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• Economics /

Class 11 Collection, Organisation and Presentation of Data

• Updated on  
• Jun 22, 2023

The collection of data aims to collect evidence for attaining a sound and comprehensible solution to a problem. To understand the inconsistencies in the output, we need the ‘data’ on the generation. It is a process which is conducted to measure and gather information. ‘Data’ is a device, which aids in the comprehension of problems by providing knowledge. Here is this blog, we will talk in detail about the Class 11 collection, organisation and presentation of data. 

Must Read: Business Services Class 11 Notes

This Blog Includes:

What are the sources of data, primary data, secondary data, preparation of instrument, mode of data collection, personal interviews, mailing questionnaire, telephone interviews, pilot survey, census and sample surveys, census , random sampling, non-random sampling, sampling errors, non-sampling errors, census of india and nsso.

To understand more about the chapter Class 11 collection, organisation and presentation of data, we fist need to know the sources of data. Statistical data can be obtained from two sources:

• Primary data

We further move on to the concept of primary data in class 11 collection, organisation and presentation of data. The important points of primary data are:

• The enumerator (person who assembles the data) may collect the data by administering an inquiry or research. Such data is called Primary Data , as it is formulated on first-hand information.
• Primary data are unique, do not require any modification, and are costly.

Next important form of data in class 11 collection, organisation and presentation of data is secondary data.

• If the data have been examined and analyzed by another agency, they are called Secondary Data . Usually, the issued data are secondary.
• They are already in the presence and therefore are not unique.
• It demands to be modified to satisfy the aim of the study at hand.
• Secondary data are low priced.

How do we collect Data?

Collection of data is important in class 11 collection, organisation and presentation of data. It is done by the following ways:

• The survey aims to describe characteristics like cost, worth, utility (in case of the product) and reputation, honesty, loyalty (in case of the nominee).
• The objective of the survey is to gather data and is a method of gathering information from individuals.

The most prevalent type of tool employed in surveys is a questionnaire/ interview schedule. The questionnaire is either self-directed by the interviewee or conducted by the enumerator or qualified investigator. While drawing-up the questionnaire/interview schedule, the following points should be kept in mind:

• The questionnaire should not be lengthy.
• The array of problems should move from indefinite to distinct.
• Questions should not be enigmatic.
• Questions should not use binary negatives. 
• Questions should not be leading.
• Questions should not indicate choices. 

Also Read: Emerging Modes of Business Class 11 Notes

The next important topic in class 11 collection, organisation and presentation of data is the mode of data collection. The aim of probing questions is to survey the acquisition of data. There are three ways of collecting data: 

• Mailing (questionnaire) Surveys

Personal interviews form an important part of the mode of data collection in class 11 collection, organisation and presentation of data. In this method, the researcher has the main role as he/she conducts the interviews face-to-face with the respondents. Personal interviews are preferred due to various reasons:

• Highest Response Rate 
• Allows use of all types of questions 
• Better for using open-ended questions 
• Allows clarification of ambiguous questions.

The personal interview has some demerits too:

• Most expensive 
• Possibility of influencing respondents 
• More time taking

Another important part of class 11 collection, organisation and presentation of data is the mailing questionnaire. In such a method, the data is collected through the mail. The questionnaire is mailed to each person and a  request is attached to complete and return it on time. 

The advantages of this method are:

• Least expensive 
• The only method to reach remote areas 
• No influence on respondents 
• Maintains anonymity of respondents 
• Best for sensitive questions

The disadvantages of mail surveys are:

• Cannot be used by illiterates 
• Long r esponse time  
• Does not allow an explanation of unambiguous questions  
• Reactions cannot be watched 

In telephone interviews, the investigator asks questions over the telephone. 

The advantages of telephone interviews are:

• Relatively low cost 
• Relatively less influence on respondents 
• Relatively high response rate.

The disadvantages of this method are:

• Limited use 
• Possibility of influencing respondents

Explore: Accountancy Class 11 NCERT Solutions

The pilot survey is another important tool in class 11 collection, organisation and presentation of data.

• After the questionnaire is ready, it is desirable to carry a try-out with a diminutive group, known as Pilot Survey or Pre-Testing of the questionnaire . 
• The pilot survey serves to give a preliminary impression of the survey. 
• It helps to pretest the questionnaire and know the lapses and drawbacks.
• It also aids to assess the appropriateness of questions, the accuracy of guidance, the administration of enumerators, and the expense and time required in the actual survey.

Census and sample surveys are an important tool in class 11 collection, organisation and presentation of data. 

• A survey, which encompasses every component of the population, is apprehended as Census or the Method of Complete Enumeration.
• The primary feature of this approach is that this comprises every individual unit in the whole population.

Sample Survey

• A sample refers to a section of the population from which information has to be taken. A good sample (representative sample) is usually short and competent in giving reasonably accurate information about the population at a lower cost and in less time.
• Most of the surveys are sample surveys and are preferable in statistics because of several reasons.
• A sample can give rationally secure and authentic information at a lower cost and in less time. 
• Now the question is how do you do the sampling? There are two main types of sampling:
• Non-random Sampling
• It is also known as the lottery method.
• Random sampling is where the specific units from the population (samples) are randomly selected. 
• In random sampling, each person has an equal possibility of being chosen, and the person who is selected is the same as the one who is not selected.
• Random number tables are generated to ensure an equal chance of selection of every single unit in the population.
• They are accessible either in an issued form or can be generated by employing relevant software packages.
• In this method, units of the population don’t have equal chances of being selected. 
• The convenience or interpretation of the investigator plays a crucial role in the adoption of the sample. 
• They are chiefly selected based on belief, purpose, ease, or quota and are non-random samples.

Sampling and Non-sampling Errors

While conducting surveys, in class 11 collection, organisation and presentation of data, sample and non-sampling errors find an important mention. 

• Sampling error applies to the variations between the sample estimate and the actual value.
• It is the error that transpires when you observe the sample taken from the population. 
• The point of differentiation between the actual parameter of the population and its estimate is known as sampling error. 

Non-sampling errors are more consequential than sampling errors. Sampling error can be minimized by taking a larger sample, on the other hand, it is difficult to minimize non-sampling error. Even a Census can carry non-sampling errors.

