Ogive (Cumulative Frequency Curve) There are two ways of constructing an ogive or cumulative frequency curve. (Ogive is pronounced as O-jive). The curve is usually of 'S' shape. We illustrate both methods by examples given below: Draw a 'less than' ogive curve for the following data: To Plot an Ogive: (i) We plot the points with coordinates having abscissae as actual limits and ordinates as the cumulative frequencies, (10, 2), (20, 10), (30, 22), (40, 40), (50, 68), (60, 90), (70, 96) and (80, 100) are the coordinates of the points. (ii) Join the points plotted by a smooth curve. (iii) An Ogive is connected to a point on the X-axis representing the actual lower limit of the first class. Scale: X -axis 1 cm = 10 marks, Y -axis 1cm = 10 c.f. Using the data given below, construct a 'more than' cumulative frequency table and draw the Ogive. To Plot an Ogive (i) We plot the points with coordinates having abscissae as actual lower limits and ordinates as the cumulative frequencies, (70.5, 2), (60.5, 7), (50.5, 13), (40.5, 23), (30.5, 37), (20.5, 49), (10.5, 57), (0.5, 60) are the coordinates of the points. (ii) Join the points by a smooth curve. (iii) An Ogive is connected to a point on the X-axis representing the actual upper limit of the last class [in this case) i.e., point (80.5, 0)]. Scale: X-axis 1 cm = 10 marks Y-axis 2 cm = 10 c.f To reconstruct frequency distribution from cumulative frequency distribution. When we write, 'less than 10 - less than 0', the difference give the frequency 4 for the class interval (0 - 10) and so on. When we write 'more than 0 - more than 10', the difference gives the frequency 4 for the class interval (0 - 10) and so on. Ogive (Cumulative Frequency Curve) There are two ways of constructing an ogive or cumulative frequency curve. (Ogive is pronounced as O-jive). The curve is usually of 'S' shape. We illustrate both methods by examples given below: Draw a 'less than' ogive curve for the following data: To Plot an Ogive: (i) We plot the points with coordinates having abscissae as actual limits and ordinates as the cumulative frequencies, (10, 2), (20, 10), (30, 22), (40, 40), (50, 68), (60, 90), (70, 96) and (80, 100) are the coordinates of the points. (ii) Join the points plotted by a smooth curve. (iii) An Ogive is connected to a point on the X-axis representing the actual lower limit of the first class. Scale: X -axis 1 cm = 10 marks, Y -axis 1cm = 10 c.f. Using the data given below, construct a 'more than' cumulative frequency table and draw the Ogive. To Plot an Ogive (i) We plot the points with coordinates having abscissae as actual lower limits and ordinates as the cumulative frequencies, (70.5, 2), (60.5, 7), (50.5, 13), (40.5, 23), (30.5, 37), (20.5, 49), (10.5, 57), (0.5, 60) are the coordinates of the points. (ii) Join the points by a smooth curve. (iii) An Ogive is connected to a point on the X-axis representing the actual upper limit of the last class [in this case) i.e., point (80.5, 0)]. Scale: X-axis 1 cm = 10 marks Y-axis 2 cm = 10 c.f To reconstruct frequency distribution from cumulative frequency distribution. When we write, 'less than 10 - less than 0', the difference give the frequency 4 for the class interval (0 - 10) and so on. When we write 'more than 0 - more than 10', the difference gives the frequency 4 for the class interval (0 - 10) and so on.
It is 3 more than the cumulative frequency up to the previous class or value.
It is not. It depends on what question you want to answer. They are both equally informative, but in different circumstances.the CRFD can be used to determine a summary of proportion of observations that lies above(or below) a particular value in a data set which the RFD cannot
The answer will depend on what you mean by "solve". Find the mean, median, mode, variance, standard error, standard deviation, quartiles, deciles, percentiles, cumulative distribution, goodness of fit to some distribution etc. The question needs to be a bit more specific than "solve".
The first is more commonly used and, in a usual graph, goes from bottom left to top right. The second goes from top left to bottom right. Both are equally valid.
Organizing the data into a frequency distribution may make patterns within the data more evident.
Simple frequency distribution is a method of organizing large data sets into more easily interpreted sets. An example is organizing sample test scores by the individual scores.
Hi im 15 n i am doing my maths coursework which requires me to make a few cumulative frequency curves. Basically all you do is add the frequency as you go along. for example if the frequencies were: 4 5 2 3 then the cumulative frequency would be 4 9 11 14 You would then use this by plotting it along the y axis. There is a little more but that's mainly what u need to know to get started.
The 2k or 2 to K rule is used to determine the number of classes for a frequency distribution. The 2k rule should be used as a guide more than a dictator of determining the number of classes for a frequency distribution.
Organizing the data into a frequency distribution can make patterns within the data more evident.
Information on frequency distribution can be found on many sites related to psychology and biotechnology. The About's Psychology page offers a good overview on the topic while the National Center for Biotechnology Information has a more in depth reading.
Finding the average from the raw data requires a lot more calculations. By using frequency distributions you reduce the number of calculations.
Yes. Often hundreds and sometimes (eg in a population Census), millions.