Name: Bar Graph
Uploaded: 2022-01-25T10:06:55-05:00
Duration: 1 min 7 s
Description: A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots).

Chapter 2: Summarizing and Visualizing Data Back To Chapter

2.12: Bar Graph

TABLE OF
CONTENTS

X

Chapter 1: Understanding Statistics

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1.1: Introduction to Statistics
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1.2: How Data are Classified: Categorical Data
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1.3: How Data are Classified: Numerical Data
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1.4: Nominal Level of Measurement
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1.5: Ordinal Level of Measurement
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1.6: Interval Level of Measurement
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1.7: Ratio Level of Measurement
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1.8: Data Collection by Observations
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1.9: Data Collection by Experiments
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1.10: Data Collection by Survey
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1.11: Random Sampling Method
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1.12: Systematic Sampling Method
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1.13: Convenience Sampling Method
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1.14: Stratified Sampling Method
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1.15: Cluster Sampling Method

Chapter 2: Summarizing and Visualizing Data

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2.1: Review and Preview
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2.2: What is a Frequency Distribution
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2.3: Construction of Frequency Distribution
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2.4: Relative Frequency Distribution
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2.5: Percentage Frequency Distribution
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2.6: Cumulative Frequency Distribution
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2.7: Ogive Graph
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2.8: Histogram
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2.9: Relative Frequency Histogram
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2.10: Scatter Plot
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2.11: Time-Series Graph
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2.12: Bar Graph
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2.13: Multiple Bar Graph
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2.14: Pareto Chart
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2.15: Pie Chart

Chapter 3: Measure of Central Tendency

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3.1: What is Central Tendency?
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3.2: Arithmetic Mean
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3.3: Geometric Mean
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3.4: Harmonic Mean
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3.5: Trimmed Mean
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3.6: Weighted Mean
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3.7: Root Mean Square
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3.8: Mean From a Frequency Distribution
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3.9: What is a Mode?
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3.10: Median
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3.11: Midrange
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3.12: Skewness
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3.13: Types of Skewness

Chapter 4: Measures of Variation

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4.1: What is Variation?
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4.2: Range
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4.3: Standard Deviation
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4.4: Standard Error of the Mean
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4.5: Calculating Standard Deviation
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4.6: Variance
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4.7: Coefficient of Variation
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4.8: Range Rule of Thumb to Interpret Standard Deviation
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4.9: Empirical Method to Interpret Standard Deviation
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4.10: Chebyshev's Theorem to Interpret Standard Deviation
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4.11: Mean Absolute Deviation

Chapter 5: Measures of Relative Standing

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5.1: Review and Preview
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5.2: Introduction to z Scores
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5.3: z Scores and Unusual Values
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5.4: Percentile
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5.5: Quartile
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5.6: 5-Number Summary
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5.7: Boxplot
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5.8: What Are Outliers?
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5.9: Modified Boxplots

Chapter 6: Probability Distributions

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6.1: Probability in Statistics
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6.2: Random Variables
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6.3: Probability Distributions
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6.4: Probability Histograms
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6.5: Unusual Results
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6.6: Expected Value
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6.7: Binomial Probability Distribution
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6.8: Poisson Probability Distribution
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6.9: Uniform Distribution
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6.10: Normal Distribution
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6.11: z Scores and Area Under the Curve
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6.12: Applications of Normal Distribution
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6.13: Sampling Distribution
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6.14: Central Limit Theorem

Chapter 7: Estimates

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7.1: What are Estimates?
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7.2: Sample Proportion and Population Proportion
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7.3: Confidence Intervals
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7.4: Confidence Coefficient
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7.5: Interpretation of Confidence Intervals
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7.6: Critical Values
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7.7: Margin of Error
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7.8: Sample Size Calculation
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7.9: Estimating Population Mean with Known Standard Deviation
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7.10: Estimating Population Mean with Unknown Standard Deviation
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7.11: Confidence Interval for Estimating Population Mean

