Test: Statistics - Class 9 MCQ

An exclusive distribution is characterized by class intervals that do not include their upper limits. This means the upper limit of one class coincides with the lower limit of the next, ensuring distinct categories.

Organizing data into tables allows for clearer presentation and easier analysis. Tables provide a structured way to view data, making it simpler to identify trends, compare values, and summarize information effectively.

The adjustment factor is used to ensure equal spacing between class intervals when converting from inclusive to exclusive classes. This adjustment helps maintain clarity and accuracy in data representation.

A cumulative frequency table summarizes the frequencies up to each class, showing how many observations fall within or below certain class intervals. It provides insight into the distribution of data across categories.

Cumulative frequency is the sum of frequencies of all classes up to and including a particular class. It provides insight into how many observations fall below a certain value, which is useful for understanding data distributions.

An inclusive distribution is one where the upper limit of one class is included in that class, while the lower limit of the next class starts from the next value. This method helps in organizing data without confusion over boundaries.

A continuous variable can take any numerical value within a range, such as height, which can vary infinitely between any two points. In contrast, discrete variables, such as the number of students, can only take specific values.

The primary purpose of statistics is to analyze and interpret data to gain meaningful insights. Through collecting data, organizing it, and presenting it in various formats, statistics helps individuals and organizations make informed decisions based on numerical evidence.

Clear titles and labels are crucial in graphs as they help viewers quickly understand what the graph represents. Effective labeling enhances comprehension and makes the data more accessible to the audience.

The class mark is the midpoint of a class interval, calculated as the average of the upper and lower limits of that interval. It is useful for summarizing and analyzing data within the classes.

Class size is calculated by subtracting the lower limit from the upper limit of a class interval. This measurement indicates the range of values that the class covers, which is essential for organizing data accurately.

The primary focus of statistics is to collect, analyze, and interpret data for various applications. This field is essential in many areas, including business, healthcare, and social sciences, for making informed decisions based on data.

Tabulation of data refers to presenting data in an organized table format, making it easier to read and understand. This process helps summarize and clarify data, allowing for efficient analysis.

Frequency refers to how often a specific value appears within a dataset. Understanding frequency is essential for analyzing data distributions and identifying patterns.

The main goal of graphical representation is to present numerical data in a visually appealing way that simplifies understanding. Well-designed graphs can convey complex information more effectively than text alone.

Calculating frequencies in a dataset helps identify the most common observations, allowing for the analysis of trends and patterns within the data. This information is vital for drawing conclusions and making decisions.

A histogram is a type of bar graph where the heights of the bars represent the frequencies of data within specified intervals. It visually displays the distribution of data, making it easier to identify trends and patterns.

A discrete variable can only take specific, distinct values, such as whole numbers. For example, the number of children in a family can only be whole numbers (1, 2, 3, etc.) and cannot be fractions.

A frequency polygon is created by plotting the midpoints of the histogram bars and connecting them with straight lines. This graph helps illustrate the distribution of data in a more continuous form.

In statistics, a variable refers to a quantity that can change from one individual to another, such as height, weight, or age. Variables are fundamental in data collection and analysis, allowing comparisons across different subjects.