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<p>Last updated on<strong>November 26, 2025</strong></p>
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<p>Last updated on<strong>November 26, 2025</strong></p>
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<p>Descriptive statistics is the branch of statistics that uses the method of summarizing and organizing data to reveal patterns and trends. These techniques help present data clearly through tables, graphs, and charts without making predictions or inferences.We shall now learn more about descriptive statistics in the topic.</p>
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<p>Descriptive statistics is the branch of statistics that uses the method of summarizing and organizing data to reveal patterns and trends. These techniques help present data clearly through tables, graphs, and charts without making predictions or inferences.We shall now learn more about descriptive statistics in the topic.</p>
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<h2>What is Descriptive Statistics?</h2>
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<h2>What is Descriptive Statistics?</h2>
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<p>Descriptive<a>statistics</a>is a branch of statistics that is used and also focuses on summarizing and organizing<a>data</a>to make it easily interpretable. It involves different measures that each describes the data like<a>measures of central tendency</a>,<a>measures of dispersion</a>and measures of<a>frequency distribution</a>. </p>
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<p>Descriptive<a>statistics</a>is a branch of statistics that is used and also focuses on summarizing and organizing<a>data</a>to make it easily interpretable. It involves different measures that each describes the data like<a>measures of central tendency</a>,<a>measures of dispersion</a>and measures of<a>frequency distribution</a>. </p>
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<p>Descriptive statistics can be categorized into three types: </p>
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<p>Descriptive statistics can be categorized into three types: </p>
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<ol><li>Measures of Central Tendency </li>
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<ol><li>Measures of Central Tendency </li>
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<li>Measures of Variability </li>
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<li>Measures of Variability </li>
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<li>Measures of Frequency Distribution</li>
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<li>Measures of Frequency Distribution</li>
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</ol><p>Some key takeaways are: </p>
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</ol><p>Some key takeaways are: </p>
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<ul><li>Descriptive statistics summarizes or describes the characteristics of a dataset. </li>
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<ul><li>Descriptive statistics summarizes or describes the characteristics of a dataset. </li>
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<li>Descriptive statistics consists of three basic categories of measurements: measures of variability, measures of central tendency and measures of frequency distributions. </li>
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<li>Descriptive statistics consists of three basic categories of measurements: measures of variability, measures of central tendency and measures of frequency distributions. </li>
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<li>Measures of central tendency include<a>mean</a>,<a>median</a>, and mode. </li>
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<li>Measures of central tendency include<a>mean</a>,<a>median</a>, and mode. </li>
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<li>Measures of variability include range, variance, standard deviation. </li>
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<li>Measures of variability include range, variance, standard deviation. </li>
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<li>Measures of frequency distribution include the occurrence of data, frequency counts, class intervals.</li>
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<li>Measures of frequency distribution include the occurrence of data, frequency counts, class intervals.</li>
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</ul><h2>Measures of Central Tendency</h2>
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</ul><h2>Measures of Central Tendency</h2>
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<p>We use the measures of central tendency to describe the center or<a>average</a>of a data<a>set</a>. The types of measures that are used to measure the central tendency are: </p>
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<p>We use the measures of central tendency to describe the center or<a>average</a>of a data<a>set</a>. The types of measures that are used to measure the central tendency are: </p>
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<ul><li><strong>Mean:</strong>Mean is the average of all the values in the data set. We calculate the mean by adding up all the values and dividing the result by the<a>number</a>of observations<p>Mean for ungrouped data can be given as, </p>
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<ul><li><strong>Mean:</strong>Mean is the average of all the values in the data set. We calculate the mean by adding up all the values and dividing the result by the<a>number</a>of observations<p>Mean for ungrouped data can be given as, </p>
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<p>\(\bar{x} = \frac{\sum_{i=1}^{n} x_i}{n}\)</p>
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<p>\(\bar{x} = \frac{\sum_{i=1}^{n} x_i}{n}\)</p>
