Name: Percentage Frequency Distribution
Uploaded: 2022-01-24T13:51:26-05:00
Duration: 57 s
Description: A percentage frequency distribution, in general, is a display of data that indicates the percentage of observations for each data point or grouping of data points.

Chapter 2: Summarizing and Visualizing Data Back To Chapter

2.5: Percentage Frequency Distribution

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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Percentage Frequency Distribution

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2.4: Relative Frequency Distribution

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2.6: Cumulative Frequency Distribution

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TRANSCRIPT

Consider a relative frequency distribution table of hockey players with different heights. This table provides information about the fraction, or proportion, of data values under each class.

If this relative frequency is expressed in terms of percentage, it is called the percentage frequency distribution.

Suppose one is interested in the percentage of players with heights between 152 and 157 centimeters. To find out, multiply the corresponding relative frequency with 100 to get the percentage frequency. This indicates that 5 percent of players fall within the required height range.

Repeat the similar calculation for all the other relative frequencies to obtain the percentage frequencies under each class. Generally, the sum of all the percentage frequencies is equal to 100.

2.5: Percentage Frequency Distribution

A percentage frequency distribution, in general, is a display of data that indicates the percentage of observations for each data point or grouping of data points. It is a commonly used method for expressing the relative frequency of survey responses and other data. The percentage frequency distributions are often displayed as bar graphs, pie charts, or tables.

The process of making a percentage frequency distribution involves the following few steps: note the total number of observations; count the total number of observations within each data point or grouping of data points; and finally, divide the total observations within each data point or grouping of data points by the total number of observations. However, it is to be noted that whenever the percentage frequencies are used in the relative frequency distribution, it is also sometimes termed as percentage frequency distribution.

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

2.5: Percentage Frequency Distribution

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