Ogive – The cumulative line graph

In statistics, a frequency chart displays the given data, in which the frequency of each data item is found.  What does frequency mean? Frequency as we use in case of the frequency of the flight from one place to another means the number of times the particular flight travels from one place to another.  In statistics, frequency is used to display the number of times the data item occurs in a data set.  Tally marks or tallies are used to record and show the frequency of an item in a data. Now, let us learn about cumulative frequency. It is the total of the frequency and all the frequencies below it in a frequency distribution.  In simple words, it is the running total of frequencies. Given frequency of a set of data, the Ogive chart looks something similar to the chart given below:

Age Frequency      cumulative frequency
8      4 4
9      6 4+6 =10
10    15 10+15=25
11     9 25+9=34
12    18 34+18 = 52
13    10 52+10 =62

The Ogive Definition can be given as a distribution curve in which the frequencies are cumulative
Now that we have the cumulative frequencies, we shall now plot the graph. To plot the graph we take the ages on the x-axis and the cumulative frequencies on the y-axis as we plot a normal line graph. Once all the points are plotted, we now join the points. The curve we get is the cumulative frequency curve, also called the Ogive

We can define Ogive as a cumulative frequency graph which is a curve or graph showing the cumulative frequency for a given set of data. When the given data is an un-grouped data, to get Ogive, we find the cumulative frequency of the data and plot that on the y-axis and the given data to which cumulative frequency is calculated is taken on the x-axis. The graph we get is the Ogive of ungrouped data. When the data given is a grouped data, we divide the group into classes with upper and lower boundary which is taken on the x-axis and the cumulative frequency of the data on the y-axis. The graph we get here is the Ogive of a grouped data.

Ogive Example
For example, let us assume the amount of savings for the months of January and March as $200 and the savings of $125 for the months February, April and May.  For the given data, the ogive displays a running total of the savings with the amount saved in dollars on the y-axis and the months on the x-axis.

Trigonometric Identities | Theorems Based on Trigonometric Identities

Trigonometric Identies are some identies used in Trigonometry in order to make the calculations easier.
Trigonometry is a word consisting of three Greek words " Tri" means three, "Gon" means side, and "Metron" means measure. Thus, trigonometry is a study related to the measures of sides and angles of a triangle. Trigonometry is mainly used by captains of ships to find the direction and distance of islands and light houses from sea. Trigonometry is also used in astronomy, geography and engineering.
Trigonometric Ratios
In any right-angled triangle ABC,
let  angle B = 90 o  and angle C = T.                                                      

Line segment AC is the hypotenuse.
With reference to angle C, we can say that,
Line segment AB is the opposite side of Line segment BC is the adjacent side of Therefore, trigonometric ratios are given as,


Trigonometric Identities
Basic trigonometric identities are:
sin^2 T + cos^2 T = 1.
tan^2 T + 1 = sec^2 T
1 + cot 2T  = cosec^2 T
Theorems Based on Trigonometric Identities

Theorem 1: sin^2 T + cos^2 T =1
In right-angled triangle ABC, let angle< B = 90, angle< C = T.
Let AB = a, BC = b, and AC = c
By Pythagorean theorem we can say,
(hypotenuse)^2 = ( side)^2 + (side)^2
From figure we can say,
(AC)^2 =  (AB)^2 + (BC)^2
c^2 =  a^2 + b^2
divide throughout by c^2, we get,
(c^2 ) / c^2 =  ( a^2 + b^2 ) / c^2
1  =  a^2 / c^2 + b^2 / c^2
=  ( AB )^2 / ( AC)^2 + ( BC)^2 / (AC)^2
=   (AB / AC)^2 + ( BC / AC)^2
=   ( sin T )^2 +  ( cos T )^2
Therefore,
1 = sin^2 T + cos^2 T

Theorem 2: tan2 T + 1 = sec2 T
We have sin^2 T + cos^2 T = 1
Divide on both sides by cos^2 T,
( sin^2 T + cos^2 T ) / cos^2 T  =  1 / cos^2 T
(sin^2 T / cos^2 T) + (cos^2 T / cos^2 T)  =  1 / cos^2 T
By using trigonometric ratios,
sin T/ cos T  =  tan T
1 / cos T  =  sec T
substitute the values we get,
( sin T / cos T )^2 + 1  =  ( 1 / cos T)^2
(tan T)^2 + 1  =  ( sec T )^2
tan^2 T + 1  =  sec^2 T
Theorem 3: 1 + cot2 T  = cosec^2 T
We have sin^2 T + cos^2 T = 1
Divide on both sides by sin^2 T,
( sin^2 T + cos^2 T ) / sin^2 T  =  1 / sin^2 T
(sin^2 T / sin^2 T) + (cos^2 T / sin^2 T)  =  1 / sin^2 T
By using trigonometric ratios,
cos T/ sin T  =  cot T
1 / sin T  =  cosec T
substitute the values we get,
1 +  ( cos T / sin T )^2   =  ( 1 / sin T)^2
1 +  (cot T)^2  =  ( cosec T )^2
1 +  cot 2T  =  cosec^2 T

Standard Deviation of Mean in a nutshell

Standard deviation of Mean is the measure of the spread of the data about the mean value. If the standard deviation is low it shows that the values of the data are not spread out much and if the standard deviation is high it shows that the values of the data are spread out. At times we come across data which has the same mean but different range; to compare the sets of data standard deviation is very useful.  The average squared deviation from the mean is called the Variance. The square root of variance is the Standard Deviation of Mean. It is a statistical measure to know how the data is spread in the distribution, in simple words statistical measure of dispersion. Standard Deviation Means is also called the Mean of the Means.

In a population Variance is given by the formula: sigma^2 =summation[x – mu]^2/n
Where, x is each value in the data, mu is the mean of the data, n is the total number of values in the data.  Usually variance is estimated from a sample in a population. Variance calculated from a sample is given by the formula: sigma^2 = summation[x – x bar] ^2/ (n-1), here, x is each value from the sample, x bar is the mean of the values in the sample; n-1 is one less than the total number of values in the sample.  One Standard Deviation of the Mean is given by sigma= sqrt [summation[x – x bar] ^2/ (n-1)]

Standard Deviation of the Mean Equation
The equation or the formula to be used to calculate the standard deviation depends on whether the data is grouped or non-grouped. For example, given data, 42, 35, 48, 53, 47 is a non-grouped data.

In such a case, the standard deviation of the mean is calculated using the equation:
sigma = sqrt [summation (x- x bar) ^2/ (n-1)] where sigma is the standard deviation, (x-x bar) ^2 is the square of the deviations of the data values and n is the total number of values. Let us consider the data given below
Hours of components Frequency
300-400                   13
400-500                   25
500-600                   66
600-700                            58
700-800                   38
Understanding statistics problems is always challenging for me but thanks to all math help websites to help me out.
The data is a grouped data, here the standard deviation of the mean is estimated using the equation given by, sigma = sqrt [summation f(x-x bar) ^2/summation (f)] where sigma is the standard deviation, f is the frequency, x is each value of the data, x bar is the mean of the data values, summation is the sum of.