🎓 Academic [DATA AND ANALYSIS] Multiple Random Variables and the Sampling Distribution

[getRONed]

MATH MAJORS OR ENGINEERS PA HELP DITO
Our professor taught us how to calculate the probability using the formula
1/1+e^(-AX³-BX) or 1/1+e^(AX³+BX)
instead of using the double integral, as it is much easier and convenient.

In solving for the total standard deviation, I find it very hard to distinguish whether to use the standard error formula (σ/√n) or the formula σ√n.

Need clarification, quiz namin next week. Gracias!

 

[Lowrangeee]

Probability Formula​

It looks like your professor is teaching you a logistic function, which is commonly used in statistics and machine learning for binary classification problems. The formula you mentioned,

1+e−AX3−BX1

or
1+eAX3+BX1

, is a variation of the logistic function.

Standard Deviation vs. Standard Error​

When it comes to standard deviation and standard error, it’s important to understand the context in which each is used:

1. Standard Deviation (σ): This measures the amount of variation or dispersion in a set of values. It is used to quantify the amount of variation in a dataset.
2. Standard Error (SE): This measures the accuracy with which a sample represents a population. It is calculated as
   nσ
   , where ( \sigma ) is the standard deviation of the sample and ( n ) is the sample size.
   

When to Use Each Formula​

• Standard Deviation (σ): Use this when you are interested in the variability within a single dataset.
• Standard Error (SE): Use this when you are interested in how accurately your sample mean represents the population mean. This is particularly useful in inferential statistics, where you are making predictions or inferences about a population based on a sample.

Example​

If you have a dataset of exam scores for a class of students, the standard deviation will tell you how spread out the scores are. If you take a sample of students from the class and want to estimate the average score of the entire class, you would use the standard error to understand how close your sample mean is likely to be to the true population mean.


I hope this helps!

 

[getRONed]

Probability Formula​

It looks like your professor is teaching you a logistic function, which is commonly used in statistics and machine learning for binary classification problems. The formula you mentioned,

1+e−AX3−BX1

or
1+eAX3+BX1

, is a variation of the logistic function.

Standard Deviation vs. Standard Error​

When it comes to standard deviation and standard error, it’s important to understand the context in which each is used:

1. Standard Deviation (σ): This measures the amount of variation or dispersion in a set of values. It is used to quantify the amount of variation in a dataset.
2. Standard Error (SE): This measures the accuracy with which a sample represents a population. It is calculated as
   nσ
   , where ( \sigma ) is the standard deviation of the sample and ( n ) is the sample size.

When to Use Each Formula​

• Standard Deviation (σ): Use this when you are interested in the variability within a single dataset.
• Standard Error (SE): Use this when you are interested in how accurately your sample mean represents the population mean. This is particularly useful in inferential statistics, where you are making predictions or inferences about a population based on a sample.

Example​

If you have a dataset of exam scores for a class of students, the standard deviation will tell you how spread out the scores are. If you take a sample of students from the class and want to estimate the average score of the entire class, you would use the standard error to understand how close your sample mean is likely to be to the true population mean.


I hope this helps!

ᑕᕼᗩTGᑭT aint helping brother. I need real explanation

 

[Lowrangeee]

ᑕᕼᗩTGᑭT aint helping brother. I need real explanation