increase will continue forever. As an example, prior to 2007, many people noticed that housing prices were increasing. The belief at the time was that housing prices would continue to increase. However, the housing bubble burst in 2007, and many houses lost value, and haven’t recovered. #examplebox("Example 3")[Time-series plot][ The following table tracks the weight of a dieter, where the time in months is measuring how long since the person started the diet #figure(table( columns: 7, align: left, inset: 6pt, table.header([Time (months)], [0], [1], [2], [3], [4], [5]), [Weight (pounds)], [200], [195], [192], [193], [190], [187], )) Make a time-series plot of this data #solutionbox[ In R, the command would be: variable1\<-c(type in data with commas in between values, this should be the time variable) #linebreak() variable2\<-c(type in data with commas in between values) #linebreak() plot(variable1, variable2, ylim=c(0,number over max), main="type in a title you want", xlab="type in a label for the horizontal axis", ylab="type in a label for the vertical axis") #linebreak() lines(variable1, variable2) – connects the dots For this example: #linebreak() time\<-c(0, 1, 2, 3, 4, 5) #linebreak() weight\<-c(200, 195, 192, 193, 190, 187) #linebreak() plot(time, weight, ylim=c(0,250), main="Weight over Time", xlab="Time (Months) ", ylab="Weight (pounds)") #linebreak() ines(time, weight) time \<- c(0, 1, 2, 3, 4, 5) weight \<- c(200, 195, 192, 193, 190, 187) plot(time, weight, ylim=c(0, 250), main="Weight over Time", xlab="Time (Months) ", ylab="Weight (pounds)") #figure(figph[A line chart of weight in pounds over six months shows an overall decrease from 200 pounds at month 0 to 187 pounds at month 5, with a slight increase at month 3.], alt: "A line chart of weight in pounds over six months shows an overall decrease from 200 pounds at month 0 to 187 pounds at month 5, with a slight increase at month 3.", caption: [Figure of Weight versus Time]) Notice, that over the 5 months, the weight appears to be decreasing. Though it doesn’t look like there is a large decrease. ] ] Be careful when making a graph. If you don’t start the vertical axis at 0, then the change can look much more dramatic than it really is. As an example, #emph[Graph 2.3.8] shows the #emph[Graph 2.3.7] with a different scaling on the vertical axis. Notice the decrease in weight looks much larger than it really is. #figure(figph[Line chart titled Weight over Time showing weight decreasing overall from 200 pounds at month 0 to 187 pounds at month 5, with a slight increase from month 2 to month 3.], alt: "Line chart titled Weight over Time showing weight decreasing overall from 200 pounds at month 0 to 187 pounds at month 5, with a slight increase from month 2 to month 3.", caption: [Figure]) === Homework Exercise #math.equation(block: false, alt: "1")[$1$] + Students in a statistics class took their first test. The data in Example #math.equation(block: false, alt: "4")[$4$] are the scores they earned. Create a stem plot. #figure(table( columns: 7, align: left, inset: 6pt, table.header([80], [79], [89], [74], [73], [67], [79]), [93], [70], [70], [76], [88], [83], [73], [81], [79], [80], [85], [79], [80], [79], [58], [93], [94], [74], [], [], [], )) + Students in a statistics class took their first test. The data in Example #math.equation(block: false, alt: "5")[$5$] are the scores they earned. Create a stem plot. Compare to the graph in question 1. #figure(table( columns: 6, align: left, inset: 6pt, table.header([67], [67], [76], [47], [85], [70]), [87], [76], [80], [72], [84], [98], [84], [64], [65], [82], [81], [81], [88], [74], [87], [83], [], [], )) + When an anthropologist finds skeletal remains, they need to figure out the height of the person. The height of a person (in cm) and the length of one of their metacarpal bone (in cm) were collected and are in Example #math.equation(block: false, alt: "6")[$6$] ("Prediction of height," 2013). Create a scatter plot and state if there is a relationship between the height of a person and the length of their metacarpal. #figure(table( columns: 2, align: left, inset: 6pt, table.header([Length of Metacarpal], [Height of