 Some of the non-sampling errors are:

• Errors in Data Acquisition: This type of error stems from recording inaccurate responses.
• Non-Response Errors: Non-response happens if an interviewer is incapable to contact a person listed in the sample or a person from the sample declined to respond. In this case, the sample research may not be representative.
• Sampling Bias: Sampling bias happens when the sampling plan is such that some portion of the target population could not possibly be incorporated into the sample.

Must Read: Class 11 Oscillations Notes

The census of India is a very important body of our country and is an important part in the chapter class 11 collection, organisation and presentation of data. 

• The Census of India and the National Sample Survey Organisation (NSSO), are two significant firms at the national level, which gather, manner, and tabulate data.
• The Census of India produces the most comprehensive and continuous demographic record of the population. 
• The NSSO was established by the Government of India to conduct nationwide surveys on socio-economic issues. 
• NSSO gives periodic measures of education, school enrolment, utilization of educational aids, employment, unemployment, manufacturing, and service sector enterprises, morbidity, maternity, child care, utilization of the public distribution system, etc.

Ans. Three methods exist for gathering data: Personal meetings. Telephonic Interviews, and mailing surveys with questions.

Ans. The term “presentation of data” refers to the display of data in a way that makes it easy for viewers to understand and examine it.

Ans. Based on the methods used to acquire them, data can be divided into four basic categories: observational, experimental, simulational, and generated. The kind of research data you gather may have an impact on how you manage that data.

Also Read: Class 11 Formation of a Company

We hope the Class 11- Collection of Data notes helped you understand the essential concepts covered in this chapter. Still unsure about which stream to choose after Class 12. Our Leverage Edu experts are here to guide you in selecting the right stream of study to make sure that you make an informed decision. Sign up for a free session with us now!

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Data Collection and Presentation

• First Online: 01 January 2012

Cite this chapter

• Cheng-Few Lee 4 ,
• John C. Lee 5 &
• Alice C. Lee 6  

8687 Accesses

The collection, organization, and presentation of data are basic background material for learning descriptive and inferential statistics and their applications. In this chapter, we first discuss sources of data and methods of collecting them. Then we explore in detail the presentation of data in tables and graphs. Finally, we use both accounting and financial data to show how the statistical techniques discussed in this chapter can be used to analyze the financial condition of a firm and to analyze the recent deterioration of the financial health of the US banking industry. In addition, we use a pie chart to examine how Congress voted on the Gulf Resolution in 1991.

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T-bill rate data can be found in Table  2.1 ; rates of return on the S&P 500 can be found in Table  2.4 in Appendix 2 of this chapter. Most of the figures in this book are drawn with the Microsoft Excel PC program. The procedure for using the Excel program to draw these graphs can be found in Appendix 1 of this chapter.

Both the DJIA and the S&P 500 will be discussed in Chap. 19 of this book.

Rates of return for Johnson & Johnson and Merck and market rates of return are analyzed in more detail in Appendix 2 .

Eleven times can be calculated as \( \frac{1.0-.778 }{1.0-.98 }=.111 \) .

Author information

Authors and affiliations.

Department of Finance and Economics, Rutgers University Business School, Piscataway, New Jersey, USA

Cheng-Few Lee

Center for PBBEF Research, Morrisplains, New Jersey, USA

John C. Lee

State Street Corporation, Boston, Massachusetts, USA

Alice C. Lee

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Appendix 1: Using Microsoft Excel to Draw Graphs

This appendix explains how to use Microsoft Excel to draw graphs. Eight steps are involved: entering Microsoft Excel, preparing the data, and drawing the graph(s):

Stage 1: Start Excel and enter data for Johnson & Johnson and Merck as shown in Fig.  2.9 .

Rates of return for JNJ and MRK

Stage 2: Select the data to be graphed as shown in Fig.  2.10 .

Data to be graphed

Stage 3: From the Insert Menu, choose Line on charts option as shown in Fig.  2.11 to get the 2-D line chart as shown in Fig.  2.12 .

Select graph option

Stage 4: Choose the Select Data on Chart Tools as shown in Fig.  2.12 .

Step 1 of Chart Tools

Stage 5: Choose Year and press Remove as shown in Fig.  2.13 . Then press Edit to select axis labels as shown in Fig.  2.14 .

Step 2 of Chart Tools

Stage 6: Select Cell A2 to Cell A22 in Axis label range as shown in Fig.  2.14 and press OK.

Step 3 of Chart Tools

Stage 7: Press OK on Select Data Source again, and then we can get the chart as shown in Fig.  2.15 . Press Move Chart on Chart Tools, select New sheet, and press OK. The finished chart is shown in Fig.  2.16 .

Step 4 of Chart Tools

Line Charts of rates of return for JNJ and MRK

Microsoft Excel has strong charting features. With more work, the chart in Fig.  2.16 can look like the chart in Fig.  2.17 .

Appendix 2: Stock Rates of Return and Market Rates of Return

Table  2.3 presents data on earnings per share (EPS), dividends per share (DPS), and price per share (PPS) for Johnson & Johnson, Merck, and the S&P 500 during the period 1988–2009. Table  2.4 shows rates of return for Johnson & Johnson, Merck, and the S&P 500, calculated from the data in Table  2.3 .

The formula for calculating the rate of return, R jt , on the j th individual stock in period t is

where P jt represents price per share for the j th stock in period t and d jt represents dividends per share for the j th stock. The market rate of return, R mt , in period t is

where SP t represents the S&P 500 in period t.

The rate of return on an individual stock can be rewritten as

The first term of the rewritten equation is the capital gain yield (in percent); the second is the dividend yield (also in percent). As an example, let us calculate the rate of return for Johnson & Johnson in 2009. From Table  2.3 , we know that PPS 08  = $59.83, PPS 09  = $64.41, and DPS 09  = $1.91. Thus, the rate of return for Johnson & Johnson in 2009 equals

As another example, from Table  2.3 , we know that the S&P 500 was 1220.04 and 948.05 in 2008 and 2009, respectively. Thus, the market rate of return in 2008 equaled

Figure  2.5 in the text compares the rates of return for Johnson & Johnson, Merck, and the S&P 500 over time, as discussed in Sect.  2.5 of this chapter.

Appendix 3: Financial Statements and Financial Ratio Analysis

3.1 review of balance sheets and income statements.

