Data consist of individuals and variables that give us information about those individuals. Ranges of values, called classes, are listed at the bottom, and the classes with greater frequencies have taller bars. In this case height is a quantitate variable while biological sex is a categorical variable. The side-by-side boxplots allow us to easily compare the median, IQR, and range of the two groups. Graphs: Bar Charts and Pie Charts. A histogram often looks similar to a bar graph, but they are different because of the level of measurement of the data. They can show relationships that are not obvious from studying a list of numbers. Distinguish between quantitative and categorical variables in context. The bars are arranged in order of frequency, so more important categories are emphasized. A scatterplot usually looks like a line or curve moving up or down from left to right along the graph with points "scattered" along the line. By looking at all of the pie pieces, you can compare how much of the data fits in each category, or slice. In both cases, we will use the frequency distribution to make the graphs… The type of data often determines what graph is appropriate to use. This is wher… This kind of graph is helpful when graphing qualitative data, where the information describes a trait or attribute and is not numerical. Reading bar graphs: Harry Potter. There is little difference between the two graphs except that the histogram uses rectangles, but the polygon uses dots. To create dotplots with groups in Minitab Express: This should result in the following dotplots with groups: To create histograms with groups in Minitab Express: This should result in the following histograms with groups: Students in this course should pause here and return to complete the assignment in Canvas. The scatterplot helps you uncover more information about any data set, including: Peter James Eaton / Wikimedia Commons / CC BY 4.0. To create a side-by-side boxplots in Minitab Express: This should result in the following side-by-side boxplots: Select your operating system below to see a step-by-step guide for this example. For example, we may want to compare the heights of males and females. Seven types of graphs are commonly used in statistics. Histograms, by contrast, are used for data that involve ordinal variables, or things that are not easily quantified, like feelings or opinions. A scatterplot displays data that is paired by using a horizontal axis (the x-axis), and a vertical axis (the y-axis). 3.3 - One Quantitative and One Categorical Variable, 1.1.1 - Categorical & Quantitative Variables, 126.96.36.199 - Minitab Express: Simple Random Sampling, 188.8.131.52.1 - Minitab Express: Frequency Tables, 184.108.40.206 - Minitab Express: Clustered Bar Chart, 220.127.116.11.1 - Disjoint & Independent Events, 18.104.22.168.5.1 - Advanced Conditional Probability Applications, 2.2.6 - Minitab Express: Central Tendency & Variability, 22.214.171.124 - Minitab Express: Simple Scatterplot, 126.96.36.199 - Formulas for Computing Pearson's r, 188.8.131.52 - Example of Computing r by Hand (Optional), 184.108.40.206 - Minitab Express to Compute Pearson's r, 3.5 - Relations between Multiple Variables, 4.2 - Introduction to Confidence Intervals, 4.2.1 - Interpreting Confidence Intervals, 4.3.1 - Example: Bootstrap Distribution for Proportion of Peanuts, 4.3.2 - Example: Bootstrap Distribution for Difference in Mean Exercise, 220.127.116.11 - Example: Proportion of Lactose Intolerant German Adults, 18.104.22.168 - Example: Difference in Mean Commute Times, 22.214.171.124 - Example: Correlation Between Quiz & Exam Scores, 126.96.36.199 - Example: Difference in Dieting by Biological Sex, 4.7 - Impact of Sample Size on Confidence Intervals, 5.3.1 - StatKey Randomization Methods (Optional), 5.5 - Randomization Test Examples in StatKey, 5.5.1 - Single Proportion Example: PA Residency, 5.5.3 - Difference in Means Example: Exercise by Biological Sex, 5.5.4 - Correlation Example: Quiz & Exam Scores, 5.6 - Randomization Tests in Minitab Express, 6.6 - Confidence Intervals & Hypothesis Testing, 7.2 - Minitab Express: Finding Proportions, 188.8.131.52 - Video Example: Proportion Between z -2 and +2, 7.3 - Minitab Express: Finding Values Given Proportions, 7.3.1 - Video Example: Middle 80% of the z Distribution, 184.108.40.206 - Video Example: Mean Body Temperature, 220.127.116.11 - Video Example: Correlation Between Printer Price and PPM, 18.104.22.168 - Example: Proportion NFL Coin Toss Wins, 22.214.171.124 - Example: Proportion of Women Students, 126.96.36.199 - Example: Difference in Mean Commute Times, 188.8.131.52 - Video Example: 98% CI for Mean Atlanta Commute Time, 184.108.40.206 - Video Example: 90% CI for the Correlation between Height and Weight, 220.127.116.11 - Example: 99% CI for Proportion of Women Students, 18.104.22.168 - Minitab Express: Confidence Interval for a Proportion, 22.214.171.124.1 - Video Example: Lactose Intolerance (Summarized Data, Normal Approximation), 126.96.36.199.2 - Video Example: Dieting (Summarized Data, Normal Approximation), 188.8.131.52 - Computing Necessary Sample Size, 184.108.40.206 - Normal Approximation Method Formulas, 220.127.116.11 - Minitab Express: Hypothesis Tests for One Proportion, 18.104.22.168.1 - Minitab Express: 1 Proportion z Test, Raw Data, 22.214.171.124.2 - Minitab Express: 1 Sample Proportion z test, Summary Data, 126.96.36.199.2.1 - Video Example: Gym Members (Normal Approx.