The normal distribution is a continuous probability distribution that can be presented on a graph. Continuous probability distributions represent continuous random variables, which can take one or more values. Some examples of continuous variables that can be displayed on a normal distribution graph are:
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Jetzt kostenlos anmeldenThe normal distribution is a continuous probability distribution that can be presented on a graph. Continuous probability distributions represent continuous random variables, which can take one or more values. Some examples of continuous variables that can be displayed on a normal distribution graph are:
Height
Weight
Measurement errors
The bell-shaped curve on the normal distribution graph is symmetrical to the mean, which can be represented by the symbol \(\mu\). The area under the curve is equal to 1. Below is an example of what the normal distribution graph looks like:
The standard deviation tells you how spread out the data are. When this is calculated from the curve above, it can tell you certain things about the data:
68% of the data fall within one standard deviation from the mean, making the probability likely.
95% of the data fall within two standard deviations of the mean, making the probability very likely.
Nearly all the data, 99.7% of the data fall within three standard deviations of the mean, making the probability almost certain.
Standard deviation is a measurement of how spread out the data is, and it can be notated with the symbol \(\sigma\).
When looking at normal distribution, the notation can be written as:
\[X \sim N(\mu, \sigma^2)\]
\(\mu\) = the mean \(\sigma^2\) = the population variance
When you are given this notation, it gives you the information needed to create a normal distribution curve.
The distribution of X is modelled as \(X \sim N(23, 0.25^2)\). Sketch the distribution of X.
To do this, you can start by identifying the mean and the population variance:
\[\mu = 23 \qquad \sigma^2 = 0.25\]
You know that on a normal distribution graph, the curve is symmetrical about the mean, which allows you to draw the bell shape:
The lengths of wingspans are normally distributed with a mean of 22 cm and a standard deviation of 0.4. Sketch the distribution of X.
To do this, you know that the bell-shaped curve will be symmetrical about the mean; therefore, you can sketch the graph as follows:
Normal distribution can help you to find probabilities, and to do this you can use the normal cumulative function on your calculator.
Standard normal distribution is a way of standardising normal distribution. It has a mean, \(\mu\), of 0 and a standard deviation, \(\sigma\), of 1. The notation for the standard normal distribution can be written as:
\[Z \sim N(0,1^2)\]
Normal distribution is a continuous probability distribution that can be presented on a graph.
To calculate normal distribution you can use the formula, X~N(μ,σ^2), where μ represents the mean and σ^2 represents the population variance.
To find probabilities using normal distributions you can use the normal cumulative function on your calculator.
What is the normal distribution?
The normal distribution is a continuous probability distribution that can be presented on a graph.
What does the normal distribution represent? Please give examples.
The normal distribution represents continuous random variables, such as, height, weight and measurement errors.
What is the normal distribution curve?
The normal distribution curve is a bell shaped curve that is symmetrical to the mean.
What can the standard deviation tell you?
The standard deviation tells you how spread out the data is.
What does it mean when the data falls within one standard deviation of the mean?
68% of the data falls here, the probability is likely.
What does it mean when the data falls within two standard deviations of the mean?
95% of the data falls here, the probability is very likely.
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