Normal distribution property

Related BrainMass Solutions

• Normal Distribution and Binomial Experiment

  (2) If a and b are real numbers, and X ~ N(, ^2), then the normal distribution has the property that X ~ N(a + b, (a)^2) It is because of this property that the Standard Normal Distribution can be used for all normal distributions.

• Symmetric Property of Normal Distribution

  The solution uses the symmetric property of normal distribution to determine the two values that 80% of the apples will fall.

• Probabilities and statistics in insurance

  An insurance company has two divisions, auto and property. Total annual claims, X, in the auto division follow a normal distribution with mean 10 and standard deviation 3.

• Standard normal random variables

  Therefore, we use the symmetry property of the standard normal distribution: P(Z < a) = P (Z > -a) Therefore, P (Z > - 0.64) = P(Z < 0.64) And now we can use the table.

• Exponential Distribution compared to Poisson Distribution

  The normal distribution has the property that the curve is symmetric at the center point.

• Reliability maths problems and solutions

  We will then use the symmetrical property of the distribution to work our way back to determine the 95% reliability time based on reflection of the results obtained for 5% reliability about the mean For the normal distribution 5% reliability represents

• Approximate Distribution: Central Limits and Delta

  , roughly follows a normal distribution with mean and variance .

• Population Sample Data, Binomial & Normal Distributions

  The normal distribution follows: The 68-95-99.7% Rule All normal density curves satisfy the following property which is often referred to as the Empirical Rule. 68% of the observations fall within 1 standard deviation of the mean, that is, between

• Normal distribution, exponential & Poisson distributions

  The probability distribution of a Poisson random variable is called a Poisson distribution. The Poisson distribution has the property that the mean of the distribution is meu and the variance is also equal to meu.