can someone show Q1 for AB is 5 ? for Ac is -5 , while for CB is -2 ?foo9008 said:
It's a problem when you reply to your own post that other readers think you've had a reply unless they follow the link to the thread.foo9008 said:
It's only a trial solution. Any values could have been used that add up correctly.foo9008 said:
what do you mean by trial solution ? I'm confused... how did the author get the Q= 5, -5 , and -2 based on 10 , 3 and 7 ?haruspex said:
I mean it is just an initial guess, consistent with the 10, 3 and 7. The method goes through an iteration to get closer to the actual solution. As an exercise, you could start with 6, -4 and -1 instead and check that you end up with the same final answer.foo9008 said:
is there any rule to choose the 3 numbers for initial guess? e.g , if i choose the first number is 15 , will it affect the second and third number that i am choosing ? or it is just guessing ?haruspex said:
The purpose of finding Q1 values for A, B, and C is to understand the distribution of the data and identify the middle point or median of the first quarter of the data set. This can help in analyzing the data and making informed decisions.
To calculate Q1 values, first arrange the data in ascending order. Then, find the median of the lower half of the data set, which will be the Q1 value for A. Similarly, find the median of the lower half of the remaining data to get the Q1 values for B and C.
Q1, Q2, and Q3 values are all measures of central tendency used to understand the distribution of a data set. Q1 represents the median of the first quarter of the data, Q2 represents the median of the entire data set, and Q3 represents the median of the last quarter of the data.
Finding the Q1 values for A, B, and C is important because it helps in identifying any outliers or extreme values in the data set. It also provides a better understanding of the spread of the data and helps in making more accurate interpretations and predictions.
Q1 values can be used in data analysis to compare the distribution of different data sets. They can also be used to calculate other measures of variability such as interquartile range and to identify any potential anomalies or patterns in the data.
