Roots of the normal distribution

In summary, the roots of the given equation are the values of x at which f(x) equals zero, but since the normal distribution curve never actually reaches zero, the roots can be expressed as the limit of f(x) as x approaches positive or negative infinity, which is equal to zero. Therefore, the equation has no roots but has a limit of 0.
  • #1
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Homework Statement


$$f:\mathbb{R} \rightarrow \mathbb{R},$$

$$ f(x) = \frac{1}{\sigma \sqrt{2 \pi}} e^{\frac{-(x-\mu)^2}{2 \sigma ^{2}}}$$

What are the roots of this equation?

Homework Equations

The Attempt at a Solution



The roots of an equation are the values of [itex]x[/itex] such that [itex]f(x) = 0[/itex]. This is the first time I have seen a question like this and am still getting my head around the normal distribution, but as far as I'm aware the curve never does reach [itex]f(x) = 0[/itex] so I want to express the idea that the roots of this equation are [itex]+/- \infty[/itex] but I don't know how to do this...

[itex]lim_{x \rightarrow +/- \infty} f(x) = 0[/itex]

I'd appreciate some guidance,

thanks :)
 
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  • #2
It doesn't look to me like you need much guidance on this. You have it exactly correct. But I wouldn't say the roots are ##\pm \infty##. Just say it has no roots but the limit is 0 as you have stated.
 
  • #3
LCKurtz said:
It doesn't look to me like you need much guidance on this. You have it exactly correct. But I wouldn't say the roots are ##\pm \infty##. Just say it has no roots but the limit is 0 as you have stated.

Well that is good news, thanks!
 

1. What is the normal distribution?

The normal distribution, also known as the Gaussian distribution, is a probability distribution that is bell-shaped and symmetrical. It is characterized by a mean (average) and a standard deviation, and is often used to describe natural phenomena such as human height, test scores, and blood pressure.

2. What are the properties of the normal distribution?

The normal distribution has several important properties, including:

  • The mean, median, and mode are all equal.
  • The curve is symmetrical about the mean.
  • 68% of the data falls within one standard deviation of the mean, 95% falls within two standard deviations, and 99.7% falls within three standard deviations.
  • The area under the curve represents the probability of a random variable falling within a certain range of values.

3. What are the applications of the normal distribution?

The normal distribution is widely used in statistics and data analysis. Some common applications include:

  • Hypothesis testing and confidence intervals - the normal distribution is often used to determine whether a sample is representative of a larger population.
  • Quality control - the normal distribution can be used to monitor and analyze the variability of a process.
  • Forecasting - the normal distribution can be used to model and predict future outcomes.
  • Risk assessment - the normal distribution is often used to calculate the likelihood of certain events or outcomes.

4. What are the factors that can affect the shape of the normal distribution?

The shape of the normal distribution can be affected by several factors, including:

  • The mean and standard deviation - changing these parameters can shift the curve and change its spread.
  • Skewness - if the data is skewed (asymmetrical), the normal distribution may not be the best fit.
  • Outliers - extreme values in the data can affect the shape of the curve.
  • Sample size - larger sample sizes tend to produce more accurate and symmetrical normal distributions.

5. How is the normal distribution related to the central limit theorem?

The central limit theorem states that as the sample size increases, the sampling distribution of the mean will approach a normal distribution regardless of the shape of the population distribution. This means that even if the data is not normally distributed, we can still use the normal distribution to make inferences about the population mean. This makes the normal distribution an essential tool in statistics and data analysis.

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