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<p>Mean for grouped data can be given as, </p>
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<p>Mean for grouped data can be given as, </p>
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<p>\(\bar{x} = \frac{\sum_{i=1}^{n} M_i f_i}{\sum_{i=1}^{n} f_i}\)</p>
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<p>\(\bar{x} = \frac{\sum_{i=1}^{n} M_i f_i}{\sum_{i=1}^{n} f_i}\)</p>
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<p>Here, \(x_i\) is the \(i^{th}\) observation, \(m_i\) is the midpoint of the \(i^{th}\) interval, \(f_i\) is the corresponding frequency and n is the sample size.</p>
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<p>Here, \(x_i\) is the \(i^{th}\) observation, \(m_i\) is the midpoint of the \(i^{th}\) interval, \(f_i\) is the corresponding frequency and n is the sample size.</p>
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</li>
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</li>
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<li><strong>Median:</strong>Median is the middle value of an ordered data set. We calculate the median by finding the middle value of the dataset if the dataset is odd. If the dataset is even, then we average the middle values to get the median.<p>The median for ungrouped data, when n is odd, can be given as, </p>
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<li><strong>Median:</strong>Median is the middle value of an ordered data set. We calculate the median by finding the middle value of the dataset if the dataset is odd. If the dataset is even, then we average the middle values to get the median.<p>The median for ungrouped data, when n is odd, can be given as, </p>
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<p>\(\text{Median} = \left( \frac{n + 1}{2} \right)^\text{th} \text{ term} \)</p>
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<p>\(\text{Median} = \left( \frac{n + 1}{2} \right)^\text{th} \text{ term} \)</p>
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<p>The median for ungrouped data, when n is even, can be given as,</p>
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<p>The median for ungrouped data, when n is even, can be given as,</p>
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<p>\(\text{Median} = \frac{\left( \frac{n}{2} \right)^\text{th} \text{ term} + \left( \left( \frac{n}{2} + 1 \right)^\text{th} \text{ term} \right)}{2} \)</p>
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<p>\(\text{Median} = \frac{\left( \frac{n}{2} \right)^\text{th} \text{ term} + \left( \left( \frac{n}{2} + 1 \right)^\text{th} \text{ term} \right)}{2} \)</p>
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<p>The median for grouped data can be given as,</p>
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<p>The median for grouped data can be given as,</p>
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<p>\(\text{Median} = l + \left( \frac{\frac{n}{2} - c}{f} \right) h \)</p>
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<p>\(\text{Median} = l + \left( \frac{\frac{n}{2} - c}{f} \right) h \)</p>
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<p>l is the lower limit of the median class given by \(\frac{n}{2}\), c is the<a>cumulative frequency</a>, f is the frequency of the median class and h is the class height.</p>
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<p>l is the lower limit of the median class given by \(\frac{n}{2}\), c is the<a>cumulative frequency</a>, f is the frequency of the median class and h is the class height.</p>
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</li>
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</li>
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<li><strong>Mode:</strong>Mode is the most frequently occurring number in a dataset.<p>The<a>mode</a>for ungrouped data is given as, </p>
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<li><strong>Mode:</strong>Mode is the most frequently occurring number in a dataset.<p>The<a>mode</a>for ungrouped data is given as, </p>
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<p>\(\text{Mode} = \text{Most recurrent observation} \)</p>
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<p>\(\text{Mode} = \text{Most recurrent observation} \)</p>
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<p>The mode for grouped data is given as, </p>
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<p>The mode for grouped data is given as, </p>
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<p>\(\text{Mode} = L + h \left( \frac{f_m - f_1}{(f_m - f_1) + (f_m - f_2)} \right) \)</p>
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<p>\(\text{Mode} = L + h \left( \frac{f_m - f_1}{(f_m - f_1) + (f_m - f_2)} \right) \)</p>
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<p>L is the lower limit of the<a>modal class</a>, h is the class height, \(f_m\) is the frequency of the modal class, \(f_1\) is the frequency of the class preceding the modal class and \(f_2\) is the frequency of the class succeeding the modal class.</p>
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<p>L is the lower limit of the<a>modal class</a>, h is the class height, \(f_m\) is the frequency of the modal class, \(f_1\) is the frequency of the class preceding the modal class and \(f_2\) is the frequency of the class succeeding the modal class.</p>
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</li>
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</li>
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</ul><h2>Measures of Variability</h2>
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</ul><h2>Measures of Variability</h2>
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<p>We use different measures of variability to show how the data is spread or distributed. To check the spread or distribution of data, we use the following measures: </p>
