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Chapter 4: Problem 180

An article in Journal of Hydrology ["Use of a Lognormal Distribution Model for Estimating Soil Water Retention Curves from Particle-Size Distribution Data" \((2006,\) Vol. \(323(1),\) pp. \(325-334)]\) considered a lognormal distribution model to estimate water retention curves for a range of soil textures. The particle-size distribution (in centimeters) was modeled as a lognormal random variable \(X\) with \(\theta=-3.8\) and \(\omega=0.7 .\) Determine the following: (a) \(P(X<0.02)\) (b) Value for \(x\) such that \(P(X \leq x)=0.95\) (c) Mean and variance of \(X\)

Short Answer

Expert verified
(a) 0.1151 (b) x 鈮� 0.0231 (c) Mean 鈮� 0.0247, Variance 鈮� 0.00036.

Step by step solution

01

Understanding Lognormal Distribution

The particle-size distribution is modeled as a lognormal distribution. This means that if a random variable \( X \) is lognormally distributed, then \( \ln(X) \) is normally distributed with parameters \( \theta \) and \( \omega^2 \). For this problem, \( \theta = -3.8 \) and \( \omega = 0.7 \).
02

Calculating P(X < 0.02)

To find \( P(X < 0.02) \), we first convert the condition to the standard normal form: \( \ln(X) < \ln(0.02) \). Calculate \( \ln(0.02) \). Then use the standard normal distribution \( Z = \frac{\ln(X) - \theta}{\omega} \) to find the probability. \( Z = \frac{\ln(0.02) + 3.8}{0.7} \). Finally, use standard normal distribution tables (or a calculator) to find \( P(Z < z) \).
03

Calculating Value of x for P(X 鈮� x) = 0.95

Since \( \ln(X) \) is normally distributed, we set \( \frac{\ln(x) - \theta}{\omega} = z_{0.95} \), where \( z_{0.95} \) is the 95th percentile of the standard normal distribution, approximately 1.645. Rearrange this to find \( x \): \( x = e^{\theta + \omega \cdot z_{0.95}} = e^{-3.8 + 0.7 \cdot 1.645} \). Calculate \( x \).
04

