Definition of Statistics PDF

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These are terminologies in statistics...

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Definition of Statistics the practice or science of collecting and analyzing numerical data in large quantities, especially for the purpose of inferring proportions in a whole from those in a representative sample. Definition of Statistics: 1. Statistics can be defined as the collection presentation and interpretation of numerical data.- Croxton and Crowed. 2. Statistics are numerical statement of facts in any department of enquiry placed interrelation to each other.- Bouly. 3. Statistics are measurement, enumerations or estimates of natural or social phenomena systematically arrangement to exhibit their inner relation.- Conner. 4. By Statistics we mean quantitative data affected to a marked extend by a multiplicity of causes. – Youle and Kendal. 5. The science of Statistics is essentially a branch of applied mathematics and can be regarded as a mathematics applied to observation data.- R.A fisher Fields of Statistics Descriptive statistics deals with the presentation and collection of data. This is usually the first part of a statistical analysis. It is usually not as simple as it sounds, and the statistician needs to be aware of designing experiments, choosing the right focus group and avoid biases that are so easy to creep into the experiment. Different areas of study require different kinds of analysis using descriptive statistics. For example, a physicist studying turbulence in the laboratory needs the average quantities that vary over small intervals of time. The nature of this problem requires that physical quantities be averaged from a host of data collected through the experiment. Fields of Statistics The two main branches of statistics are descriptive statistics and inferential statistics. Both of these are employed in scientific analysis of data and both are equally important for the student of statistics. Descriptive Statistics Descriptive statistics deals with the presentation and collection of data. This is usually the first part of a statistical analysis. It is usually not as simple as it sounds, and the statistician needs to be aware of designing experiments, choosing the right focus group and avoid biases that are so easy to creep into the experiment. Inferential Statistics Inferential statistics, as the name suggests, involves drawing the right conclusions from the statistical analysis that has been performed using descriptive statistics. In the end, it is the inferences that make studies important and this aspect is dealt with in inferential statistics. population vs Sample The population includes all objects of interest whereas the sample is only a portion of the population.

The computation is the first part of the statistics course (Descriptive Statistics) and the estimation is the second part (Inferential Statistics) Scope of Statistics Scope and importance of Statistics: 1. Statistics and planning: Statistics in indispensable into planning in the modern age which is termed as “the age of planning”. Almost all over the world the govt. are re-storing to planning for economic development. 2. Statistics and economics: Statistical data and techniques of statistical analysis have to immensely useful involving economical problem. Such as wages, price, time series analysis, demand analysis. 3. Statistics and business: Statistics is an irresponsible tool of production control. Business executive are relying more and more on statistical techniques for studying the much and desire of the valued customers. 4. Statistics and industry: In industry statistics is widely used inequality control. In production engineering to find out whether the product is confirming to the specifications or not. Statistical tools, such as inspection plan, control chart etc. 5. Statistics and mathematics: Statistics are intimately related recent advancements in statistical technique are the outcome of wide applications of mathematics. 6. Statistics and modern science: In medical science the statistical tools for collection, presentation and analysis of observed facts relating to causes and incidence of dieses and the result of application various drugs and medicine are of great importance. 7. Statistics, psychology and education: In education and physiology statistics has found wide application such as, determining or to determine the reliability and validity to a test, factor analysis etc. 8. Statistics and war: In war the theory of decision function can be a great assistance to the military and personal to plan “maximum destruction with minimum effort.” Mathematical Preliminaries Constant

A quantity which can assume only one value is called a constant. It is usually denoted by the first letters of the alphabet, a,b,ca,b,c. For example: The value of π=227=3.14159…π=227=3.14159… and the value of e=2.71828…e=2.71828….

Variable A quantity which can vary from one individual or object to another is called a variable. It is usually denoted by the last letters of the alphabet, x,y,zx,y,z. For example: Heights and weights of students, income, temperature, number of children in a family, etc. A variable which can assume each and every value within a given range is called a continuous variable. It can occur in decimals. For example: Heights and weights of students, speed of a bus, the age of a shopkeeper, the life time of a T.V, etc. Continuous Data Data which can be described by a continuous variable are called continuous data. For example: Weights of 50 students in a class. Discrete Variable A variable which can assume only some specific values within a given range is called a discrete variable. It cannot occur in decimals; it can only occur in whole numbers. For example: Number of students in a class, number of flowers on a tree, number of houses on a street, number of chairs in a room, etc. Discrete Data Data which can be described by a discrete variable are called discrete data. For example: Number of students in a college. Quantitative Variable A characteristic which varies only in magnitude from one individual to another is called a quantitative variable. It can be measurable. For example: Wages, prices, heights, weights, etc. Qualitative Variable A characteristic which varies only in quality from one individual to another is called a qualitative variable. It cannot be measured. For example: Beauty, marital status, rich, poor, scent, etc.

Discrete vs Continuous Discrete variables are usually obtained by counting. There are a finite or countable number of choices available with discrete data. You can't have 2.63 people in the room. Continuous variables are usually obtained by measuring. Length, weight, and time are all examples of continous variables. Since continuous variables are real numbers, we usually round them. This implies a boundary depending on the number of decimal places. For example: 64 is really anything 63.5...