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<p>We use different measures of variability to show how the data is spread or distributed. To check the spread or distribution of data, we use the following measures: </p>
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<ul><li><strong>Range:</strong>Range is the difference between the highest and lowest points in a given distribution.<p>The<a>formula</a>to find range is as given below:</p>
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<ul><li><strong>Range:</strong>Range is the difference between the highest and lowest points in a given distribution.<p>The<a>formula</a>to find range is as given below:</p>
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<p>\(\text{Range = H - S}\)</p>
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<p>\(\text{Range = H - S}\)</p>
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<p>Where, H is the highest value and S is the lowest value in a data set.</p>
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<p>Where, H is the highest value and S is the lowest value in a data set.</p>
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</li>
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</li>
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<li><strong>Variance:</strong>Variance measures how the data points differ from the mean. The formulas for<a>variance</a>is as given below:<p>The<a>sample variance</a>of grouped data can be given as, </p>
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<li><strong>Variance:</strong>Variance measures how the data points differ from the mean. The formulas for<a>variance</a>is as given below:<p>The<a>sample variance</a>of grouped data can be given as, </p>
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<p>\(s^{2} = \frac{\sum f (M_i - \bar{X})^{2}}{N - 1} \)</p>
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<p>\(s^{2} = \frac{\sum f (M_i - \bar{X})^{2}}{N - 1} \)</p>
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<p>The<a>population variance</a>of grouped data can be given as,</p>
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<p>The<a>population variance</a>of grouped data can be given as,</p>
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<p>\(\sigma^{2} = \frac{\sum f (M_i - \bar{X})^{2}}{N} \)</p>
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<p>\(\sigma^{2} = \frac{\sum f (M_i - \bar{X})^{2}}{N} \)</p>
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<p>The sample variance of ungrouped data is given as, </p>
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<p>The sample variance of ungrouped data is given as, </p>
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<p>\(s^{2} = \frac{\sum (X_i - \bar{X})^{2}}{n - 1}\)</p>
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<p>\(s^{2} = \frac{\sum (X_i - \bar{X})^{2}}{n - 1}\)</p>
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<p>The population variance of ungrouped data is given as,</p>
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<p>The population variance of ungrouped data is given as,</p>
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<p>\(\sigma^{2} = \frac{\sum (X_i - \bar{X})^{2}}{n} \)</p>
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<p>\(\sigma^{2} = \frac{\sum (X_i - \bar{X})^{2}}{n} \)</p>
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</li>
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</li>
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<li><strong>Standard deviation:</strong>Standard deviation measures the dispersion or spread of data points around the mean in the dataset.<p>The formula to find the<a>standard deviation</a>is given as, </p>
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<li><strong>Standard deviation:</strong>Standard deviation measures the dispersion or spread of data points around the mean in the dataset.<p>The formula to find the<a>standard deviation</a>is given as, </p>
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<p>\(\text{S.D.} = \sqrt{\text{Variance}} = \sigma \)</p>
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<p>\(\text{S.D.} = \sqrt{\text{Variance}} = \sigma \)</p>
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</li>
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</li>
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<li><strong>Interquartile range (IQR):</strong>IQR is the difference between the third quartile and the first quartile, showing the spread of the middle 50% of data.<p>\(\text{IQR} = Q_3 - Q_1 \)</p>
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<li><strong>Interquartile range (IQR):</strong>IQR is the difference between the third quartile and the first quartile, showing the spread of the middle 50% of data.<p>\(\text{IQR} = Q_3 - Q_1 \)</p>
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</li>
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</li>
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<h2>Measures of Frequency Distribution</h2>
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<h2>Measures of Frequency Distribution</h2>
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<p>A<a>frequency distribution table</a> helps summarize how data points are distributed across categories. The frequency table includes measures like:</p>
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<p>A<a>frequency distribution table</a> helps summarize how data points are distributed across categories. The frequency table includes measures like:</p>
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<p><strong>Data intervals:</strong>Data intervals, also known as classes or categories, are based on the range. This is useful for large datasets or continuous data.</p>
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<p><strong>Data intervals:</strong>Data intervals, also known as classes or categories, are based on the range. This is useful for large datasets or continuous data.</p>
