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### 6.12: Applications of Normal Distribution TABLE OFCONTENTS X ## Chapter 1: Understanding Statistics 301.1: Introduction to Statistics301.2: How Data are Classified: Categorical Data301.3: How Data are Classified: Numerical Data301.4: Nominal Level of Measurement301.5: Ordinal Level of Measurement301.6: Interval Level of Measurement301.7: Ratio Level of Measurement301.8: Data Collection by Observations301.9: Data Collection by Experiments301.10: Data Collection by Survey301.11: Random Sampling Method301.12: Systematic Sampling Method301.13: Convenience Sampling Method301.14: Stratified Sampling Method301.15: Cluster Sampling Method ## Chapter 2: Summarizing and Visualizing Data 302.1: Review and Preview302.2: What is a Frequency Distribution302.3: Construction of Frequency Distribution302.4: Relative Frequency Distribution302.5: Percentage Frequency Distribution302.6: Cumulative Frequency Distribution302.7: Ogive Graph302.8: Histogram302.9: Relative Frequency Histogram302.10: Scatter Plot302.11: Time-Series Graph302.12: Bar Graph302.13: Multiple Bar Graph302.14: Pareto Chart302.15: Pie Chart ## Chapter 3: Measure of Central Tendency 303.1: What is Central Tendency?303.2: Arithmetic Mean303.3: Geometric Mean303.4: Harmonic Mean303.5: Trimmed Mean303.6: Weighted Mean303.7: Root Mean Square303.8: Mean From a Frequency Distribution303.9: What is a Mode?303.10: Median303.11: Midrange303.12: Skewness303.13: Types of Skewness ## Chapter 4: Measures of Variation 304.1: What is Variation?304.2: Range304.3: Standard Deviation304.4: Standard Error of the Mean304.5: Calculating Standard Deviation304.6: Variance304.7: Coefficient of Variation304.8: Range Rule of Thumb to Interpret Standard Deviation304.9: Empirical Method to Interpret Standard Deviation304.10: Chebyshev's Theorem to Interpret Standard Deviation304.11: Mean Absolute Deviation ## Chapter 5: Measures of Relative Standing 305.1: Review and Preview305.2: Introduction to z Scores305.3: z Scores and Unusual Values305.4: Percentile305.5: Quartile305.6: 5-Number Summary305.7: Boxplot305.8: What Are Outliers?305.9: Modified Boxplots ## Chapter 6: Probability Distributions 306.1: Probability in Statistics306.2: Random Variables306.3: Probability Distributions306.4: Probability Histograms306.5: Unusual Results306.6: Expected Value306.7: Binomial Probability Distribution306.8: Poisson Probability Distribution306.9: Uniform Distribution306.10: Normal Distribution306.11: z Scores and Area Under the Curve306.12: Applications of Normal Distribution306.13: Sampling Distribution306.14: Central Limit Theorem ## Chapter 7: Estimates 307.1: What are Estimates?307.2: Sample Proportion and Population Proportion307.3: Confidence Intervals307.4: Confidence Coefficient307.5: Interpretation of Confidence Intervals307.6: Critical Values307.7: Margin of Error307.8: Sample Size Calculation307.9: Estimating Population Mean with Known Standard Deviation307.10: Estimating Population Mean with Unknown Standard Deviation307.11: Confidence Interval for Estimating Population Mean ## Chapter 8: Distributions 308.1: Distributions to Estimate Population Parameter308.2: Degrees of Freedom308.3: Student t Distribution308.4: Choosing Between z and t Distribution308.5: Chi-square Distribution308.6: Finding Critical Values for Chi-Square308.7: Estimating Population Standard Deviation308.8: Goodness-of-Fit Test308.9: Expected Frequencies in Goodness-of-Fit Tests308.10: Contingency Table308.11: Introduction to Test of Independence308.12: Hypothesis Test for Test of Independence308.13: Determination of Expected Frequency308.14: Test for Homogeneity308.15: F Distribution ## Chapter 9: Hypothesis Testing 309.1: What is a Hypothesis?309.2: Null and Alternative Hypotheses309.3: Critical Region, Critical Values and Significance Level309.4: P-value309.5: Types of Hypothesis Testing309.6: Decision Making: P-value Method309.7: Decision Making: Traditional Method309.8: Hypothesis: Accept or Fail to Reject?309.9: Errors In Hypothesis Tests309.10: Testing a Claim about Population Proportion309.11: Testing a Claim about Mean: Known Population SD309.12: Testing a Claim about Mean: Unknown Population SD309.13: Testing a Claim about Standard Deviation ## Chapter 10: Analysis of Variance 3010.1: What is an ANOVA?3010.2: One-Way ANOVA3010.3: One-Way ANOVA: Equal Sample Sizes3010.4: One-Way ANOVA: Unequal Sample Sizes3010.5: Multiple Comparison Tests3010.6: Bonferroni Test3010.7: Two-Way ANOVA ## Chapter 11: Correlation and Regression 3011.1: Correlation3011.2: Coefficient of Correlation3011.3: Calculating and Interpreting the Linear Correlation Coefficient3011.4: Regression Analysis3011.5: Outliers and Influential Points3011.6: Residuals and Least-Squares Property3011.7: Residual Plots3011.8: Variation3011.9: Prediction Intervals3011.10: Multiple Regression ## Chapter 12: Statistics in Practice 3012.1: What is an Experiment?3012.2: Study Design in Statistics3012.3: Observational Studies3012.4: Experimental Designs3012.5: Randomized Experiments3012.6: Crossover Experiments3012.7: Controls in Experiments3012.8: Bias3012.9: Blinding3012.10: Clinical Trials Full Table of Contents

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Applications of Normal Distribution

### 6.12: Applications of Normal Distribution

The normal distribution is a useful statistical tool. One of its practical applications is determining the door height after considering the normal distribution of heights of persons, such that many can pass through it easily without striking their heads. The normal distribution can also determine the probability of a person having a height less than a specific height.

The heights of 15 to 18-year-old males from Chile from 1984 to 1985 followed a normal distribution. The mean height is 172.36 cm, and the standard deviation of 6.34 cm. This information can be used to find the probability of males from Chile having a height of less than 162.85 cm.

Begin by finding the z score for the height of 162.85 cm. After using the formula for the z score, the value is found to be -1.5. From the table for negative z scores, the cumulative area under the curve (from the left of the standard normal distribution) or the probability is found to be 0.0668. Converting this value to a percentage gives 6.68%. It can be concluded that there is a 6.68% probability of males among 15 to 18-year-old males that have a height below 162.85 cm.

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Normal Distribution Statistical Tool Door Height Probability Specific Height Mean Height Standard Deviation Z Score Cumulative Area Probability Percentage

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