Person]), [45], [171], [51], [178], [39], [157], [41], [163], [48], [172], [49], [183], [46], [173], [43], [175], [47], [173], )) + Table 7 contains the value of the house and the amount of rental income in a year that the house brings in ("Capital and rental," 2013). Create a scatter plot and state if there is a relationship between the value of the house and the annual rental income. #figure(table( columns: 8, align: left, inset: 6pt, table.header([Value], [Rental], [Value], [Rental], [Value], [Rental], [Value], [Rental]), [81000], [6656], [77000], [4576], [75000], [7280], [67500], [6864], [95000], [7904], [94000], [8736], [90000], [6240], [85000], [7072], [121000], [12064], [115000], [7904], [110000], [7072], [104000], [7904], [135000], [8320], [130000], [9776], [126000], [6240], [125000], [7904], [145000], [8320], [140000], [9568], [140000], [9152], [135000], [7488], [165000], [13312], [165000], [8528], [155000], [7488], [148000], [8320], [178000], [11856], [174000], [10400], [170000], [9568], [170000], [12688], [200000], [12272], [200000], [10608], [194000], [11232], [190000], [8320], [214000], [8528], [280000], [10400], [200000], [10400], [200000], [8320], [240000], [10192], [240000], [12064], [240000], [11648], [225000], [12480], [289000], [11648], [270000], [12896], [262000], [10192], [244500], [11232], [325000], [12480], [310000], [12480], [303000], [12272], [300000], [12480], )) + The World Bank collects information on the life expectancy of a person in each country ("Life expectancy at," 2013) and the fertility rate per woman in the country ("Fertility rate," 2013). The data for 24 randomly selected countries for the year 2011 are in Table 8. Create a scatter plot of the data and state if there appears to be a relationship between life expectancy and the number of births per woman. #figure(table( columns: 4, align: left, inset: 6pt, table.header([Life Expectancy], [Fertility Rate], [Life Expectancy], [Fertility rate]), [77.2], [1.7], [72.3], [3.9], [55.4], [5.8], [76.0], [1.5], [69.9], [2.2], [66.0], [4.2], [76.4], [2.1], [5.9], [5.2], [75.0], [1.8], [54.4], [6.8], [78.2], [2.0], [62.9], [4.7], [73.0], [2.6], [78.3], [2.1], [70.8], [2.8], [72.1], [2.9], [82.6], [1.4], [80.7], [1.4], [68.9], [2.6], [74.2], [2.5], [81.0], [1.5], [73.3], [1.5], [54.2], [6.9], [67.1], [2.4], )) + The World Bank collected data on the percentage of gross domestic product (GDP) that a country spends on health expenditures ("Health expenditure," 2013) and the percentage of woman receiving prenatal care ("Pregnant woman receiving," 2013). The data for the countries where this information is available for the year 2011 is in Table 9. Create a scatter plot of the data and state if there appears to be a relationship between percentage spent on health expenditure and the percentage of woman receiving prenatal care. #figure(table( columns: 2, align: left, inset: 6pt, table.header([Prenatal Care (%)], [Health Expenditure (% of GDP)]), [47.9], [9.6], [54.6], [3.7], [93.7], [5.2], [84.7], [5.2], [100.0], [10.0], [42.5], [4.7], [96.4], [4.8], [77.1], [6.0], [58.3], [5.4], [95.4], [4.8], [78.0], [4.1], [93.3], [6.0], [93.3], [9.5], [93.7], [6.8], [89.8], [6.1], )) + The Australian Institute of Criminology gathered data on the number of deaths (per 100,000 people) due to firearms during the period 1983 to 1997 ("Deaths from firearms," 2013). The data is in Table 10. Create a time-series plot of the data and state any findings you can from the graph. #figure(table( columns: 9, align: left, inset: 6pt, table.header([Year], [1983], [1984], [1985], [1986], [1987], [1988], [1989], [1990]), [Rate], [4.31], [4.42], [4.52], [4.35], [4.39], [4.21], [3.40], [3.61], [Year], [1991], [1992], [1993], [1994], [1995], [1996], [1997], [], [Rate], [3.67], [3.61], [2.98], [2.95], [2.72], [2.95], [2.3], [], )) + The economic crisis of 2008 affected many countries, though some more than others. Some people in Australia have claimed that Australia wasn’t hurt that badly from the crisis. The bank assets (in billions of Australia dollars (AUD)) of the Reserve Bank of Australia (RBA) for the time period of March 2007 through March 2013 are contained in Table 11 ("B1 assets of," 2013). Create a time-series plot and interpret any findings. #figure(table( columns: 2, align: left, inset: 