Neither the left or the right but the middle
When the majority of the data values fall to the right of the mean, the distribution is indeed said to be left skewed, or negatively skewed. In this type of distribution, the tail on the left side is longer or fatter, indicating that there are a few lower values pulling the mean down. This results in the mean being less than the median, as the median is less affected by extreme values. Overall, left skewed distributions show that most data points are higher than the average.
In a standard normal distribution, approximately 95% of the data falls within two standard deviations (±2σ) of the mean (μ). This means that if you take the mean and add or subtract two times the standard deviation, you capture the vast majority of the data points. This property is a key aspect of the empirical rule, which describes how data is spread in a normal distribution.
In a normal distribution, approximately 95% of the data falls within 2 standard deviations of the mean. This is part of the empirical rule, which states that about 68% of the data is within 1 standard deviation, and about 99.7% is within 3 standard deviations. Therefore, the range within 2 standard deviations captures a significant majority of the data points.
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.
It is not necessary that all symetric distribution may be normal.
on the left and when it is skewed left it is on the right
positively skewed
yes
1. The typical distribution of data in a bell curve shows that variations occur rarely and the majority of data is clustered around a mean or average. 2. The distribution of funds by the board of directors will be decided based on several factors that affect the organizations needs. 3. After the earthquake, the aid relief was quick to respond with distribution of water, food and medical supplies
When the majority of the data values fall to the right of the mean, the distribution is indeed said to be left skewed, or negatively skewed. In this type of distribution, the tail on the left side is longer or fatter, indicating that there are a few lower values pulling the mean down. This results in the mean being less than the median, as the median is less affected by extreme values. Overall, left skewed distributions show that most data points are higher than the average.
In a standard normal distribution, approximately 95% of the data falls within two standard deviations (±2σ) of the mean (μ). This means that if you take the mean and add or subtract two times the standard deviation, you capture the vast majority of the data points. This property is a key aspect of the empirical rule, which describes how data is spread in a normal distribution.
frequency distribution contain qualitative data
Many line of
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.
In a normal distribution, approximately 95% of the data falls within 2 standard deviations of the mean. This is part of the empirical rule, which states that about 68% of the data is within 1 standard deviation, and about 99.7% is within 3 standard deviations. Therefore, the range within 2 standard deviations captures a significant majority of the data points.