1: Biostats Chapter 1: Intro & Graphs
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Questions and Answers

Which of the following best describes what individuals are in a dataset?

  • The objects being described in a set of data. (correct)
  • The average measurement taken from the dataset.
  • The properties that characterize the dataset.
  • The count or proportion of each type of variable.

    • Which of the following is a method for charting categorical data?

  • Dot plot
  • Time plot
  • Bar graph (correct)
  • Histogram

    • Which statement accurately defines a quantitative variable?

  • A variable that includes categories or groups.
  • A variable that categorizes individuals into discrete groups.
  • A variable that can take on different numerical values. (correct)
  • A variable that describes a property using counts.

    • When classifying variables, what question should be asked to determine if it is quantitative or categorical?

    <p>What characteristic describes each individual?</p> Signup and view all the answers

    What does the vertical axis of a histogram represent?

    <p>The frequency or relative frequency of the data</p> Signup and view all the answers

    When creating histogram classes, which of the following should be avoided?

    <p>Using classes with 0 or 1 counts</p> Signup and view all the answers

    What is the recommended starting number of classes for a histogram?

    <p>5 to10 classes</p> Signup and view all the answers

    What is a common mistake made when developing histogram classes?

    <p>Creating overly summarized classes that lose information</p> Signup and view all the answers

    Which of the following best describes the ideal histogram class characteristics?

    <p>Balanced with sufficient information without excessive detail</p> Signup and view all the answers

    What should be observed when interpreting histograms?

    <p>The overall pattern and deviations from it</p> Signup and view all the answers

    What is the role of classes in a histogram?

    <p>To divide the range of values into equal-size intervals</p> Signup and view all the answers

    Study Notes

    Course Information

    • The course is BMS 511 Biostats & Statistical Analysis, Chapter 1: Intro & Displaying Data with Graphs
    • The instructor is Guang Xu, PhD, MPH
    • The instructor is an assistant professor of Biostatistics and Public Health at the College of Osteopathic Medicine, Marian University
    • The course provides a comprehensive introduction to statistical practices relevant to biomedical and clinical research
    • The course covers experimental questions and approaches to data collection, statistical analysis, basic statistical concepts, and appropriate statistical methodology
    • The course emphasizes the relationship between statistics and medical research
    • Examples from healthcare and research are used to illustrate statistical concepts and methodology.

    Required Texts

    • The required textbook is "The practice of statistics in the life sciences" by Baldi, Brigitte, and David S. Moore, 4th edition, Macmillan Higher Education
    • Required supplementary textbook is "Epidemiology: With Student Consult Online Access," 5e, by Gordis, L., WB Saunders Co., Philadelphia (5th edition, 2013), ISBN: 978-1455737338
    • "Intuitive Biostatistics: a nonmathematical guide to statistical thinking" by Motulsky, Harvey, 4th edition, Oxford University Press
    • "Statistics in Medicine" by Robert H. Riffenburgh, 3rd edition, Elsevier

    Course Grades

    • Exam I: 16%
    • Exam II: 16%
    • Exam III: 16%
    • Exam IV: 16%
    • In-class Quizzes: 16%
    • Homework (10 total): 10%

    Group Project

    • The group project gives students a chance to apply the learned statistics and to think critically
    • The project necessitates an understanding of the story, methods, results, conclusions, future, and personal thoughts.

    Office Hours

    Learning Objectives

    • Determine and apply methods for depicting data distributions using graphs.
    • Define and utilize individuals and variables.
    • Understand the various types of data: categorical and quantitative
    • Understand different ways to display categorical data (e.g., bar graphs, pie charts)
    • Understand different ways to display quantitative data (e.g., histograms, dot plots)
    • Interpret histograms.
    • Graph time series using time plots.

    Variable Types

    • Quantitative Variables: Variables that measure or assess a quantity, allowing for calculating an average for all individuals. Examples include age, blood pressure, leaf length.
    • Categorical Variables: Variables that describe a characteristic or quality of an individual, allowing for counting or calculating the proportion of individuals with that characteristic. Examples include gender, blood type, flower color

    Classifying Variables

    • Identify the individuals being studied.
    • Define what is being recorded about those individuals.
    • Determine whether the recorded information is a number (quantitative) or a description (categorical).

    Graphing Categorical Data

    • Bar graphs: Each category is represented by a bar. The bar's height represents the frequency or relative frequency of individuals in that category.
    • Pie charts: The whole pie represents all individuals. Each slice represents a category, and the size of the slice corresponds to the proportion of individuals in that category.

    Graphing Quantitative Data

    • Histograms: A summary graph for a single numerical variable, useful for understanding variability, especially with large datasets.
    • Dot plots: A graph of raw data, useful to clarify variation patterns, primarily with small datasets.
    • Time plots: A graph employing time on the horizontal axis and a variable on the vertical axis, highlighting changes over time.

    Making Histograms

    • Divide the range of the quantitative variable into equal-size intervals.
    • The vertical axis represents either frequency (counts) or relative frequency (percentages of total).
    • For each interval, create a column whose height corresponds to the count or percentage of data points in that interval.

    Choosing Histogram Classes

    • The process of selecting classes is iterative (repeated)
    • Avoid too many classes with values of 0 or 1 (pancake graph).
    • Avoid overly summarized classes (skyscraper graph); data is no longer informative.
    • Begin by selecting 5 to 10 intervals, then adjust accordingly.

    Interpreting Histograms

    • Shape: Examine the overall pattern and deviations. Look for patterns like unimodal (single peak), bimodal (two peaks), symmetric (similar shape on both sides), skewed (one tail longer).
    • Center: Estimate the approximate midpoint of the data.
    • Spread: Determine the range of values observed.
    • Outliers: Identify values that deviate significantly from the overall pattern and try to explain them.

    Common Distribution Shapes

    • Symmetric: The graph of the left half of the data and right half looks identical.
    • Left-Skewed: The left side of the graph, containing extreme values, extends further than the right side.
    • Right-Skewed: The right side of the graph, containing extreme values, extends further than the left side.

    Outliers

    • An outlier is a data point that falls outside the overall distribution pattern.
    • Look for outliers and try to explain them.
    • Note that the largest observation isn't always an outlier. It must not be consistent with the rest of the data.

    Making Dot Plots

    • Create a single axis representing the variable's range.
    • Place a dot for each data point, positioned according to its value on the axis.
    • Stack dots when multiple data points have the same value.

    Graphing Time Series

    • Time plots usually involve plotting a variable against time.
    • Look for overall trends and cyclical variability in the data.

    Homework

    • Homework is available under the "Modules" section on Canvas.

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    Related Documents

    BMS 511 Biostats & Statistical Analysis Lecture Notes PDF

    Description

    This quiz covers Chapter 1 of BMS 511, focusing on the introduction to biostatistics and displaying data using graphs. It emphasizes fundamental statistical concepts and methodologies applicable to biomedical and clinical research.

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