Already have an account? Get multiple benefits of using own account!
Login in your account..!
Remember me
Don't have an account? Create your account in less than a minutes,
Forgot password? how can I recover my password now!
Enter right registered email to receive password!
Measures of Dispersion
Box 3: Food vs. Oil
Below are the figures for foodgrain procurement and crude oil production during 20x1-x2
Month
Foodgrain Procurement ('000 tonnes)
Crude Oil Production (lakh tonnes)
20x1
April
3,506
23.5
May
3,558
23.2
June
285
22.3
July
65
23.0
August
25
22.9
September
782
22.1
October
2,473
22.6
November
1,920
21.8
December
2,403
20x2
January
2,300
February
1,603
20.1
March
837
22.4
Total
19,757
269.1
The variability of the crude oil figures is much less than those of the foodgrain figures. This can be seen from the fact that all the crude oil production figures lie within 11% of the mean value of 22.4 lakh tonnes (i.e. between 19.9 lakh tonnes and 24.9 lakh tonnes). On the other hand only one foodgrain figure, i.e. February 20x2 procurement lies within 11% of the mean procurement value of 1646.4 thousand tonnes (between 1465.3 lakh tonnes and 1827.5 lakh tonnes). Hence the mean is a more representative value for the crude oil data as compared with the foodgrain data.
In the case of foodgrain procurement, the monthly figures vary from a low of 25 thousand tonnes in August 20x1 to a high of 3558 thousand tonnes in May 20x1. This is due to the seasonal nature of agriculture.
In the case of crude oil production, the monthly figures vary from a low of 20.1 lakh tonnes in February 20x2 to a high of 23.5 lakh tonnes in April 20x1.
The above vignette reveals that while a mean may give the central tendency of data, it does not reveal anything about the variability or dispersion of the data. For this another measure is needed.
The variability or dispersion of data is given by measures of dispersion.
When there is no dispersion, all the data points have identical values and the values of all the measures of central tendency converge.
If the test is two-tailed, H1: μ ≠ μ 0 then the test is called two-tailed test and in such a case the critical region lies in both the right and left tails of the sampling distr
The cornlnunalities h j represent the fraction of the total variance' 'accounted for of variabie j. Ry calculating the communalities we can keep track of how much of-the orig
Dr. Jim Mirabella UNIT EIGHT: DATA ANALYSIS PROJECT All Excel output should be copied into a single Word document where you must enter all of your responses to the questions below.
find the expected value of the mean square error and of the mean square reggression
We want to investigate the income data. In the Excel file Midterm Data.xls there is a tab labeled "Income Data 2006". The data in the tab is the income reported by 400 people in
To use Linear Programming for solving the following inequalities. Following Twin Conditions (as mandated by the Indian Regulatory Authority) Twin Condition I for TV Broadcasters
Show that when h = h* for the histogram, the contribution to AMISE of the IV and ISB terms is asymptotically in the ratio 2:1. Compare the sensitivity of the AMISE(ch) in Equa
Standard Deviation The concept of standard deviation was first introduced by Karl Pearson in 1893. The standard deviation is the most important and the popular measure of disp
A consumer preference study involving three different bottle designs (A, B, and C) for the jumbo size of a new liquid detergent was carried out using a randomized block experimenta
what the purpose we use it
Get guaranteed satisfaction & time on delivery in every assignment order you paid with us! We ensure premium quality solution document along with free turntin report!
whatsapp: +1-415-670-9521
Phone: +1-415-670-9521
Email: [email protected]
All rights reserved! Copyrights ©2019-2020 ExpertsMind IT Educational Pvt Ltd