Computers As a quality control manager at Texas Instruments, you find that defective calculators have various causes, including worn machinery, human error, bad supplies, and packaging mistreatment. Which of the following graphs would be best for describing the causes of defects: histogram; scatterplot; Pareto chart; dotplot; pie chart?
Table of contents
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample3h 29m
- 10. Hypothesis Testing for Two Samples4h 50m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- 11. Correlation1h 6m
- 12. Regression1h 50m
- 13. Chi-Square Tests & Goodness of Fit1h 57m
- 14. ANOVA1h 57m
2. Describing Data with Tables and Graphs
Visualizing Qualitative vs. Quantitative Data
Problem 2.3.19
Textbook Question
In Exercises 17–20, identify how the graph is deceptive.
Cost of Giving Birth According to the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project, the typical cost of a C-section baby delivery is $4500, and the typical cost of a vaginal delivery is $2600. See the following illustration.

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Examine the illustration provided. The image shows two baby bottles, which might be used to represent the costs of C-section and vaginal deliveries.
Identify the potential deceptive element in the graph. If the bottles are used to visually compare costs, check whether their sizes or proportions accurately reflect the numerical difference between $4500 and $2600.
Consider whether the visual representation exaggerates or minimizes the difference between the two costs. For example, if the bottle representing $4500 is disproportionately larger than the bottle for $2600, the graph may be misleading.
Analyze whether the graph uses consistent scaling. A deceptive graph might use inconsistent scaling to make the difference appear larger or smaller than it actually is.
Conclude that the graph may be deceptive if it relies on visual elements that do not accurately represent the numerical data, such as disproportionate sizes or misleading scaling.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Graphical Misrepresentation
Graphical misrepresentation occurs when data is presented in a way that distorts the true nature of the information. This can happen through misleading scales, selective data presentation, or inappropriate graph types. In the context of healthcare costs, a graph may exaggerate differences between C-section and vaginal delivery costs, leading to incorrect conclusions about their relative expenses.
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Intro to Histograms
Cost Comparison
Cost comparison involves evaluating the expenses associated with different options, in this case, C-section versus vaginal delivery. Understanding the typical costs is essential for making informed decisions about healthcare. However, the way these costs are visually represented can influence perceptions, making it crucial to analyze the data critically.
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Introduction to Matched Pairs
Contextual Interpretation
Contextual interpretation refers to understanding data within its broader context, including factors that may affect the figures presented. For instance, the costs of childbirth can vary based on location, insurance coverage, and additional medical needs. Recognizing these factors is vital to avoid drawing misleading conclusions from a graph that may not account for all relevant variables.
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Step 1: Write Hypotheses Example 1
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Textbook Question