Accounting concepts are used to understand a firm’s financial condition. We will discuss two basic sources of accounting information: the balance sheet , which reveals the assets, liabilities, and owners’ (stockholders’) equity of a firm at a point in time, and the income statement, which shows the firm’s profit or loss over a given period of time. Assets , which are things that the firm owns, can be classified as current, fixed, or “other” assets. Current assets consist of cash and of property that can be turned into cash quickly. Examples of current assets include cash, stocks, bonds, inventory, and accounts receivable (cash that customers owe the firm). Fixed assets are not easily convertible into cash; they include land, machinery, and buildings. Fixed assets are generally valued at their historical value (purchase price) minus depreciation, not at their current market value. Other assets include intangibles such as goodwill, trademarks, patents, copyrights, and leases.

Liabilities , which are debts of the firm, are divided into current and long-term debts. Current debts come due within 1 year, whereas long-term debts are due in more than 1 year. Current debts include accounts payable (unpaid bills), notes payable, debts on agreements, accrued expenses, expenses incurred but not yet paid, and taxes payable. Examples of long-term liabilities include mortgages, payable corporate bonds, and capitalized leases.

Stockholders’ equity makes up the second part of the liabilities section of a balance sheet. It consists of funds invested by shareholders plus retained earnings. The net worth of the firm is calculated by subtracting total liabilities from total assets.

Whereas a balance sheet looks at the firm at a point in time, the income statement, as we noted earlier, evaluates the firm over a period of time. The end product of the income statement is the profit or loss, which is calculated by taking the sales for a period and subtracting the cost of goods sold and such expenses as research and development, interest, and selling, general, and administrative expenses.

Table  2.5 presents JNJ’s balance sheet for 2004–2009. Total assets are divided into current and fixed assets. Again, the current assets are those assets that can be converted into cash in a year or less; fixed assets such as land cannot be turned quickly into cash. The liabilities section is separated into liabilities and stockholders’ equity. GM’s liabilities include long-term debt, current liabilities such as accounts payable, and deferred taxes. Stockholders’ equity consists of common stock and paid-in surplus, preferred stock, retained earnings, and other adjustments. Note that total assets equal the sum of total liabilities and equity.

Table  2.6 displays JNJ’s income statement for both 2004–2009, showing the firm’s profit after expenses are subtracted from revenues. To determine gross profits, the costs of goods sold are subtracted from net sales. Operating income is then calculated by deducting selling and administrative expenses, debt amortization, and depreciation. Earnings before interest and taxes (EBIT) are next obtained by adding other income to operating income, and interest is subtracted to get earnings before taxes (EBT). Finally, net income is obtained by subtracting the provision for income taxes and adding earnings in unconsolidated subsidiaries and associates. Both earnings per share and dividends per share also are reported.

3.2 Financial Ratio Analysis

To help them analyze balance sheets and income statements, financial managers construct various financial ratios. There are five basic types of these ratios: leverage ratios, activity ratios, liquidity ratios, profitability ratios, and market value ratios. Let us use Merck (MRK) and Johnson & Johnson data to calculate a number of these ratios and discuss their significance.

Table  2.7 shows how financial ratios are derived from the 2009 balance sheet and income statement. The information for the current ratio comes from the assets side of the balance sheet. The ratio for JNJ is 1.82, which means that 1 dollar in current liabilities is matched by 1.82 dollars in current assets. To calculate the inventory turnover ratio, we use cost of goods sold from the income statement and inventories from the current assets in the balance sheet. The resulting ratio (3.561) reveals how often the average value of goods in inventory was sold in 2009.

The total debt to total assets ratio is derived from the balance sheet; it indicates that about 45.1 % of JNJ’s assets are financed by debt. Data to calculate the net profit margin come from the income statement. JNJ’s profit margin is.198, that is, about 20 cents out of every dollar of sales is profit (net income). ROI (return on investment) is more accurately described as return on total assets; it is calculated from information in both the income statement and the balance sheet. The resulting figure for JNJ is 12.9 %.

The price/earnings (P/E) ratio is calculated by taking the price per share divided by the earnings per share (EPS). Although the price per share does not appear in the balance sheet or the income statement, it can be found in stock reports in newspapers. The EPS is then found by dividing net income by the number of common shares. (The ratio cannot be calculated if the firm experienced losses.) The P/E ratio for JNJ is 14.474.

Finally, the payout ratio is calculated by dividing the price of the stock by the dividends per share (DPS). DPS is the value of dividends paid out divided by the number of shares of common stock. This ratio reveals that JNJ paid out about 0.434 % of its earnings in dividends.

The seven ratios discussed in Table  2.7 for both Johnson & Johnson and Merck during 2004–2009 are presented in Table  2.8 following the method discussed in Appendix 2 . Line charts in terms of data presented in Table  2.8 are presented in Figs. 2.17 , 2.18 , 2.19 , 2.20 , 2.21 , 2.22 and 2.23 . By using these seven graphs, we now compare the financial ratios of JNJ to those of Merck.

As mentioned above, there are five basic types of financial ratios. Liquidity ratios measure how quickly or effectively the firm can obtain cash. If the bulk of a firm’s assets are fixed (such as land), the firm may not be able to obtain enough cash to finance its operations. The current ratio , defined as current assets divided by current liabilities, is used to gage the firm’s ability to meet current obligations. If the firm’s current assets do not significantly exceed current liabilities, the firm may not be able to pay current bills, because although current assets are expected to generate cash within 1 year, current liabilities are expected to use cash within that same 1-year period. In Fig.  2.17 , this ratio is graphed for both Johnson & Johnson and Merck for the years 1990–2009. As the figure reveals, JNJ had a higher current ratio almost the entire time with the exception of 1998 when its current ratio was1.364, while Merck’s was 1.685.

Leverage ratios measure how much of the firm’s operation is financed by debt. Although some debt is expected, too much debt can be a sign of trouble. One indicator of how much debt the firm has incurred is the ratio of total debt to total assets, which measures the percentage of total assets financed by debt. Figure  2.18 shows that Johnson & Johnson had a greater share of its assets financed by debt than did Merck over most of the 1977–2009 periods. This fact is not necessarily a reason for concern unless Johnson & Johnson’s leverage ratio was too high in absolute terms. The general trend shows that both firms increased their debt during the period, particularly from 1985 to 1998, and that a sharp increase occurred from 1987 to 1992.

