The History of Statistics

Origin of The Word
The word statistik comes from Italian word statista (meaning "statesman). It was first used by Gottfried Achenwall (1719-1772), a professor at Marlborough and Gottingen. Dr. E. A. W. Zimmerman introduced the word statistics into England. Its use was popularized by Sir John Sinclair in his work Statistical Account of Scotland 1791-1799. Long before the eighteenth century, people had been recording and using data.

Early Government Records
Official government statistics are as old as recorded history. The Old Testament contain several accounts of census taking. Governments of ancient Babylonia, Egypt, and Rome gathered detailed records of populations and resources. In the Middle Ages, governments began to register the ownership of land. In A.D. 762, Charlemagne asked for detailed descriptions of church-owned properties. Early in the ninth century, he completed a statistical enumeration of the serfs attached to the land. About 1086. William the Conqueror ordered the writing of Domesday Book, a record of the ownership, extent and value of the lands of England. This work was England's first statistical abstract.

An early prediction from statistics
Because of Henry VII's fear of the plague, England began to register its dead in 1532. About this same time, French law required the clergy to register baptisms, deaths and marriages. During an outbreak of the plague in the late 1500s, the English government started publishing weekly death statistics. This practice continued, and by 1632, these Bills of Mortality listed births and death by sex. In 1662, Captain John Graunt used 30 years of  these Bills to make predictions about the number of people who would die from various diseases and the proportions of male and female births that could be expected. Summarized in his work Natural and Political Observations... Made Upon the Bills of Mortality, Graunt's study was a pioneer effort in statistical analysis. For his achievement in using past records to predict future events, Graunt was made a member of the original Royal Society.
The history of the  .... (to be continued) :)
 

Simple Linear Regression

Regression analysis is a very valuable tool for today's manager. Regression has been used to model such things as the relationship between level of education and income, the price of a house and the square footage, and the sales volume for a company relative to the dollars spent on advertising. When business are trying to decide which location is best for a new store or branch office, regression models are often used. Cost estimation models are often regression models. The applicability of regression analysis virtually limitless.
There are generally two purposes for regression analysis. The first is to understand the relationship between variables such as advertising expenditures and sales. The second purposes is to predict the value of one variable based on the value of the other.
The simple linear regression model will first be developed, and then a more complex multiple regression model will be used to incorporate even more variables into our model. In any regression model, the variable to be predicted is called the dependent variable or response variable. The value of this is said to be dependent upon the value of an independent variable, which is sometimes called an explanatory variable or a predictor variable.
In any regression model, there is an implicit assumption (which can be tested) that a relationship exist between the variables. There is also some random error that cannot be predicted. The underlying simple linear regression model is:
Y = b0 + b1X + error

where:
Y = dependent variable (response variable)
X = independent variable (predictor variable or explanatory variable)
bo = intercept (value of Y when X = 0)
b1 = slope of regression line
error = random error
Download statistics handout of Simple Linear Regression, here. Full version and it's free.
Distance Learning Business and Management on Statistics object. 

Simple Linier Regression

A. Introduction
Regression is a measuring instrument which can also be used to measure whether there is any correlation. If we have two or more variables It is only fitting if we want to study how these variables are related or can be foreseen.

Regression analysis to investigate the relationship obtained is expressed in mathematical equations that express the functional relationship between the variables. The functional relationship between one predictor variable with a single criterion variable is called a simple regression analysis (single), while the functional relationship that is more than one variable is called multiple regression analysis.

The term regression (forecast / estimate) was first introduced by Sir Francis Galton in 1877 in connection with research on human height, namely the high child and high between her parents. In his research Galton found that children of parents high high tend to increase or decrease from the average population weight. Line showing the relationship is called the regression line.

Regression analysis was more accurate in analyzing the correlation, because the analysis was the difficulty in showing Slop (rate of change of a variable against another can be determined). Thus, through regression analysis, forecasting the value of the dependent variable in a more accurate value of the independent variables as well.

B. Linear Regression equation of Y on X
Linear regression equation of Y on X defined as follows:

Y = a + b X

Description:

Y = dependent variable
X = independent variable
a = intercept
b = regression coefficient / Slop

In the above equation, the value of a and b can be determined in the following manner:




Examples of simple regression exercise

Below are the data and work experience of eight sales turnover of marketing at Bang Toyib company

Question:

1. 1. Determine the value of a and b!
   2. Make a regression line equation!
   3. What is the estimated sales turnover of a marketing that have 3.5 years work experience?

Completion:

 

Answered:

1. value a = 3.25 and b = 1.25
2. The linear regression equation is

Y = a + bX
   = 3.25 + 1.25 X

1. Predictive value of Y, if X = 3.5

Y = a + bX
  = 3.25 + 1.25 X
= 3.25 + 1.25 (3.5)
= 7.625
Thus "Learning and Sharing" us this time .. Thanks

The Normal Distribution

I know of scarcely anything so apt to impress the imagination as the wonderful form of cosmic order expressed by the "Law of Frequency of Error".
The Law would have been personified by the Greeks and deified, if they had known of it. It reigns with serenity and in complete self-effacement amidst the wildest confusion. The huger the mob and the greater the apparent anarchy, the more perfect is its sway. It is the supreme law of Unreasoned.
Whenever a large sample of chaotic elements are taken in hand and marshaled in the order of their magnitude, and unsuspected and most beautiful form of regularity proves to have been latent all along.
The tops of the marshaled row form a flowing curve of invariable proportions; and each element, as it is sorted into place, finds, as it were, a preordained niche accurately adapted to fit it.
If the measurement at any two specified Grades in the row are known, those that will be found at every other Grade, except towards the extreme ends, can be predicted in the way already explained and with much precision.


Sir Francis Galton on Lapin, Statistics Meaning and Method

Statistics College Tutoring

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