Normal distribution models what type of variable??
Random variable
The normal distribution, also known as the Gaussian distribution, models continuous variables.
To understand what a normal distribution models, one must first understand what a continuous variable is. A continuous variable is one that can take on any value within a range. Examples of continuous variables include height, weight, temperature, and time.
The normal distribution is a probability distribution that is symmetric and bell-shaped. It is defined by its mean (μ) and standard deviation (σ). The mean represents the center of the distribution, and the standard deviation represents the spread or dispersion of the values around the mean.
The normal distribution models variables that are naturally occurring and have a tendency to cluster around a central value, with fewer occurrences at the extremes. Many real-world phenomena can be approximated by a normal distribution, such as the heights of individuals in a population, the scores on a standardized test, or the error in measurements.
To determine whether a variable can be modeled by a normal distribution, one can examine the histogram or plot the data on a normal probability plot. If the data roughly follows a bell-shaped curve and the points on the normal probability plot form a relatively straight line, then it suggests that the variable may be normally distributed.