Yes- the highest probability value is the mode. Let me clarify this answer: For a probability mass function for a discrete variables, the mode is the value with the highest probability as shown on the y axis. For a probability density function for continuous variables, the mode is the value with the highest probability density as shown on the y-axis.
Mode is the most frequently occurring value in a data set. See related link. Note that in statistics, the definition is related to the data collected. In probability, the definition is related to the probability distribution which describes the random variable.
Yes. When we refer to the normal distribution, we are referring to a probability distribution. When we specify the equation of a continuous distribution, such as the normal distribution, we refer to the equation as a probability density function.
No. Normal distribution is a continuous probability.
The statement is true that a sampling distribution is a probability distribution for a statistic.
Yes, the uniform probability distribution is symmetric about the mode. Draw the sketch of the uniform probability distribution. If we say that the distribution is uniform, then we obtain the same constant for the continuous variable. * * * * * The uniform probability distribution is one in which the probability is the same throughout its domain, as stated above. By definition, then, there can be no value (or sub-domain) for which the probability is greater than elsewhere. In other words, a uniform probability distribution has no mode. The mode does not exist. The distribution cannot, therefore, be symmetric about something that does not exist.
Yes- the highest probability value is the mode. Let me clarify this answer: For a probability mass function for a discrete variables, the mode is the value with the highest probability as shown on the y axis. For a probability density function for continuous variables, the mode is the value with the highest probability density as shown on the y-axis.
They are probability distributions!
Mode is the most frequently occurring value in a data set. See related link. Note that in statistics, the definition is related to the data collected. In probability, the definition is related to the probability distribution which describes the random variable.
Yes. When we refer to the normal distribution, we are referring to a probability distribution. When we specify the equation of a continuous distribution, such as the normal distribution, we refer to the equation as a probability density function.
No. Normal distribution is a continuous probability.
The statement is true that a sampling distribution is a probability distribution for a statistic.
how do i find the median of a continuous probability distribution
A bell shaped probability distribution curve is NOT necessarily a normal distribution.
None. The full name is the Probability Distribution Function (pdf).
They are the same. The full name is the Probability Distribution Function (pdf).
Normal distribution is the continuous probability distribution defined by the probability density function. While the binomial distribution is discrete.