Current ratio for JNJ and MRK

Total debt to total assets ratio for JNJ and MRK

Activity ratios measure how efficiently the firm is using its assets. Figure  2.19 graphs the inventory turnover ratio for each firm; it is found by dividing cost of goods sold by average inventory. This ratio measures how quickly a firm is turning over its inventories. A high ratio usually implies efficiency because the firm is selling inventories quickly. This ratio varies greatly with the line of business, however. A supermarket must have a high turnover ratio because it is dealing with perishable goods; in contrast, a jewelry store selling diamonds has a much lower turnover ratio. The seasonality of the product must also be considered. Auto dealers have high inventories in the fall, when the new autos arrive, and lower inventories in other seasons. On the other hand, Christmas tree dealers have rather low inventories in August!

Profitability ratios measure the profitability of the firm’s operations. One of these ratios is the return on total assets, defined as net income divided by total assets. This ratio, often abbreviated ROA, measures how much the company has earned on its total investment of financial resources. Looked at in another way, it measures how well the firm used funds, regardless of how the firm’s assets are divided into fixed and current assets. As Fig.  2.20 suggests, Merck had a higher ROA than Johnson & Johnson until 2004–2007.

Inventory turnover for JNJ and MRK

Return on total assets for JNJ and MRK

Net profit margin for JNJ and MRK

The net profit margin, defined as net income divided by net sales, is another measure of profitability. This ratio gages the percentage of sales revenue that consists of profit. This ratio varies for different industries; a successful supermarket might have a ratio of 20 %, whereas most manufacturing firms tend to have ratios around 8 %. Although many Americans believe that corporations make a profit of 25 cents or more on each dollar of sales, the average net profit ratio for the Fortune 500 industrial firms in 1981 was 4.6 %. The net profit margins for Merck and Johnson & Johnson are presented in Fig.  2.21 .

An indirect profitability indicator is the payout ratio , which measures the proportion of current earnings paid out in dividends. This ratio, which is expressed as dividends per share divided by earnings per share, can fluctuate widely because of the variability in earnings per share.. The reason, for example, why the payout ratio was so high for Merck in 1981 is that earnings per share were low. The payout ratios for Merck and Johnson & Johnson are illustrated in Fig.  2.22 .

Market value ratios measure how stock price per share is related to either earnings per share or book value per share. The price/earnings ratio, or P/E, is shown in Fig.  2.23 . This ratio, defined as the price per share of a stock divided by the earnings per share, is usually reported in stock quotations in newspapers such as the Wall Street Journal every day. However, you should be careful in looking at P/E ratios because a high ratio can be the result of low earnings. This seems to have been the case for Merck in 2007. Moreover, firms calculate earnings per share differently, making comparisons of P/E ratios between firms difficult or even misleading.

Payout ratio for JNJ and MRK

Price/earnings ratio for JNJ and MRK

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Lee, CF., Lee, J.C., Lee, A.C. (2013). Data Collection and Presentation. In: Statistics for Business and Financial Economics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5897-5_2

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• School Guide
• Mathematics
• Number System and Arithmetic
• Trigonometry
• Probability
• Mensuration
• Maths Formulas
• Class 8 Maths Notes
• Class 9 Maths Notes
• Class 10 Maths Notes
• Class 11 Maths Notes
• Class 12 Maths Notes
• CBSE Class 9 Maths Revision Notes

Chapter 1: Number System

• Number System in Maths
• Natural Numbers | Definition, Examples & Properties
• Whole Numbers - Definition, Properties and Examples
• Rational Numbers: Definition, Examples, Worksheet
• Irrational Numbers: Definition, Examples, Symbol, Properties
• Real Numbers
• Decimal Expansion of Real Numbers
• Decimal Expansions of Rational Numbers
• Representation of Rational Numbers on the Number Line | Class 8 Maths
• Represent √3 on the number line
• Operations on Real Numbers
• Rationalization of Denominators
• Laws of Exponents for Real Numbers

Chapter 2: Polynomials

• Polynomials in One Variable | Polynomials Class 9 Maths
• Polynomial Formula
• Types of Polynomials (Based on Terms and Degrees)
• Zeros of Polynomial
• Factorization of Polynomial
• Remainder Theorem
• Factor Theorem
• Algebraic Identities

Chapter 3: Coordinate Geometry

• Coordinate Geometry
• Cartesian Coordinate System
• Cartesian Plane

Chapter 4: Linear equations in two variables

• Linear Equations in One Variable
• Linear Equation in Two Variables
• Graph of Linear Equations in Two Variables
• Graphical Methods of Solving Pair of Linear Equations in Two Variables
• Equations of Lines Parallel to the x-axis and y-axis

Chapter 5: Introduction to Euclid's Geometry

• Euclidean Geometry
• Equivalent Version of Euclid’s Fifth Postulate

Chapter 6: Lines and Angles

• Lines and Angles
• Types of Angles
• Pairs of Angles - Lines & Angles
• Transversal Lines
• Angle Sum Property of a Triangle

Chapter 7: Triangles

• Triangles in Geometry
• Congruence of Triangles |SSS, SAS, ASA, and RHS Rules
• Theorem - Angle opposite to equal sides of an isosceles triangle are equal | Class 9 Maths
• Triangle Inequality Theorem, Proof & Applications

Chapter 8: Quadrilateral

• Angle Sum Property of a Quadrilateral
• Quadrilateral - Definition, Properties, Types, Formulas, Examples
• Introduction to Parallelogram: Properties, Types, and Theorem
• Rhombus: Definition, Properties, Formula, Examples
• Trapezium in Maths | Formulas, Properties & Examples
• Square in Maths - Area, Perimeter, Examples & Applications
• Kite - Quadrilaterals
• Properties of Parallelograms
• Mid Point Theorem

Chapter 9: Areas of Parallelograms and Triangles

• Area of Triangle | Formula and Examples
• Area of Parallelogram (Definition, Formulas & Examples)
• Figures on the Same Base and between the Same Parallels

Chapter 10: Circles

• Circles in Maths
• Radius of Circle
• Tangent to a Circle
• What is the longest chord of a Circle?
• Circumference of Circle - Definition, Perimeter Formula, and Examples
• Angle subtended by an arc at the centre of a circle
• What is Cyclic Quadrilateral
• The sum of opposite angles of a cyclic quadrilateral is 180° | Class 9 Maths Theorem