6pt, table.header([Date], [Assets in Billions of AUD]), [Mar-2006], [96.9], [Jun-2006], [107.4], [Sep-2006], [107.2], [Dec-2006], [116.2], [Mar-2007], [123.7], [Jun-2007], [134.0], [Sep-2007], [123.0], [Dec-2007], [93.2], [Mar-2008], [93.7], [Jun-2008], [105.6], [Sep-2008], [101.5], [Dec-2008], [158.8], [Mar-2009], [118.7], [Jun-2009], [111.9], [Sep-2009], [87.0], [Dec-2009], [86.1], [Mar-2010], [83.4], [Jun-2010], [85.7], [Sep-2010], [74.8], [Dec-2010], [76.0], [Mar-2011], [75.7], [Jun-2011], [75.9], [Sep-2011], [75.2], [Dec-2011], [87.9], [Mar-2012], [91.0], [Jun-2012], [90.1], [Sep-2012], [83.9], [Dec-2012], [95.8], [Mar-2013], [90.5], )) + The consumer price index (CPI) is a measure used by the U.S. government to describe the cost of living. Table 12 gives the cost of living for the U.S. from the years 1947 through 2011, with the year 1977 being used as the year that all others are compared (DeNavas-Walt, Proctor & Smith, 2012). Create a time-series plot and interpret. #figure(table( columns: 4, align: left, inset: 6pt, table.header([Year], [CPI-U-RS1 index (December 1977=100)], [Year], [CPI-U-RS1 index (December 1977=100)]), [1947], [37.5], [1980], [127.1], [1948], [40.5], [1981], [139.2], [1949], [40.0], [1982], [147.6], [1950], [40.5], [1983], [153.9], [1951], [43.7], [1984], [160.2], [1952], [44.5], [1985], [165.7], [1953], [44.8], [1986], [168.7], [1954], [45.2], [1987], [174.4], [1955], [45.0], [1988], [180.8], [1956], [45.7], [1989], [188.6], [1957], [47.2], [1990], [198.0], [1958], [48.5], [1991], [205.1], [1959], [48.9], [1992], [210.3], [1960], [49.7], [1993], [215.5], [1961], [50.2], [1994], [220.1], [1962], [50.7], [1995], [225.4], [1963], [51.4], [1996], [231.4], [1964], [52.1], [1997], [236.4], [1965], [52.9], [1998], [239.7], [1966], [54.4], [1999], [244.7], [1967], [56.1], [2000], [252.9], [1968], [58.3], [2001], [260.0], [1969], [60.9], [2002], [264.2], [1970], [63.9], [2003], [270.1], [1971], [66.7], [2004], [277.4], [1972], [68.7], [2005], [286.7], [1973], [73.0], [2006], [296.1], [1974], [80.3], [2007], [304.5], [1975], [86.9], [2008], [316.2], [1976], [91.9], [2009], [315.0], [1977], [97.7], [2010], [320.2], [1978], [104.4], [2011], [330.3], [1979], [114.4], [], [], )) + The median incomes for all households in the U.S. for the years 1967 to 2011 are given in Table 13 (DeNavas-Walt, Proctor & Smith, 2012). Create a time-series plot and interpret. #figure(table( columns: 4, align: left, inset: 6pt, table.header([Year], [Median Income], [Year], [Median Income]), [1967], [42,056], [1990], [49,950], [1968], [43,868], [1991], [48,516], [1969], [45,499], [1992], [48,117], [1970], [45,146], [1993], [47,884], [1971], [44,707], [1994], [48,418], [1972], [46,622], [1995], [49,935], [1973], [47,563], [1996], [50,661], [1974], [46,057], [1997], [51,704], [1975], [44,851], [1998], [53,582], [1976], [45,595], [1999], [54,932], [1977], [45,884], [2000], [54,841], [1978], [47,659], [2001], [53,646], [1979], [47,527], [2002], [53,019], [1980], [46,024], [2003], [52,973], [1981], [45,260], [2004], [52,788], [1982], [45,139], [2005], [53,371], [1983], [44,823], [2006], [53,768], [1984], [46,215], [2007], [54,489], [1985], [47,079], [2008], [52,546], [1986], [48,746], [2009], [52,195], [1987], [49,358], [2010], [50,831], [1988], [49,737], [2011], [50,054], [1989], [50,624], [], [], )) + State everything that makes #emph[Graph 2.3.9] a misleading or poor graph. #linebreak() #linebreak() #strong[#emph[Graph 2.3.9]]#emph[: Example of a Poor Graph] + State everything that makes #emph[Graph 2.3.10] a misleading or poor graph (Benen, 2011). #linebreak() #linebreak() #strong[#emph[Graph 2.3.10]]#emph[: Example of a Poor Graph] + State everything that makes #emph[Graph 2.3.11] a misleading or poor graph ("United States unemployment," 2013). #linebreak() #linebreak() #strong[#emph[Graph 2.3.11]]#emph[: Example of a Poor Graph] + State everything that makes #emph[Graph 2.3.12] a misleading or poor graph. #linebreak() #linebreak() #strong[#emph[Graph 2.3.12]]#emph[: Example of a Poor Graph] Answer See solutions === Data Sources: #emph[B1 assets of financial institutions.] (2013, June 27). Retrieved from www.rba.gov.au/statistics/tables/xls/b01hist.xls Benen, S. (2011, September 