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<p><strong>Frequency counts:</strong>The frequency counts, or “f,” is the number of times a data value appears in a dataset. It helps us in understanding how common or rare certain values are.</p>
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<p><strong>Frequency counts:</strong>The frequency counts, or “f,” is the number of times a data value appears in a dataset. It helps us in understanding how common or rare certain values are.</p>
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<p><strong>Relative frequency:</strong>The<a>relative frequency</a>is a<a>proportion</a>of the occurrences of a particular class relative to the total number of observations.</p>
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<p><strong>Relative frequency:</strong>The<a>relative frequency</a>is a<a>proportion</a>of the occurrences of a particular class relative to the total number of observations.</p>
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<p><strong>Cumulative frequency:</strong>It is the running total of frequencies up to a certain<a>class interval</a>. </p>
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<p><strong>Cumulative frequency:</strong>It is the running total of frequencies up to a certain<a>class interval</a>. </p>
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<h2>Difference Between Descriptive Statistics and Inferential Statistics</h2>
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<h2>Difference Between Descriptive Statistics and Inferential Statistics</h2>
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<p>There are a lot of differences between<a>descriptive and inferential statistics</a>. Let us now see the differences of descriptive statistics and inferential statistics in the given table mentioned below:</p>
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<p>There are a lot of differences between<a>descriptive and inferential statistics</a>. Let us now see the differences of descriptive statistics and inferential statistics in the given table mentioned below:</p>
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<strong>Descriptive Statistics</strong><strong>Inferential Statistics</strong>Descriptive statistics summarizes and organizes the data. Inferential statistics draws conclusions and makes predictions from data. Descriptive statistics uses complete data from a sample or population. It uses sample data to estimate population parameters. It uses measures of tendency (mean, median, mode), dispersion (range, variance, standard deviation), and frequency distributions. It uses<a>hypothesis testing</a>, confidence intervals,<a>regression</a>analysis, correlation, and<a>probability</a>distributions. We use graphs to visually represent the data. Statistical tests and models are used to visually represent the data. It is 100% accurate for the given data. It contains some uncertainty due to sampling errors.<h2>How to Represent Descriptive Statistics</h2>
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<strong>Descriptive Statistics</strong><strong>Inferential Statistics</strong>Descriptive statistics summarizes and organizes the data. Inferential statistics draws conclusions and makes predictions from data. Descriptive statistics uses complete data from a sample or population. It uses sample data to estimate population parameters. It uses measures of tendency (mean, median, mode), dispersion (range, variance, standard deviation), and frequency distributions. It uses<a>hypothesis testing</a>, confidence intervals,<a>regression</a>analysis, correlation, and<a>probability</a>distributions. We use graphs to visually represent the data. Statistical tests and models are used to visually represent the data. It is 100% accurate for the given data. It contains some uncertainty due to sampling errors.<h2>How to Represent Descriptive Statistics</h2>
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<p>There are various ways to represent descriptive statistics. Some of the ways are mentioned below:</p>
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<p>There are various ways to represent descriptive statistics. Some of the ways are mentioned below:</p>
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<p><strong>Numerical Representation:</strong> We use this method as it provides key measures to summarize data like mean, median, and mode. It also uses measures of dispersion like range, variance, and standard deviation. Descriptive data uses measures of position like percentiles and quartiles and z-scores.</p>
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<p><strong>Numerical Representation:</strong> We use this method as it provides key measures to summarize data like mean, median, and mode. It also uses measures of dispersion like range, variance, and standard deviation. Descriptive data uses measures of position like percentiles and quartiles and z-scores.</p>
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<p><strong>Tabular Representation:</strong> This type of representation is used to organize and summarize data for easy interpretation. We use the frequency distribution table, the grouped frequency table and the cumulative frequency table to represent the organized form of data.</p>
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<p><strong>Tabular Representation:</strong> This type of representation is used to organize and summarize data for easy interpretation. We use the frequency distribution table, the grouped frequency table and the cumulative frequency table to represent the organized form of data.</p>
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<p><strong>Graphical Representation: </strong>We use this type of representation to visualize the data and help us in finding patterns and trends. Some types of graphs used are bar charts, histograms, pie charts, box plots, scatter plots and line graphs.</p>
