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Frequency Distribution - Presentation of Data, Business Mathematics & Statistics Video Lecture | SSC CGL Tier 2 - Study Material, Online Tests, Previous Year

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1. What is a frequency distribution?
Ans. A frequency distribution is a representation of data that shows the number of times a particular value or range of values occurs in a dataset. It helps in understanding the distribution and pattern of data by organizing it into groups or intervals along with their corresponding frequencies.
2. How is a frequency distribution useful in analyzing data?
Ans. A frequency distribution provides a concise summary of data by showing the frequency or count of each value or range of values. This helps in identifying the most common or rare values, outliers, and patterns in the data. It also aids in making comparisons between different groups or categories within the dataset.
3. What are the steps involved in creating a frequency distribution?
Ans. The steps involved in creating a frequency distribution are as follows: 1. Determine the range of values in the dataset. 2. Decide on the number of intervals or groups to be created. 3. Calculate the width of each interval by dividing the range by the number of intervals. 4. Set up the intervals and create a frequency table. 5. Count the number of data points falling into each interval and record the frequencies. 6. Optionally, calculate cumulative frequencies and percentages.
4. How can a frequency distribution be presented graphically?
Ans. A frequency distribution can be presented graphically using various types of charts or graphs. Some commonly used graphical representations include histograms, bar charts, and frequency polygons. These visual representations provide a visual summary of the distribution of data, making it easier to interpret and analyze.
5. What are some common measures used in analyzing a frequency distribution?
Ans. Some common measures used in analyzing a frequency distribution are: 1. Measures of central tendency, such as mean, median, and mode, which provide information about the central or typical value of the data. 2. Measures of dispersion, such as range, variance, and standard deviation, which indicate the spread or variability of the data. 3. Measures of skewness and kurtosis, which describe the shape of the distribution. 4. Percentiles and quartiles, which divide the data into equal parts and provide information about the relative position of values within the distribution.
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