02). \[Web log message\]. Retrieved from #link("http://www.washingtonmonthly.com/politicalanimal/2011_09/gop_leaders_stop_taking_credit031960.php")[http://www.washingtonmonthly.com/pol...edit031960.php] #emph[Capital and rental values of Auckland properties]. (2013, September 26). Retrieved from #link("http://www.statsci.org/data/oz/rentcap.html")[http://www.statsci.org/data/oz/rentcap.html] #emph[Contraceptive use]. (2013, October 9). Retrieved from #link("http://www.prb.org/DataFinder/Topic/Rankings.aspx?ind=35")[http://www.prb.org/DataFinder/Topic/...gs.aspx?ind=35] #emph[Deaths from firearms]. (2013, September 26). Retrieved from #link("http://www.statsci.org/data/oz/firearms.html")[http://www.statsci.org/data/oz/firearms.html] DeNavas-Walt, C., Proctor, B., & Smith, J. U.S. Department of Commerce, U.S. Census Bureau. (2012). #emph[Income, poverty, and health insurance coverage in the United States: 2011] (P60-243). Retrieved from website: www.census.gov/prod/2012pubs/p60-243.pdf #emph[Density of people in Africa]. (2013, October 9). Retrieved from #link("http://www.prb.org/DataFinder/Topic/Rankings.aspx?ind=30&loc=249,250,251,25")[http://www.prb.org/DataFinder/Topic/...249,250,251,25] 2,253,254,34227,255,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,27 2,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,294, 295,296,297,298,299,300,301,302,304,305,306,307,308 Department of Health and Human Services, ASPE. (2013). #emph[Health insurance marketplace premiums for 2014.] Retrieved from website: aspe.hhs.gov/health/reports/2...b\_premiumsland scape.pdf #emph[Electricity usage]. (2013, October 9). Retrieved from #link("http://www.prb.org/DataFinder/Topic/Rankings.aspx?ind=162")[http://www.prb.org/DataFinder/Topic/...s.aspx?ind=162] #emph[Fertility rate.] (2013, October 14). Retrieved from #link("http://data.worldbank.org/indicator/SP.DYN.TFRT.IN")[http://data.worldbank.org/indicator/SP.DYN.TFRT.IN] #emph[Fuel oil usage.] (2013, October 9). Retrieved from #link("http://www.prb.org/DataFinder/Topic/Rankings.aspx?ind=164")[http://www.prb.org/DataFinder/Topic/...s.aspx?ind=164] #emph[Gas usage.] (2013, October 9). Retrieved from #link("http://www.prb.org/DataFinder/Topic/Rankings.aspx?ind=165")[http://www.prb.org/DataFinder/Topic/...s.aspx?ind=165] #emph[Health expenditure.] (2013, October 14). Retrieved from #link("http://data.worldbank.org/indicator/SH.XPD.TOTL.ZS")[http://data.worldbank.org/indicator/SH.XPD.TOTL.ZS] Hinatov, M. U.S. Consumer Product Safety Commission, Directorate of Epidemiology. (2012). #emph[Incidents, deaths, and in-depth investigations associated with non-fire carbon monoxide from engine-driven generators and other engine-driven tools, 1999-2011.]Retrieved from website: www.cpsc.gov/PageFiles/129857/cogenerators.pdf #emph[Life expectancy at birth.] (2013, October 14). Retrieved from #link("http://data.worldbank.org/indicator/SP.DYN.LE00.IN")[http://data.worldbank.org/indicator/SP.DYN.LE00.IN] #emph[Median income of males.] (2013, October 9). Retrieved from #link("http://www.prb.org/DataFinder/Topic/Rankings.aspx?ind=137")[http://www.prb.org/DataFinder/Topic/...s.aspx?ind=137] #emph[Median income of males.] (2013, October 9). Retrieved from #link("http://www.prb.org/DataFinder/Topic/Rankings.aspx?ind=136")[http://www.prb.org/DataFinder/Topic/...s.aspx?ind=136] #emph[Prediction of height from metacarpal bone length]. (2013, September 26). Retrieved from #link("http://www.statsci.org/data/general/stature.html")[http://www.statsci.org/data/general/stature.html] #emph[Pregnant woman receiving prenatal care.] (2013, October 14). Retrieved from #link("http://data.worldbank.org/indicator/SH.STA.ANVC.ZS")[http://data.worldbank.org/indicator/SH.STA.ANVC.ZS] #emph[United States unemployment.] (2013, October 14). Retrieved from #link("http://www.tradingeconomics.com/united-states/unemployment-rate")[http://www.tradingeconomics.com/unit...mployment-rate] Weissmann, J. (2013, March 20). A truly devastating graph on state higher education spending. #emph[The Atlantic.] Retrieved from #link("http://www.theatlantic.com/business/archive/2013/03/a-truly-devastating-graph-onstate-higher-education-spending/274199/")[http://www.theatlantic.com/business/...ending/274199/]