Chapter 11: Construction

• Basic Constructions - Angle Bisector, Perpendicular Bisector, Angle of 60°
• Construction of Triangles

Chapter 12: Heron's Formula

• Area of Equilateral Triangle
• Area of Isosceles Triangle
• Heron's Formula
• Applications of Heron's Formula
• Area of Quadrilateral
• Area of Polygons

Chapter 13: Surface Areas and Volumes

• Surface Area of Cuboid
• Volume of Cuboid | Formula and Examples
• Surface Area of Cube
• Volume of a Cube
• Surface Area of Cylinder
• Volume of a Cylinder: Formula, Definition and Examples
• Surface Area of Cone
• Volume of Cone: Formula, Derivation and Examples
• Surface Area of Sphere: Formula, Derivation and Solved Examples
• Volume of a Sphere
• Surface Area of a Hemisphere
• Volume of Hemisphere

Chapter 14: Statistics

Collection and presentation of data.

• Graphical Representation of Data
• Bar Graphs and Histograms
• Central Tendency
• Mean, Median and Mode

Chapter 15: Probability

• Experimental Probability
• Empirical Probability
• CBSE Class 9 Maths Formulas
• NCERT Solutions for Class 9 Maths: Chapter Wise PDF 2024
• RD Sharma Class 9 Solutions

We come across a lot of information every day from different sources. Our newspapers, TV, Phone and the Internet, etc are the sources of information in our life. This information can be related to anything, from bowling averages in cricket to profits of the company over the years. These facts and figures are often numerical and are called Data. Statistics is the study of data. Let’s look into this in detail. 

Statistics – Collection and Presentation of Data

Before going into Statistics, first, let’s define what is Data. 

“Data are units of information, often numeric, collected through observation.”  It is plural form of the Latin word “Datum”.

Our world has become very information-oriented in the past two decades. So, it becomes essential for us to extract meaningful information out of data. For that we need statistics. Let’s see what statistics mean in formal terms. 

Statistics is derived from Latin word “Status” which means “a state”. It concerns with the nature, meaning and distribution of the data. 

Collection of Data

Collection of data refers to collecting information about something with an objective to analyze it or extract some meaningful information from it. Some examples of activities involving the collection of data are: 

Students collecting data from their localities about the number of people with Covid Vaccines. A Football fan collecting information about the goals scored by his favorite player. A record company collecting information about album sales by their artists.

Types of Recorded Data

Most of the time when we collect data for our experiment with an objective. It usually falls into one of these two categories: 

Categorical Data

• Numerical Data

This data represents the characteristics of something entity. For example, if we are collecting data about some people. Categorical data related to this information might be, gender of the person, marital status, etc. These things will have values that are not numerical, often “Yes/No” or in this case “Male/Female”. Since they are not numerical, they cannot be added together. 

Numerical Data 

This data comes out of measurement and is numerical in nature. For example, Weight of the person, stock prices, marks of students of class XII, etc. This data is also called quantitative data. It can be broken down further into types: 

• Continuous Data
• Discrete Data

Continuous Data : This data can take any value between intervals. The number of possible values for this data cannot be counted. For example Length of a ruler can take any length between 0-100cm. It can be either 30cm, 30.11cm and so on. There are infinitely many possible values. 

Discrete Data: This data takes only certain values. For example: If a coin is tossed three times, and we want to count the number of heads. There are only a handful of values that are possible. 0,1,2 or 3. It cannot take 2.2 or any other value. So, there are only finite possible values. 

Presentation of Data

After collecting the data, we need to present it in a meaningful way. Let’s take an example, 

Suppose we have the data of heights of students in a class, 

140, 161, 152, 184, 135, 168 and 144.

We need to answer the following questions related to the data: 

• What is the height of the longest student in the class?
• What is the height of the shortest student in the class?
• What is the average height?

It is a little difficult to analyze the data in this format. The data in the form is called raw data. Analyzing the data in this form might take more time if the data is big. It can be made a little easier if sort the data in ascending or descending order.  Thus, in this way, the presentation of data affects the information and the time taken to extract it from the data. 

Suppose if this data was even bigger, then it would be very difficult to organize the data in sorted order. In such cases, we might use a frequency table. Let’s see this through an example. 

Un-Grouped Frequency Distribution

In this type of frequency table, we consider the values as it is and then count their number of occurrences in the data. We don’t group the data. Let’s see this through an example. 

Question: Let’s say we have marks of students of class XII. The marks are out of 40. 

Represent this data using a frequency table. 

Solution: 

Let’s take marks of some student in one column and frequency of such marks in another column.  Marks Frequency 5 1 7 1 8 1 10 1 11 1 13 2 15 2 20 2 21 1 24 2 29 1 33 1 38 1 40 1 Notice that in this table, we have not grouped the data instead we have taken exact values and their frequency. So, this type of representation is called ungrouped frequency distribution. 

Grouped Frequency Distribution

The previous kind of representation is definitely an improvement over previous representations but as seen in the above example, tables can get pretty big in such representations. Tally Marks and grouping can also be used to represent this data. 

Question: We have the data for the number of covid cases on a particular day in 20 cities. 

In the previous example we saw that ungrouped frequency distribution is cumbersome and very long to look at. So now, we will divide the data into groups. This kind of frequency table representation is called grouped frequency representation.  Let’s divide the numbers of cases in the groups like, 0-5, 5-10, 10-15 … and so on.  Then the frequency table will become,  Group Frequency 0-5 2 5-10 3 10-15 1 15-20 3 20-25 2 25-30 2 30-35 1 35-40 1

The intervals like 0-5, 5-10 .. And so on given in the above example are called class intervals. The larger number is called higher limit and the lower number is called the lower limit. 

Let’s see some sample problems on these concepts 

Sample Problems

Problem 1: The table below represents the data. Represent this data in the form of suitable frequency distribution. 

We can see from the data given above, that there are only three values – 2,3 and 4. These values occur multiple times throughout the data. Since there are very less number of values, we can represent this kind of data in the form un-grouped frequency table.  Value Frequency 2 4 3 5 4 3 Total – 12

Problem 2: The data given below represents the blood groups of the 20 students of class XI. 

