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Related Concept Videos

Applications of Normal Distribution01:22

Applications of Normal Distribution

5.0K
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...
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Central Limit Theorem01:14

Central Limit Theorem

14.7K
The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
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Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
5.3K
Normal Distribution01:11

Normal Distribution

10.8K
The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is...
10.8K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K

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Effects of Antibiotic Residues on Fish Gut Microbiome Dysbiosis and Mucosal Barrier-Related Pathogen Susceptibility in Zebrafish Experimental Model.

Antibiotics (Basel, Switzerland)·2024
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Proportion-based normalizations outperform compositional data transformations in machine learning applications.

Aaron Yerke1,2, Daisy Fry Brumit1, Anthony A Fodor3

  • 1Department of Bioinformatics and Genomics, Bioinformatics Building, UNC Charlotte, The University of North Carolina, Charlotte 9331 Robert D. Snyder Rd, Charlotte, USA.

Microbiome
|March 5, 2024
PubMed
Summary
This summary is machine-generated.

For microbiome machine learning, simple relative abundance transformations often outperform complex compositionally aware methods. Minimizing transformation complexity while correcting for read depth is a recommended strategy.

Area of Science:

  • Microbiome bioinformatics
  • Machine learning in biology
  • Data pre-processing techniques

Background:

  • Normalization is crucial for microbiome machine learning resolution.
Keywords:
Compositional dataHigh-throughput nucleotide sequencingMachine learningMetagenomicsNormalizationPhILRRandom forestStatistical data interpretationTransformation

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  • Numerous normalization schemes exist, impacting analysis outcomes.
  • Compositional data analysis methods are increasingly used for microbiome data.