Chapter 8: Distributions

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8.1: Distributions to Estimate Population Parameter
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8.2: Degrees of Freedom
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8.3: Student t Distribution
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8.4: Choosing Between z and t Distribution
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8.5: Chi-square Distribution
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8.6: Finding Critical Values for Chi-Square
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8.7: Estimating Population Standard Deviation
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8.8: Goodness-of-Fit Test
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8.9: Expected Frequencies in Goodness-of-Fit Tests
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8.10: Contingency Table
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8.11: Introduction to Test of Independence
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8.12: Hypothesis Test for Test of Independence
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8.13: Determination of Expected Frequency
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8.14: Test for Homogeneity
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8.15: F Distribution

Chapter 9: Hypothesis Testing

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9.1: What is a Hypothesis?
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9.2: Null and Alternative Hypotheses
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9.3: Critical Region, Critical Values and Significance Level
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9.4: P-value
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9.5: Types of Hypothesis Testing
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9.6: Decision Making: P-value Method
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9.7: Decision Making: Traditional Method
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9.8: Hypothesis: Accept or Fail to Reject?
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9.9: Errors In Hypothesis Tests
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9.10: Testing a Claim about Population Proportion
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9.11: Testing a Claim about Mean: Known Population SD
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9.12: Testing a Claim about Mean: Unknown Population SD
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9.13: Testing a Claim about Standard Deviation

Chapter 10: Analysis of Variance

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10.1: What is an ANOVA?
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10.2: One-Way ANOVA
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10.3: One-Way ANOVA: Equal Sample Sizes
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10.4: One-Way ANOVA: Unequal Sample Sizes
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10.5: Multiple Comparison Tests
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10.6: Bonferroni Test
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10.7: Two-Way ANOVA

Chapter 11: Correlation and Regression

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11.1: Correlation
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11.2: Coefficient of Correlation
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11.3: Calculating and Interpreting the Linear Correlation Coefficient
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11.4: Regression Analysis
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11.5: Outliers and Influential Points
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11.6: Residuals and Least-Squares Property
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11.7: Residual Plots
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11.8: Variation
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11.9: Prediction Intervals
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11.10: Multiple Regression

Chapter 12: Statistics in Practice

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12.1: What is an Experiment?
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12.2: Study Design in Statistics
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12.3: Observational Studies
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12.4: Experimental Designs
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12.5: Randomized Experiments
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12.6: Crossover Experiments
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12.7: Controls in Experiments
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12.8: Bias
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12.9: Blinding
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12.10: Clinical Trials

Full Table of Contents

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TRANSCRIPT

Recall that qualitative data represent non-numerical variables such as different hair colors, types of vehicles, or various courses offered by a college.

For instance, consider a frequency table with the number of students enrolled for each of these courses—Statistics, Biology, Physics, and Chemistry.

Such qualitative data can be represented visually using a bar graph.

The categories of qualitative data—the different courses offered—are represented along the horizontal axis, and the frequencies, or the number of students, are given along the vertical axis.

Then, draw bars of equal width connecting the categories and the corresponding number of students. The height of these bars denotes the frequencies of different categories.

The first bar indicates that five students are enrolled in Statistics, the second bar shows three students enrolled in Biology, and so on. These bars can be placed with or without gaps between them.

2.12: Bar Graph

A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals in each category. Some bar charts show bars clustered in groups of more than one (grouped bar graphs), and others depict the bars divided into subparts to show cumulative effect (stacked bar graphs). A bar graph is a better choice than a line graph or histogram because this data is categorical rather than continuous. Also, it is appropriate to compare the relative size of the categories. It is to be noted that the bar graphs that are organized from highest to lowest are called Pareto charts.

Chapter 1: Understanding Statistics

Chapter 2: Summarizing and Visualizing Data

Chapter 3: Measure of Central Tendency

Chapter 4: Measures of Variation

Chapter 5: Measures of Relative Standing

Chapter 6: Probability Distributions

Chapter 7: Estimates

Chapter 8: Distributions

Chapter 9: Hypothesis Testing

Chapter 10: Analysis of Variance

Chapter 11: Correlation and Regression

Chapter 12: Statistics in Practice

2.12: Bar Graph

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