Business Statistics 3rd Semester PDF

Business Statistics 3rd Semester PDF Download

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Business Statistics 3rd Semester PDF Summary

Dear readers, today we are going to share the Business Statistics 3rd Semester PDF Download for all of you. The word “Statistics” has been derived from the Latin word “Status” or the Italian word “Statista” or the German word “Statistika”. Each of these words means Political State.

Initially, Statistics was used to collect information about the people of the state about their income, health, illiteracy and wealth etc. But now a day, Statistics has become an important subject having useful applications in various
fields in day-to-day life.

Here in this article, we have provided Business Statistics 3rd Semester in pdf format for those students who want to prepare for examinations and also want to good marks in the upcoming exam. So friends if you are one of them then read this article properly. The given information prove will be very knowledgeable for you.

Business Statistics 3rd Semester PDF


  • 1.1 Introduction
  • 1.2 Meaning and Definitions of Statistics
  • 1.3 Types of Data and Data Sources
  • 1.4 Types of Statistics
  • 1.5 Scope of Statistics

1.1 Introduction

For a layman, ‘Statistics’ means numerical information expressed in quantitative terms. This information may relate to objects, subjects, activities, phenomena, or regions of space. As a matter of fact, data have no limits as to their reference, coverage, and scope. At the macro level, these are data on gross national product and shares of agriculture, manufacturing, and services in GDP (Gross Domestic Product).

At the micro level, individual firms, howsoever small or large, produce extensive statistics on their operations. The annual reports of companies contain a variety of data on sales, production, expenditure, inventories, capital employed, and other activities.

These data are often field data, collected by employing scientific survey techniques. Unless regularly updated, such data are the product of a one-time effort and have limited use beyond the situation that may have called for their collection. A student knows statistics more intimately as a subject of studies like economics, mathematics, chemistry, physics, and others.

It is a discipline, which scientifically deals with data, and is often described as the science of data. In dealing with statistics as data, statistics has developed appropriate methods of collecting, presenting, summarizing, and analysing data, and thus consists of a body of these methods.

1.2 Meaning And Definitions Of Statistics

In the beginning, it may be noted that the word ‘statistics’ is used rather curiously in two senses plural and singular. In the plural sense, it refers to a set of figures or data. In the singular sense, statistics refer to the whole body of tools that are used to collect data, organise and interpret them and, finally, draw conclusions from them.

It should be noted that both aspects of statistics are important if the quantitative data are to serve their purpose. If statistics, as a subject, is inadequate and consists of poor methodology, we could not know the right procedure to extract from the data the information they contain. Similarly, if our data are defective or that they are inadequate or inaccurate, we could not reach the right conclusions even though our subject is well-developed.

A.L. Bowley has defined statistics as (i) statistics is the science of counting, (ii) Statistics may rightly be called the science of averages, and (iii) statistics is the science of measurement of social organisms regarded as a whole in all its manifestations. Boddington defined: Statistics as the science of estimates and probabilities.

Further, W.I. King has defined Statistics in a wider context, the science of Statistics is the method of judging collective, natural or social phenomena from the results obtained by the analysis or enumeration or collection of estimates. Seligman explored that statistics is a science that deals with the methods of collecting, classifying, presenting, comparing and interpreting numerical data collected to throw some light on any sphere of enquiry.

Spiegal defines statistics highlighting its role in decision-making particularly under uncertainty, as follows: statistics is concerned with a scientific method for collecting, organising, summarising, presenting and analyzing data as well as drawing valid conclusions and making reasonable decisions on the basis of such analysis. According to Prof.

Horace Secrist, Statistics is the aggregate of facts, affected to a marked extent by a multiplicity of causes, numerically
expressed, enumerated or estimated according to reasonable standards of accuracy, collected in a systematic manner for a pre-determined purpose, and placed in relation to each other.

From the above definitions, we can highlight the major characteristics of statistics as follows:

  • (i) Statistics are the aggregates of facts. It means a single figure is not statistics. For example, the national income of a country for a single year is not statistics but the same for two or more years is statistics.
  • (ii) Statistics are affected by a number of factors. For example, the sale of a product depends on a number of factors such as its price, quality, competition, the income of the consumers, and so on.
  • (iii) Statistics must be reasonably accurate. Wrong figures, if analysed, will lead to erroneous conclusions. Hence, it is necessary that conclusions must be based on accurate figures.
  • (iv) Statistics must be collected in a systematic manner. If data are collected in a haphazard manner, they will not be reliable and will lead to misleading conclusions.
  • (v) Collected in a systematic manner for a pre-determined purpose
  • (vi) Lastly, Statistics should be placed in relation to each other. If one collects data unrelated to each other, then such data will be confusing and will not lead to any logical conclusions. Data should be comparable over time and over space.