Finding Mean and Variance of X

The mean \( \mu_X \) of a lognormal distribution is given by \( \mu_X = e^{\theta + \frac{\omega^2}{2}} \). The variance \( \sigma_X^2 \) is given by \( \sigma_X^2 = (e^{\omega^2} - 1) \cdot e^{2\theta + \omega^2} \). Substitute \( \theta = -3.8 \) and \( \omega = 0.7 \) into these formulas to find the mean and variance.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Soil Water Retention
Soil water retention is a critical aspect of studying various soil-related properties and behaviors. It helps us understand how water moves through soil and how it is retained. This concept is especially important for agricultural applications, as well as hydrological studies.
  • Soil water retention curves depict the relationship between soil water content and soil water potential.
  • The particle-size distribution in soil is a significant factor affecting water retention.
  • Estimating soil water retention involves modeling soil as a composition of different particle sizes, influencing overall porosity and capacity to retain water.
In the exercise, the particle-size distribution is modeled using a lognormal distribution to estimate these retention characteristics effectively.
Step-by-Step Solution
The step-by-step solution in the problem illustrates how soil particle distributions, modeled via the lognormal distribution, allow us to estimate certain probabilities and values. To understand this, let's look at the breakdown of the steps provided:
  • Firstly, understanding the type of distribution: A Lognormal Distribution, where the natural logarithm of the variable is normally distributed, allows handling skewed data like soil particles efficiently.
  • Computing probabilities for given conditions: This involves transforming the lognormal variable into a standard normal form to use familiar statistical methods.
  • Finding specific data points: By utilizing percentile information and rearranging equations, we find specific values that meet certain probability criteria.
  • Calculating Mean and Variance: This provides a summary statistic that describes the expected value and variability of soil particle size, crucial for interpreting various soil behaviors and engineering practices.
Through these methodical approaches, the solution grounds the abstract concept of lognormal distributions in real-world soil science.
Probability Estimation
Estimating probability plays a crucial role in making predictions about soil water retention. In a lognormal distribution, estimating the probability involves converting measurements into a form that fits within the context of normal distribution tables.
Here鈥檚 a simple overview:
  • Transform the soil particle size value to a logarithmic scale, utilizing the parameters provided, such as mean \(\theta\) and standard deviation \(\omega\).
  • Use statistical tables or calculators to find the cumulative probability corresponding to this transformed value.
  • This process helps in estimating the likelihood of soil particles being below a certain size, which directly impacts water retention capacity.
Applying probability estimation in soils helps in retaining consistency and improves prediction accuracy for agricultural planning and hydrology research.
Mean and Variance
Understanding the mean and variance of a lognormal distribution provides valuable insights into how soil particles are distributed, impacting their ability to retain water.
  • The mean of the lognormal distribution (\( \mu_X \)) provides an average particle size when measured over many samples, helping in understanding general trends.
  • Variance (\( \sigma_X^2 \)) indicates the level of spread or dispersion around the mean, giving an idea about the diversity of particle sizes.
  • These statistics are calculated using specific formulas: \( \mu_X = e^{\theta + \frac{\omega^2}{2}} \) and\( \sigma_X^2 = (e^{\omega^2} - 1) \cdot e^{2\theta + \omega^2} \).
Armed with these calculations, you can predict and analyze soil behavior more accurately, aiding in the development of effective soil management strategies.
Journal of Hydrology
The Journal of Hydrology is a key publication in the field of water science, featuring groundbreaking research such as the study mentioned in the exercise. Insights provided in such journals help in understanding and applying scientific concepts to practical problems like soil water retention.
  • The journal publishes high-quality studies, often employing statistics and models to address significant hydrological questions.
  • Articles often serve as references for educational purposes, providing methodologies that can be applied or adapted in various contexts.
  • By contributing to global knowledge, journals like this foster innovation and progress in tackling water-related challenges.
Reading and understanding articles in these journals can elevate your comprehension and equip you with the necessary tools to tackle practical applications in both study and professional work.

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Most popular questions from this chapter

If the random variable \(X\) has an exponential distribution with mean \(\theta,\) determine the following: (a) \(P(X>\theta)\) (b) \(P(X>2 \theta)\) (c) \(P(X>3 \theta)\) (d) How do the results depend on \(\theta\) ?

Use the result for the gamma distribution to determine the mean and variance of a chi-square distribution with \(r=7 / 2\).

Suppose that \(X\) has a Weibull distribution with \(\beta=2\) and \(\delta=8.6 .\) Determine the following: (a) \(P(X<10)\) (b) \(P(X>9)\) (c) \(P(8x)=0.9\)

An airline makes 200 reservations for a flight that holds 185 passengers. The probability that a passenger arrives for the flight is \(0.9,\) and the passengers are assumed to be independent. (a) Approximate the probability that all the passengers who arrive can be seated. (b) Approximate the probability that the flight has empty seats. (c) Approximate the number of reservations that the airline should allow so that the probability that everyone who arrives can be seated is \(0.95 .\) [Hint: Successively try values for the number of reservations.]

The lifetime of a mechanical assembly in a vibration test is exponentially distributed with a mean of 400 hours. (a) What is the probability that an assembly on test fails in less than 100 hours? (b) What is the probability that an assembly operates for more than 500 hours before failure? (c) If an assembly has been on test for 400 hours without a failure, what is the probability of a failure in the next 100 hours? (d) If 10 assemblies are tested, what is the probability that at least one fails in less than 100 hours? Assume that the assemblies fail independently. (e) If 10 assemblies are tested, what is the probability that all have failed by 800 hours? Assume that the assemblies fail independently.
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