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<p><strong>Graphical Representation: </strong>We use this type of representation to visualize the data and help us in finding patterns and trends. Some types of graphs used are bar charts, histograms, pie charts, box plots, scatter plots and line graphs.</p>
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<h2>Tips and Tricks to Master Descriptive Statistics</h2>
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<h2>Tips and Tricks to Master Descriptive Statistics</h2>
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<p>Descriptive statistics help summarize and interpret large datasets in a simple way. Mastering them enables clear understanding and communication of data insights.</p>
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<p>Descriptive statistics help summarize and interpret large datasets in a simple way. Mastering them enables clear understanding and communication of data insights.</p>
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<ul><li>Understand the key measures like mean, median, mode, range, and standard deviation thoroughly. </li>
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<ul><li>Understand the key measures like mean, median, mode, range, and standard deviation thoroughly. </li>
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<li>Organize your data properly before calculating any descriptive statistics. </li>
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<li>Organize your data properly before calculating any descriptive statistics. </li>
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<li>Use visual tools like histograms, bar charts, and box plots to interpret data easily. </li>
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<li>Use visual tools like histograms, bar charts, and box plots to interpret data easily. </li>
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<li>Identify and handle outliers carefully, as they can affect the<a>accuracy</a>of results. </li>
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<li>Identify and handle outliers carefully, as they can affect the<a>accuracy</a>of results. </li>
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<li>Practice solving real-world problems to strengthen your<a>understanding of</a>data summarization. </li>
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<li>Practice solving real-world problems to strengthen your<a>understanding of</a>data summarization. </li>
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<li>Statistics are easier to understand when the data are meaningful. Therefore, teachers can use data that involves and engages students mentally, drawing on their daily life experiences. </li>
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<li>Statistics are easier to understand when the data are meaningful. Therefore, teachers can use data that involves and engages students mentally, drawing on their daily life experiences. </li>
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<li>Teachers can use whiteboard drawings, Excel or Google Sheets, and some histograms to explain the grouped and ungrouped data. Visuals make it easier for students to learn. </li>
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<li>Teachers can use whiteboard drawings, Excel or Google Sheets, and some histograms to explain the grouped and ungrouped data. Visuals make it easier for students to learn. </li>
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<li>Parents can help their children learn about mean, median, and mode by using 10 LEGO bricks of different heights to form a physical<a>bar graph</a>. </li>
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<li>Parents can help their children learn about mean, median, and mode by using 10 LEGO bricks of different heights to form a physical<a>bar graph</a>. </li>
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<li>Teachers can let students practice with fun activities such as conducting mini-surveys, mean-median-mode scavenger hunts, building box plots with string and markers, etc., which will help them learn faster.</li>
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<li>Teachers can let students practice with fun activities such as conducting mini-surveys, mean-median-mode scavenger hunts, building box plots with string and markers, etc., which will help them learn faster.</li>
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</ul><h2>Common mistakes and How to Avoid Them in Descriptive Statistics</h2>
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</ul><h2>Common mistakes and How to Avoid Them in Descriptive Statistics</h2>
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<p>Students tend to make mistakes when they solve problems related to descriptive statistics. Let us now see the common mistakes they make and the solutions to avoid them:</p>
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<p>Students tend to make mistakes when they solve problems related to descriptive statistics. Let us now see the common mistakes they make and the solutions to avoid them:</p>
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<h2>Real Life Applications of Descriptive Statistics</h2>
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<h2>Real Life Applications of Descriptive Statistics</h2>
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<p>Descriptive statistics are used to summarize and analyze data in various real-life situations. They help in identifying patterns, trends, and insights for better decision-making.</p>
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<p>Descriptive statistics are used to summarize and analyze data in various real-life situations. They help in identifying patterns, trends, and insights for better decision-making.</p>
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<ul><li><strong>Education:</strong>Schools use descriptive statistics to analyze students average scores and overall performance. </li>