Represent the data given above in the table in the form of a frequency table. Which of the following blood group has the highest frequency among the students?

We know there are four types of blood groups in the table.  O, A, AB and B So, we will use ungrouped frequency distribution table to represent the data.  Blood Group Frequency  O 5 A 5 AB 4 B 6 Total  20 From the frequency distribution table we can tell the B is the blood group which most commonly occurring in students. 

Problem 3: The table represents the weights of the students of class X. 

Answer the following questions: 

• What is the range in which most students lie? 
• Suppose students weighing more than 70 are considered overweight and those weighing less than 50 are considered as underweight. How many such students are there in the class? 

Let’s make a grouped frequency distribution table for this data.  Assuming intervals like 0-10,10-20…and so on. Let’s divide the data into these intervals are count the frequency.  Weight Group Frequency 0-10 0 10-20 0 20-30 0 30-40 0 40-50 3 50-60 4 60-70 6 70-80 2 80-90 1 Total  16 This above table represents a grouped frequency table. Now answering the questions.  1. Most students lie in the range from 60-70.  2. For overweight students, we need to count the number of students with weight greater than 70. It can be observed from the table that there are three such students.  For underweight students, the number students with weight less than 50 are also three students. 

Problem 4: Three coins are tossed 20 times. The number of heads that occurred each time is recorded and given in this data below. Prepare a frequency distribution for the given data. 

We know there are maximum of three heads possible at each turn in this experiment. So we can actually make an ungrouped frequency distribution for such data Number of Heads Frequency 0 3 1 5 2 8 3 4 Total  20 Thus, the table above represents the frequency table for this data. 

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9 Data Presentation Tools for Business Success

• By Judhajit Sen
• May 29, 2024

A data presentation is a slide deck that shares quantitative information with an audience using visuals and effective presentation techniques . The goal is to make complex data easily understandable and actionable using data presentation examples like graphs and charts, tables, dashboards, and clear text explanations. 

Data presentations help highlight trends, patterns, and insights, allowing the audience to grasp complicated concepts or trends quickly. This makes it easier for them to make informed decisions or conduct deeper analysis.

Data visualization in presentations is used in every field, from academia to business and industry. Raw data is often too complex to understand directly, so data analysis breaks it down into charts and graphs. These tools help turn raw data into useful information.

Once the information is extracted, it’s presented graphically. A good presentation can significantly enhance understanding and response.

Think of data presentation as storytelling in business presentations with charts. A common mistake is assuming the audience understands the data as well as the presenter. Always consider your audience’s knowledge level and what information they need when you present your data.

To present the data effectively:

1. Provide context to help the audience understand the numbers.

2. Compare data groups using visual aids.

3. Step back and view the data from the audience’s perspective.

Data presentations are crucial in nearly every industry, helping professionals share their findings clearly after analyzing data.

Key Takeaways

• Simplifying Complex Data: Data presentations turn complex data into easy-to-understand visuals and narratives, helping audiences quickly grasp trends and insights for informed decision-making.
• Versatile Tools: Various tools like bar charts, dashboards, pie charts, histograms, scatter plots, pictograms, textual presentations, and tables each serve unique purposes, enhancing the clarity and impact of the data.
• Audience Consideration: Tailor your presentation to the audience’s knowledge level, providing context and using simple visuals to make the information accessible and actionable.
• Effective Data Storytelling: Combining clear context, organized visuals, and thoughtful presentation ensures that the data’s story is conveyed effectively, supporting better business decisions and success.

Following are 9 data presentation tools for business success.

Bar charts are a simple yet powerful method of presentation of the data using rectangular bars to show quantities or frequencies. They make it easy to spot patterns or trends at a glance. Bar charts can be vertical (column charts) or horizontal, depending on how you want to display your data.

In a bar graph, categories are displayed on one axis, usually the x-axis for vertical charts and the y-axis for horizontal ones. The bars’ lengths represent the values or frequencies of these categories, with the scale marked on the opposite axis.

These charts are ideal for comparing data across different categories or showing trends over time. Each bar’s height (or length in a horizontal chart) is directly proportional to the value it represents. This visual representation helps illustrate differences or changes in data.

Bar charts are versatile tools in business reports, academic presentations, and more. To make your bar charts effective:

• Ensure they are concise and have easy-to-read labels.
• Avoid clutter by not including too many categories, making the chart hard to read.
• Keep it simple to maintain clarity and impact, whether your bars go up or sideways.

Line Graphs

Line graphs show how data changes over time or with continuous variables. They connect points of data with straight lines, making it easy to see trends and fluctuations. These graphs are handy when comparing multiple datasets over the same timeline.

Using line graphs, you can track things like stock prices, sales projections, or experimental results. The x-axis represents time or another continuous variable, while the y-axis shows the data values. This setup allows you to understand the ups and downs in the data quickly.

To make your graphs effective, keep them simple. Avoid overcrowding with too many lines, highlight significant changes, use labels, and give your graph a clear, catchy title. This will help your audience grasp the information quickly and easily.

A data dashboard is a data analysis presentation example for analyzing information. It combines different graphs, charts, and tables in one layout to show the information needed to meet one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs) by displaying visuals you’ve already made in worksheets.

It’s best to keep the number of visuals on a dashboard to three or four. Adding too many can make it hard to see the main points. Dashboards are helpful for business analytics, like analyzing sales, revenue, and marketing metrics. In manufacturing, they help users understand the production scenario and track critical KPIs for each production line.

Dashboards represent vital points of data or metrics in an easy-to-understand way. They are often an  interactive presentation idea , allowing users to drill down into the data or view different aspects of it.

Pie charts are circular graphs divided into parts to show numerical proportions. Each portion represents a part of the whole, making it easy to see each component’s contribution to the total.

The size of each slice is determined by its value relative to the total. A pie chart with more significant points of data will have larger slices, and the whole chart will be more important. However, you can make all pies the same size if proportional representation isn’t necessary.

Pie charts are helpful in business to show percentage distributions, compare category sizes, or present simple data sets where visualizing ratios is essential. They work best with fewer variables.

Each “slice” represents a fraction of the total, and the size of each slice shows its share of the whole. Pie charts are excellent for showing how a whole is divided into parts, such as survey results or demographic data.

