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Ncert Books Class 9 Maths Chapter 14 Free Download

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Ncert Books Class 9 Maths Chapter 14 Free Download

Ever wondered how to make sense of all the numbers, graphs, and data you see in your daily life? In Ncert Books Class 9 Maths Chapter 14 Free Download, youโ€™ll learn the basics of statisticsโ€”like collecting, organizing, and understanding information. This chapter will teach you how to work with tables, graphs, and averages so you can handle data confidently, whether itโ€™s cricket scores or your own exam results.


Don't worry if statistics feels tricky at firstโ€”Vedantu gives you simple explanations and helpful NCERT solutions you can download as PDFs. You can also check the Class 9 Maths Syllabus any time to see how this topic fits into your studies.


Practicing with curated questions makes it easier to score better and overcome confusion. For more guidance, explore the Class 9 Maths Important Questions and boost your exam preparation.


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Ncert Books Class 9 Maths Chapter 14 Free Download
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STATISTICS in One Shot (Complete Chapter) CBSE Class 9 Math Chapter 14 [Term 1 Exam] NCERT Vedantu
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Vedantuโ€™s Maths Class 9 Chapter - 14 Statistics Free PDF

In statistics, data are gathered, organized, analyzed, interpreted, and presented. To analyze a scientific, industrial, or social problem, it is common to start with a statistical population or model. Populations can include a wide range of people or things, such as "all individuals living in a country" or "every atom in a solid." Statistics involves planning data collection methods, such as surveying and doing experiments. Mathematics is applied to statistics in a process known as mathematical statistics. Mathematics tools used in this process include analysis, linear algebra, stochastic analysis, differential equations, and measure-theoretic probability theory.

With the development of probability theory by Gerolamo Cardano, Blaise Pascal, and Pierre de Fermat in the 17th century, the mathematical underpinnings of contemporary statistics were created. Although the idea of probability was already addressed in medieval law and by philosophers such as Juan Caramuel, mathematical probability theory originated from the study of games of chance. Adrien-Marie Legendre was the first to describe the least-squares approach in 1805.

Statistical approaches are now used in all sectors that need decision-making, for producing reliable inferences from a large body of data, and for making conclusions based on statistical methodology in the face of ambiguity. Modern computers have sped up large-scale statistical computations and enabled novel procedures that would be impossible to accomplish manually. Statistics is still a hot topic of study, as evidenced by the issue of analysing large amounts of data.


Overview of the Chapter

  1. Introduction

We come across a lot of data every day in the form of facts, numerical figures, tables, graphs, and so on. Newspapers, TV, periodicals, and other forms of communication supply these. These may include batting or bowling averages in cricket, corporate revenues, city temperatures, spending in various areas of a five-year plan, polling results, and so on. Data refers to numerical or non-numerical facts or statistics collected for a certain reason. The plural version of the Latin word datum is data. Of course, you're not unfamiliar with the term "data." In previous classes, you learned about data and how to handle it. Our world is becoming increasingly information-centric. The use of data is everywhere and in everything today. As a result, knowing how to extract useful information from such data becomes critical. Statistics is a discipline of mathematics that studies the extraction of useful data. The word statistics is said to have sprung from the Latin word status, which means a (political) state. Statistics began as a simple gathering of data on many elements of people's lives that the government might exploit. However, statistics' scope grew throughout time, and it began to concern itself not just with the collecting and display of data, but also with the interpretation and inferences drawn from the data. Statistics is the study of data gathering, organisation, analysis, and interpretation. In different settings, the term "statistics" has distinct connotations.


  1. Collection of Data

Data collection in statistics is the process of obtaining information from all relevant sources to solve a research topic. It aids in the evaluation of the problem's outcome. The data collecting methods enable a person to conclude Upper-class the relevant question. Methods for collecting primary data.


  1. Presentation of Data

The information gathered might be presented in a tabular, diagrammatic, or visual format. A table is a column-by-column and row-by-row arrangement of categorised data. When an experiment has a significant number of observations, the number of times a value appears in the data is tabulated, which is known as the frequency of that value. A frequency distribution table is a table that depicts the frequency of distinct values in a set of data. An ungrouped frequency distribution table is a frequency distribution table that indicates the frequency of each unique value in the provided data. A grouped frequency distribution table is a table that displays the frequency of groupings of values in a set of data. Classes or class intervals are the groups used to group the values in provided data. The class size or class width refers to the number of values included in each class.


  1. Graphical Representation of Data

The use of tables to represent data has already been considered. This section looks at another type of data representation, namely the graphical representation. According to the saying, a picture is worth a thousand words. When comparing things, graphs are usually the easiest way. The depiction becomes more understandable than the facts themselves. In this part, we'll look at the graphical representations that follow.

