just go to the question like this and it will tell you
the midpoint of the data set
we prefer normal distribution over other distribution in statistics because most of the data around us is continuous. So, for continuous data normal distribution is used.
No, a frequency distribution is not a way to describe numerical data categorically; rather, it organizes numerical data into intervals or bins to show how often each range occurs. It provides a summary of the data's distribution by displaying the counts or frequencies of values within specified ranges. While categorical data can also be summarized in a frequency distribution, the term primarily refers to numerical data organized based on value ranges.
A common type of distribution used to organize numeric data is the normal distribution, which is characterized by its bell-shaped curve and symmetric properties around the mean. Additionally, other distributions such as the binomial distribution and Poisson distribution are used for specific types of data, particularly in cases involving discrete outcomes. These distributions help in understanding the underlying patterns and behaviors of the data, making it easier to analyze and interpret.
In parametric statistical analysis we always have some probability distributions such as Normal, Binomial, Poisson uniform etc.In statistics we always work with data. So Probability distribution means "from which distribution the data are?
it is used to find mean<median and mode of grouped data
Frequently it's impossible or impractical to test the entire universe of data to determine probabilities. So we test a small sub-set of the universal database and we call that the sample. Then using that sub-set of data we calculate its distribution, which is called the sample distribution. Normally we find the sample distribution has a bell shape, which we actually call the "normal distribution." When the data reflect the normal distribution of a sample, we call it the Student's t distribution to distinguish it from the normal distribution of a universe of data. The Student's t distribution is useful because with it and the small number of data we test, we can infer the probability distribution of the entire universal data set with some degree of confidence.
Frequently it's impossible or impractical to test the entire universe of data to determine probabilities. So we test a small sub-set of the universal database and we call that the sample. Then using that sub-set of data we calculate its distribution, which is called the sample distribution. Normally we find the sample distribution has a bell shape, which we actually call the "normal distribution." When the data reflect the normal distribution of a sample, we call it the Student's t distribution to distinguish it from the normal distribution of a universe of data. The Student's t distribution is useful because with it and the small number of data we test, we can infer the probability distribution of the entire universal data set with some degree of confidence.
to help determine and give insight into the data colleced.
frequency distribution contain qualitative data
It is a positively skewed distribution.
Methods of data distribution include centralized distribution, where data is stored and managed in a single location, and decentralized distribution, where data is spread across multiple locations or nodes. Other methods include peer-to-peer distribution, where data is shared directly between users without a central server, and cloud-based distribution, which leverages internet-based services to store and distribute data. Additionally, streaming distribution is used for real-time data delivery, while batch processing is utilized for larger datasets processed at scheduled intervals.
A normal data set is a set of observations from a Gaussian distribution, which is also called the Normal distribution.
Assuming that we have a Normal Distribution of Data, approx. 65% of the data will fall within One Sigma.
In the normal distribution, the mean and median coincide, and 50% of the data are below the mean.
the midpoint of the data set
The answer will depend on the set of data!