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<ul><li><strong>Education:</strong>Schools use descriptive statistics to analyze students average scores and overall performance. </li>
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<li><strong>Business:</strong>Companies track sales data, customer preferences, and revenue trends to make informed decisions. </li>
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<li><strong>Business:</strong>Companies track sales data, customer preferences, and revenue trends to make informed decisions. </li>
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<li><strong>Healthcare:</strong>Hospitals analyze patient data such as age, recovery time, and test results to identify health patterns. </li>
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<li><strong>Healthcare:</strong>Hospitals analyze patient data such as age, recovery time, and test results to identify health patterns. </li>
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<li><strong>Sports:</strong>Coaches use averages and percentages to assess player performance and team efficiency. </li>
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<li><strong>Sports:</strong>Coaches use averages and percentages to assess player performance and team efficiency. </li>
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<li><strong>Government:</strong>Officials use descriptive statistics to summarize<a>census</a>data and monitor population trends.</li>
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<li><strong>Government:</strong>Officials use descriptive statistics to summarize<a>census</a>data and monitor population trends.</li>
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</ul><h3>Problem 1</h3>
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</ul><h3>Problem 1</h3>
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<p>Compute the mean of the data set: 5, 10, 15, 20, 25.</p>
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<p>Compute the mean of the data set: 5, 10, 15, 20, 25.</p>
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<p>Okay, lets begin</p>
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<p>Okay, lets begin</p>
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<p>The mean is 15.</p>
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<p>The mean is 15.</p>
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<h3>Explanation</h3>
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<h3>Explanation</h3>
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<p>Sum the values:</p>
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<p>Sum the values:</p>
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<p>5 + 10 + 15 + 20 + 25 = 75</p>
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<p>5 + 10 + 15 + 20 + 25 = 75</p>
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<p>Count the number of observations:</p>
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<p>Count the number of observations:</p>
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<p>5 values.</p>
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<p>5 values.</p>
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<p>Calculate the mean:</p>
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<p>Calculate the mean:</p>
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<p>Mean = 75/5 = 15</p>
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<p>Mean = 75/5 = 15</p>
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<p>The mean is the arithmetic average and represents the central tendency of the dataset.</p>
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<p>The mean is the arithmetic average and represents the central tendency of the dataset.</p>
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<p>Well explained 👍</p>
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<p>Well explained 👍</p>
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<h3>Problem 2</h3>
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<h3>Problem 2</h3>
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<p>Find the median of the dataset: 8, 3, 12, 7, 5.</p>
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<p>Find the median of the dataset: 8, 3, 12, 7, 5.</p>
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<p>Okay, lets begin</p>
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<p>Okay, lets begin</p>
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<p>The median is 7</p>
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<p>The median is 7</p>
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<h3>Explanation</h3>
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<h3>Explanation</h3>
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<p>Sort the data in ascending order:</p>
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<p>Sort the data in ascending order:</p>
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<p>[3, 5, 7, 8, 12]</p>
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<p>[3, 5, 7, 8, 12]</p>
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<p>Determine the middle value:</p>
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<p>Determine the middle value:</p>
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<p>As there are 5 observations, the middle value is the 3rd value.</p>
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<p>As there are 5 observations, the middle value is the 3rd value.</p>
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<p>Median = The 3rd value is 7.</p>
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<p>Median = The 3rd value is 7.</p>
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<p>Well explained 👍</p>
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<p>Well explained 👍</p>
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<h3>Problem 3</h3>
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<h3>Problem 3</h3>
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<p>Determine the mode of the dataset: 2, 4, 4, 6, 7, 4, 9.</p>