  1. Bar graphs 

  2. Histograms of uniform width and varying widths

  3. Frequency polygons 


  1. Measures of Central Tendency

It portrayed the data in a variety of ways earlier in this chapter, including frequency distribution tables, bar graphs, histograms, and frequency polygons. Now the question is whether we always need to analyse all of the data to make sense of it, or if we can extract some key aspects by looking at only a few representative samples. Using measurements of central tendency or averages is achievable.


Important Terms 

  • Ungrouped Data: Ungrouped data is data in its unprocessed or unaltered state.

  • Grouped Data: In grouped data, all of the observations are placed into a single group.

  • Frequency: The total number of times a certain piece of information or observation appears in the data.

  • Class Interval: All of the data in a chart can be separated into groups or class intervals, with all of the observations in that range belonging to only that group.

  • Class Width: Upper-class limit - lower class limit

  • Mean: The average of "n" numbers is found by dividing the sum of all the numbers by n.

  • Mode: It is by far the most common observation. In a class interval, the modal class is the one with the highest frequency.

  • Median: The median is the value of observation in the centre.
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FAQs on Ncert Books Class 9 Maths Chapter 14 Free Download

1. What are some frequently asked 3-mark and 5-mark important questions from Statistics Class 9 for the CBSE 2025-26 exams?

For the CBSE 2025-26 exams, important questions from Statistics typically cover:

  • 3-Mark Questions: Constructing a frequency distribution table from raw data, calculating the mean or median of ungrouped data, and drawing a simple bar graph.
  • 5-Mark Questions: Drawing a histogram (both with uniform and varying class widths), drawing a frequency polygon (sometimes superimposed on a histogram), and problems involving finding a missing value when the mean is given.

2. What type of important questions are asked from the graphical representation of data in Chapter 14?

Graphical representation is a high-weightage topic in the Class 9 Maths exam. Important questions include:

  • Constructing a histogram for a continuous frequency distribution.
  • Constructing a histogram for a distribution with unequal class intervals, which requires adjusting the lengths of the rectangles.
  • Drawing a frequency polygon for a given data set, either with or without constructing a histogram first.
  • Interpreting data from a given bar graph or histogram to answer specific questions.

3. From the measures of central tendency, which topic holds more weightage in exams: mean, median, or mode?

In Class 9, questions on calculating the mean, median, and mode of ungrouped data are all considered important. However, problems involving the median are often considered slightly more complex as they test conceptual clarity, especially when dealing with an even number of observations. You can expect direct calculation questions on all three for 2 or 3 marks.

4. How does a histogram differ from a bar graph, and why is this a common source of error in exams?

This is a crucial distinction frequently tested in exams. A bar graph represents discrete or categorical data, and its bars are separated by uniform gaps. In contrast, a histogram represents continuous data grouped in class intervals, and its adjacent bars have no gaps. A common error is leaving gaps between the bars of a histogram or using a histogram for non-continuous data, which can lead to a loss of marks.

5. Why is it important to adjust the frequency when drawing a histogram with unequal class widths?

In a histogram, the area of each rectangle must be proportional to the frequency of its class interval, not just its height. When class widths are equal, height is directly proportional to frequency. However, with unequal widths, this is not true. We must calculate the adjusted frequency to modify the bar heights, ensuring the areas correctly represent the data distribution. This is a key concept tested in higher-order thinking skills (HOTS) questions.

6. In what scenario would a frequency polygon be more useful than a histogram for analysing data trends?

A frequency polygon is particularly useful when you need to compare the distributions of two or more data sets on the same graph, for example, comparing the performance of students in two different sections. Plotting two histograms on the same axes can be cluttered and difficult to interpret, whereas two frequency polygons can be easily overlaid and compared to analyse trends and differences more clearly.

7. Are questions on the difference between primary and secondary data important for the Class 9 exam?

Yes, while calculation-based problems carry more marks, you can expect 1- or 2-mark questions that test your understanding of fundamental concepts. An important question could be to define primary and secondary data with examples or to classify a given data collection scenario as either primary or secondary. This tests the foundational knowledge of the chapter as per the NCERT syllabus.

8. What is the most common mistake students make when finding the median of an ungrouped data set?

The most frequent error is forgetting to arrange the data in ascending or descending order before applying the median formula. The median is the value of the middle observation only after the data has been sorted. Another common pitfall is incorrectly applying the formula for an even number of observations, where the median is the average of the two middle terms, i.e., the average of the (n/2)th and (n/2 + 1)th terms.