While pie charts are great for simple distributions, they can get confusing with too many categories or slight differences in proportions. To keep things clear, label each slice with percentages or values and use a legend if there are many categories. If more detail is needed, consider using a donut chart with a blank center for extra information and a less cluttered look.

A histogram is a graphical presentation of data  to help in understanding the distribution of numerical values. Unlike bar charts that show each response separately, histograms group numeric responses into bins and display the frequency of reactions within each bin. The x-axis denotes the range of values, while the y-axis shows the frequency of those values.

Histograms are useful for understanding your data’s distribution, identifying shared values, and spotting outliers. They highlight the story your data tells, whether it’s exam scores, sales figures, or any other numerical data.

Histograms are great for visualizing the distribution and frequency of a single variable. They divide the data into bins, and the height of each bar indicates how many points of data fall into that bin. This makes it easy to see trends like peaks, gaps, or skewness in your data.

To make your histogram effective, choose bin sizes that capture meaningful patterns. Clear axis labels and titles also help in explaining the data distribution.

Scatter Plot

Using individual data points, a scatter plot chart is a presentation of data in visual form to show the relationship between two variables. Each variable is plotted along the x-axis and y-axis, respectively. Each point on the scatter plot represents a single observation.

Scatter plots help visualize patterns, trends, and correlations between the two variables. They can also help identify outliers and understand the overall distribution of data points. The way the points are spread out or clustered together can indicate whether there is a positive, negative, or no clear relationship between the variables.

Scatter plots can be used in practical applications, such as in business, to show how variables like marketing cost and sales revenue are related. They help understand data correlations, which aids in decision-making.

To make scatter plots more effective, consider adding trendlines or regression analysis to highlight patterns. Labeling key data points or tooltips can provide additional information and make the chart easier to interpret.

A pictogram is the simplest form of data presentation and analysis, often used in schools and universities to help students grasp concepts more effectively through pictures.

This type of diagram uses images to represent data. For example, you could draw five books to show the number of books sold in the first week of release, with each image representing 1,000 books. If consumers bought 5,000 books, you would display five book images.

Using simple icons or images makes the information visually intuitive. Instead of relying on numbers or complex graphs, pictograms use straightforward symbols to depict data points. For example, a thumbs-up emoji can illustrate customer satisfaction levels, with each emoji representing a different level of satisfaction.

Pictograms are excellent for visual data presentation. Choose symbols that are easy to interpret and relevant to the data to ensure clarity. Consistent scaling and a legend explaining the symbols’ meanings are essential for an effective presentation.

Textual Presentation

Textual presentation uses words to describe the relationships between pieces of information. This method helps share details that can’t be shown in a graph or table. For example, researchers often present findings in a study textually to provide extra context or explanation. A textual presentation can make the information more transparent.

This type of presentation is common in research and for introducing new ideas. Unlike charts or graphs, it relies solely on paragraphs and words.

Textual presentation also involves using written content, such as annotations or explanatory text, to explain or complement data. While it doesn’t use visual presentation aids like charts, it is a widely used method for presenting qualitative data. Think of it as the narrative that guides your audience through the data.

Adequate textual data may make complex information more accessible. Breaking down complex details into bullet points or short paragraphs helps your audience understand the significance of numbers and visuals. Headings can guide the reader’s attention and tell a coherent story.

Tabular Presentation

Tabular presentation uses tables to share information by organizing data in rows and columns. This method is useful for comparing data and visualizing information. Researchers often use tables to analyze data in various classifications:

Qualitative classification: This includes qualities like nationality, age, social status, appearance, and personality traits, helping to compare sociological and psychological information.

Quantitative classification: This covers items you can count or number.

Spatial classification: This deals with data based on location, such as information about a city, state, or region.

Temporal classification: This involves time-based data measured in seconds, hours, days, or weeks.

Tables simplify data, making it easily consumable, allow for side-by-side comparisons, and save space in your presentation by condensing information.

Using rows and columns, tabular presentation focuses on clarity and precision. It’s about displaying numerical data in a structured grid, clearly showing individual data points. Tables are invaluable for showcasing detailed data, facilitating comparisons, and presenting exact numerical information. They are commonly used in reports, spreadsheets, and academic papers.

Organize tables neatly with clear headers and appropriate column widths to ensure readability. Highlight important data points or patterns using shading or font formatting. Tables are simple and effective, especially when the audience needs to know precise figures.

Elevate Business Decisions with Effective Data Presentations

Data presentations are essential for transforming complex data into understandable and actionable insights. Data presentations simplify the process of interpreting quantitative information by utilizing data presentation examples like charts, graphs, tables, infographics, dashboards, and clear narratives. This method of storytelling with visuals highlights trends, patterns, and insights, enabling audiences to make informed decisions quickly.

In business, data analysis presentations are invaluable. Different types of presentation tools like bar charts help compare categories and track changes over time, while dashboards consolidate various metrics into a comprehensive view. Pie charts and histograms offer clear views of distributions and proportions, aiding in grasping the bigger picture. Scatter plots reveal relationships between variables, and pictograms make data visually intuitive. Textual presentations and tables provide detailed context and precise figures, which are essential for thorough analysis and comparison.

Consider the audience’s knowledge level to tailor the best way to present data in PowerPoint. Clear context, simple visuals, and thoughtful organization ensure the data’s story is easily understood and impactful. Mastering these nine data presentation types can significantly enhance business success by making data-driven decisions more accessible and practical.

Frequently Asked Questions (FAQs)

1. What is a data presentation?

A data presentation is a slide deck that uses visuals and narrative techniques to make complex data easy to understand and actionable. It includes charts, graphs, tables, infographics, dashboards, and clear text explanations.

2. Why are data presentations important in business?

Data presentations are crucial because they help highlight trends, patterns, and insights, making it easier for the audience to understand complicated concepts. This enables better decision-making and deeper analysis.

3. What types of data presentation tools are commonly used?

Common tools include bar charts, line graphs, dashboards, pie charts, histograms, scatter plots, pictograms, textual presentations, and tables. Each tool has a unique way of representing data to aid understanding.

4. How can I ensure my data presentation is effective?

To ensure effectiveness, provide context, compare data sets using visual aids, consider your audience’s knowledge level, and keep visuals simple. Organizing information thoughtfully and avoiding clutter enhances clarity and impact.