1.3 Types Of Data And Data Sources

Statistical data are the basic raw material of statistics. Data may relate to an activity of our interest, a phenomenon, or a problem situation under study. They derive as a result of the process of measuring, counting and/or observing. Statistical data, therefore, refers to those aspects of a problem situation that can be measured, quantified, counted, or classified.

Any object subject phenomenon or activity that generates data through this process is termed a variable. In other words, a variable is one that shows a degree of variability when successive measurements are recorded. In statistics, data are classified into two broad categories: quantitative data and qualitative data.

This classification is based on the kind of characteristics that are measured. Quantitative data are those that can be quantified in definite units of measurement. These refer to characteristics whose successive measurements yield quantifiable observations.

Depending on the nature of the variable observed for measurement, quantitative data can be further categorized as continuous and discrete data. Obviously, a variable may be a continuous variable or a discrete variable.

(i) Continuous data represent the numerical values of a continuous variable. A continuous variable is one that can assume any value between any two points on a line segment, thus representing an interval of values. The values are quite precise and close to each other, yet distinguishably different.

All characteristics such as weight, length, height, thickness, velocity, temperature, tensile strength, etc., represent continuous variables. Thus, the data recorded on these and similar other characteristics are called continuous data. It may be noted that a continuous variable assumes the finest unit of measurement. Finest in the sense that it enables measurements to the maximum degree of precision.

(ii) Discrete data are the values assumed by a discrete variable. A discrete variable is one whose outcomes are measured in fixed numbers. Such data essentially count data. These are derived from a process of counting, such
as the number of items possessing or not possessing a certain characteristic.

The number of customers visiting a departmental store every day, the incoming flights at an airport, and the defective items in a consignment received for sale, are all examples of discrete data.

Qualitative data refer to the qualitative characteristics of a subject or an object. A characteristic is qualitative in nature when its observations are defined and noted in terms of the presence or absence of a certain attribute in discrete numbers. These data are further classified as nominal and rank data.

(i) Nominal data are the outcome of classification into two or more categories of items or units comprising a sample or a population according to some quality characteristic. Classification of students according to sex (as males and
females), workers according to skill (as skilled, semi-skilled, and unskilled), and employees according to the level of education (as matriculates, undergraduates, and post-graduates), all result in nominal data.

Given any such basis of classification, it is always possible to assign each item to a particular class and make a summation of items belonging to each class. The count data so obtained are called nominal data.

(ii) Rank data, on the other hand, are the result of assigning ranks to specify the order in terms of the integers 1,2,3, …, n. Ranks may be assigned according to the level of performance in a test. a contest, a competition, an interview, or a show. The candidates appearing in an interview, for example, may be assigned ranks in integers ranging from I to n, depending on their performance in the interview.

Ranks so assigned can be viewed as the continuous values of a variable involving performance as the quality characteristic.

Data sources could be seen as of two types, viz., secondary and primary. The two can be defined as:

(i) Secondary data: They already exist in some form: published or unpublished – in an identifiable secondary source. They are, generally, available from a published source(s), though not necessarily in the form actually required.

(ii) Primary data: Those data which do not already exist in any form, and thus have to be collected for the first time from the primary source(s). By their very nature, these data require fresh and first-time collection covering the whole
population or a sample drawn from it.

1.4 Types Of Statistics

There are two major divisions of statistics such as descriptive statistics and inferential statistics. The term descriptive statistics deals with collecting, summarizing, and simplifying data, which are otherwise quite unwieldy and voluminous. It seeks to achieve this in a manner that meaningful conclusions can be readily drawn from the data.

Descriptive statistics may thus be seen as comprising methods of bringing out and highlighting the latent characteristics present in a set of numerical data. It not only facilitates an understanding of the data and systematic reporting thereof in a manner; and also makes them amenable to further discussion, analysis, and interpretations.

The first step in any scientific inquiry is to collect data relevant to the problem at hand. When the inquiry relates to physical and/or biological sciences, data collection is normally an integral part of the experiment itself. In fact, the very manner in which an experiment is designed determines the kind of data it would require and/or generate.

The problem of identifying the nature and the kind of relevant data is thus automatically resolved as soon as the design of the experiment is finalized. It is possible in the case of physical sciences. In the case of social sciences, where the required data are often collected through a questionnaire from a number of carefully selected respondents, the problem is not that simply resolved.

For one thing, designing the questionnaire itself is a critical initial problem. For another, the number of respondents to be accessed for data collection and the criteria for selecting them has their own implications and importance for the quality of results obtained. Further, the data that have been collected, are assembled, organized, and presented in the form of appropriate tables to make them readable.

Wherever needed, figures, diagrams, charts, and graphs are also used for a better presentation of the data. A useful tabular and graphic presentation of data will require that the raw data be properly classified in accordance with the objectives of the investigation and the relational analysis to be carried out.

A well-thought-out and sharp data classification facilitate an easy description of the hidden data characteristics by means of a variety of summary measures. These include measures of central tendency, dispersion, skewness, and kurtosis, which constitute the essential scope of descriptive statistics. These form a large part of the subject matter of any basic textbook on the subject, and thus they are being discussed in that order here as well.

Inferential statistics, also known as inductive statistics, goes beyond describing a given problem situation by means of collecting, summarizing, and meaningfully presenting the related data. Instead, it consists of methods that are used for drawing inferences or making broad generalizations, about a totality of observations on the basis of knowledge about a part of that totality.

The totality of observations about which an inference may be drawn, or a generalization made, is called a population or a universe. The part of the totality, which is observed for data collection and analysis to gain knowledge about the population, is called a sample.

The desired information about a given population of our interest; may also be collected even by observing all the units comprising the population. This total coverage is called a census. Getting the desired value for the population through the census is not always feasible and practical for various reasons.

Apart from time and money considerations making the census operations prohibitive, observing each individual unit of the population with reference to any data characteristic may at times involve even destructive testing. In such cases, obviously, the only recourse available is to employ the partial or incomplete information gathered through a sample for the purpose. This is precisely what inferential statistics do.

Thus, obtaining a particular value from the sample information and using it for drawing an inference about the
entire population underlies the subject matter of inferential statistics. Consider the situation in which one is required to know the average body weight of all the college students in a given cosmopolitan city during a certain year.

A quick and easy way to do this is to record the weight of only 500 students, from out of a total strength of, say, 10000, or an unknown total strength, take the average, and use this average based on incomplete weight data to represent the average body weight of all the college students.

In a different situation, one may have to repeat this exercise for some future year and use the quick estimate of average body weight for comparison. This may be needed, for example, to decide whether the weight of college students has undergone a significant change over the years compared.

Inferential statistics helps to evaluate the risks involved in reaching inferences or generalizations about an unknown population on the basis of sample information. for example, an inspection of a sample of five battery cells drawn from a given lot may reveal that all five cells are in perfectly good condition. This information may be used to conclude whether the entire lot is good enough to buy or not.

Since this inference is based on the examination of a sample of a limited number of cells, it is equally likely that all the cells in the lot are not in order. It is also possible that all the items that may be included in the sample are unsatisfactory. This may be used to conclude that the entire lot is of unsatisfactory quality, whereas the fact may
indeed be otherwise.

It may, thus, be noticed that there is always a risk of inference about a population being incorrect when based on the knowledge of a limited sample. The rescue in such situations lies in evaluating such risks. For this, statistics provide
the necessary methods. These centres on quantifying in probabilistic terms the chances of decisions taken on the basis of sample information being incorrect.

This requires an understanding of the what, why, and how of probability and probability distributions to equip ourselves with methods of drawing statistical inferences and estimating the degree of reliability of these inferences

Business Statistics 3rd Semester PDF 2022


According to Bowley – “Statistics are numerical statements of facts in any department of enquiry placed in relation to each other.”

According to Yule and Kendall – “By Statistics, we mean quantitative data affected to a marked extent by a multiplicity of causes.”

Business Statistics 3rd Semester Syllabus PDF


INTRODUCTION Origin and Development of Statistics – Definition – Importance and Scope – Limitations of Statistics – Distrust of Statistics. Statistical Investigation: Planning of Statistical Investigation – Census and Sampling Methods – Collection of Primary and Secondary Data – Statistical Errors and Approximation – Classification and Tabulation of Data – Frequency Distribution


DIAGRAMMATIC AND GRAPHIC PRESENTATION Diagrammatic Presentation: One-Dimensional and Two-Dimensional Diagrams – Pictograms – Cartograms – Graphic Presentation: Technique of Construction of Graphs – Graphs of Frequency Distribution – Graphs of Time Series or Histograms


MEASURES OF CENTRAL TENDENCY Introduction -Significance – Arithmetic Mean – Geometric Mean – Harmonic Mean – Mode – Median – Quartiles and Percentiles – Simple and Weighted Averages – Uses and Limitations of different Averages


MEASURES OF DISPERSION, SKEWNESS AND KURTOSIS Measures of Dispersion: Significance – Characteristics – Absolute and Relative Measures – Range – Quartile Deviation – Mean Deviation- Standard Deviation – Coefficient of Variation Measures of Skewness – Karl Pearson‘s Coefficient of Skewness – Bowley‘s Coefficient of Skewness – Kelly‘s Measure of Skewness – Kurtosis: Mesokurtosis, Platy kurtosis and Leptokurtosis


CORRELATION Meaning -Types – Correlation and Causation – Methods: Scatter Diagram – Karl Person’s Coefficient of Correlation – Probable Error and Interpretation of Coefficient of Correlation – Rank Correlation – Concurrent Deviation Method

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