Transform Your Data into Compelling Stories with Prezentium

Unlock the full potential of your business data with Prezentium ‘s expert presentation services. Our AI-powered solutions turn complex data into clear, actionable insights, helping you easily make informed decisions. 

Prezentium’s Overnight Presentations ensure you wake up to a stunning, ready-to-use presentation in your inbox by 9:30 am PST. Send your requirements by 5:30 pm PST, and let our team combine business acumen, visual design, and data science to craft a presentation that highlights trends and insights seamlessly.

Our Presentation Specialists transform raw ideas and meeting notes into captivating presentations. Whether you need new designs or bespoke templates, our experts bring your vision to life with precision and creativity.

Enhance your team’s skills with Zenith Learning, our interactive workshops that blend structured problem-solving with visual storytelling. Learn to present data effectively and make a lasting impact in your business communications.

Prezentium’s services are designed to help you make the most of your data, from bar charts to dashboards, ensuring your presentations are informative and visually engaging. Let us help you tell your data’s story in a way that resonates. Contact Prezentium today to elevate your business presentations.

Why wait? Avail a complimentary 1-on-1 session with our presentation expert. See how other enterprise leaders are creating impactful presentations with us.

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Unstructured Interviews: Is It a Good Choice for Qualitative Data Collection? - PowerPoint PPT Presentation

Unstructured Interviews: Is It a Good Choice for Qualitative Data Collection?

In today’s blog, we'll explore the benefits of unstructured interviews as a qualitative data research method, compare them with structured and semi-structured interviews and discuss real-world scenarios where they can be effectively used. let’s get started – powerpoint ppt presentation.

• Choosing the right qualitative data collection method is crucial for gathering rich and insightful information. Unstructured interviews happen to be one such descriptive qualitative research method that offers flexibility and depth in understanding participants' perspectives. In todays blog, we'll explore the benefits of unstructured interviews as a qualitative data research method, compare them with structured and semi-structured interviews and discuss real-world scenarios where they can be effectively used. Lets get started!
• Unstructured interviews are one of the common qualitative data collection methods. In this particular methodology, open-ended conversations are conducted between the researcher and participant(s) where the discussion flows freely without a predetermined set of questions or a rigid interview protocol. Unlike structured interviews that follow a fixed format with standardized questions, unstructured interviews allow for spontaneous exploration of topics, encouraging participants to express their thoughts, feelings, and experiences in their own words. The open-ended approach involved in these interviews fosters rich, in-depth discussions and enables researchers to uncover unexpected insights and nuances. 
• Structured vs. Semi-Structured vs. Unstructured Interviews A Detailed Comparison
• Unstructured Interviews
• Structured Interviews
• Semi-Structured Interviews
• There is no one-size-fits-all approach to choosing the right interview format. The interview format of your choice should ensure that your research objectives are met and that you gather high-quality data. Here are some considerations to help you select the most appropriate interview format
• Research Goals
• Nature of the Research Topic
• Characteristics of Participants
• Level of Flexibility Desired
• Resources and Constraints
• Researcher Skills and Experience
• Unstructured interviews offer several distinct advantages for qualitative data collection and research
• Rich, In-depth Data
• Flexibility
• Participant Engagement
• Discovery of New Findings
• Holistic Understanding
• Empowerment of Participants
• Unstructured interviews are a valuable qualitative research method that can be applied in a wide range of real-world scenarios, some of which are listed below
• Exploratory Research
• Qualitative Studies
• Case Studies
• Ethnographic Studies
• Clinical Research
• Program Evaluation
• Market Research
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• Unstructured interviews are a powerful tool for qualitative data collection, offering unparalleled flexibility, depth, and richness in understanding participants' perspectives. Even a single wrong step can potentially tamper the authenticity of the data collected. In case you are unsure of where to start, hiring global market research firms can do the trick for you. When looking for a reputable qualitative research agency adept at conducting different types of qualitative research methods, your search ends here at Unimrkt Research. We are one of the top qualitative research companies, offering market research services in over 22 languages. We are also ISO20252 and ISO 27001 certified. To learn more about how we can assist you, call us at 91-124-424-5210 or drop an email at sales_at_unimrkt.com. You may also fill out our contact form, and we will get back to you shortly.

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Best Enterprise Content Management PPT Templates

Streamlining Information Enterprise Content Management (ECM) is a comprehensive approach to organizing, storing, and managing an organization's documents and information assets. It encompasses strategies, processes, and tools to facilitate efficient access, retrieval, and utilization of content throughout its lifecycle. ECM is utilized by a diverse range of professionals including executives, IT administrators, content creators, and compliance officers. They rely on ECM to centralize information, improve collaboration, ensure regulatory compliance, and enhance decision-making. 

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Annual Disability Statistics Collection

The Annual Disability Statistics Compendium, Annual Report on People With Disabilities, the Build Your Own Statistics tool, and infographics gather disability statistics published by many federal agencies into one place. They’re helpful for lawmakers and others who are working on laws or policies about disabilities, and for those writing grants for programs for people with disabilities.

2024 Release

New this year

There are many new changes to the Annual Disability Statistics Collection this year. The HTML version of the Compendium is now accessible and downloadable. Both online and downloaded versions of the Compendium can be read by screen readers. We updated the tables in the Compendium to include more measures of accuracy, and rounded estimates to the nearest thousandths to make the tables easier to read. Instead of publishing the Supplement, this year we made an online tool where you can Build Your Own Statistics.

To receive the Annual Disability Statistics Collection by mail

• Build Your Own Statistics

Find statistics previously published in the Supplement, including intersectional stats on race, age, gender, and disability.

Look through frequently asked questions about the Annual Disability Statistics Collection.

Annual Conference and Archive

View recordings and presentations from past Research on Disability events.

Collection Materials

Statistics in the Compendium are described for people ages 18-64, the working age. It also includes new measures of accuracy, such as margins of error and relative ratio, which are defined in the glossary.

Viewing and Download Options:

Online Compendium HTML Download (39.5 MB) Compendium PDF (for Print) CSV/XLS tables can be provided upon request

The Annual Report on People with Disabilities in America tracks the progress of people with disabilities using key social and economic indicators, and highlights increases or decreases for each indicator. The PDF is accessible.

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• Section 3: Employment
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• Section 8: Veterans
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