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<p>Determine the mode of the dataset: 2, 4, 4, 6, 7, 4, 9.</p>
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<p>Okay, lets begin</p>
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<p>Okay, lets begin</p>
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<p>The mode is 4.</p>
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<p>The mode is 4.</p>
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<h3>Explanation</h3>
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<h3>Explanation</h3>
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<p>Count the frequency of each value:</p>
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<p>Count the frequency of each value:</p>
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<p>2 appears once</p>
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<p>2 appears once</p>
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<p>4 appears three times</p>
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<p>4 appears three times</p>
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<p>6, 7, and 9 appear once each.</p>
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<p>6, 7, and 9 appear once each.</p>
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<p>Identify the value with the highest frequency:</p>
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<p>Identify the value with the highest frequency:</p>
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<p>The number 4 appears most frequently.</p>
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<p>The number 4 appears most frequently.</p>
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<p>Mode = 4.</p>
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<p>Mode = 4.</p>
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<p>Well explained 👍</p>
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<p>Well explained 👍</p>
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<h3>Problem 4</h3>
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<h3>Problem 4</h3>
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<p>Calculate the range of the data set: 12, 7, 9, 15, 10.</p>
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<p>Calculate the range of the data set: 12, 7, 9, 15, 10.</p>
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<p>Okay, lets begin</p>
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<p>Okay, lets begin</p>
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<p>The range is 8.</p>
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<p>The range is 8.</p>
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<h3>Explanation</h3>
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<h3>Explanation</h3>
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<p>Identify the minimum and maximum values:</p>
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<p>Identify the minimum and maximum values:</p>
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<p>Minimum = 7 and Maximum = 15</p>
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<p>Minimum = 7 and Maximum = 15</p>
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<p>Compute the range:</p>
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<p>Compute the range:</p>
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<p>Range = 15 - 7 = 8.</p>
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<p>Range = 15 - 7 = 8.</p>
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<p>Well explained 👍</p>
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<p>Well explained 👍</p>
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<h3>Problem 5</h3>
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<h3>Problem 5</h3>
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<p>Determine the quartiles and IQR for the dataset: 6, 7, 8, 10, 12, 15, 18, 20, 22.</p>
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<p>Determine the quartiles and IQR for the dataset: 6, 7, 8, 10, 12, 15, 18, 20, 22.</p>
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<p>Okay, lets begin</p>
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<p>Okay, lets begin</p>
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<p>Q1 = 7.5, Median = 12, Q3 = 19 and IQR = 11.5</p>
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<p>Q1 = 7.5, Median = 12, Q3 = 19 and IQR = 11.5</p>
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<h3>Explanation</h3>
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<h3>Explanation</h3>
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<p>Sort the data:</p>
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<p>Sort the data:</p>
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<p>[6, 7, 8, 10, 12, 15, 18, 20, 22]. (already sorted).</p>
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<p>[6, 7, 8, 10, 12, 15, 18, 20, 22]. (already sorted).</p>
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<p>Find the median (Q2):</p>
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<p>Find the median (Q2):</p>
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<p>The 5th value = 12</p>
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<p>The 5th value = 12</p>
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<p>Determine Q1 (low quartile):</p>
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<p>Determine Q1 (low quartile):</p>
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<p>Lower half: [6, 7, 8, 10] → Q1 = (7+8)/2 = 7.5</p>
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<p>Lower half: [6, 7, 8, 10] → Q1 = (7+8)/2 = 7.5</p>
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<p>Determine Q3 (upper quartile):</p>
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<p>Determine Q3 (upper quartile):</p>
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<p>Upper half: [15, 18, 20, 22] → Q3 = (18+20)/2 = 19</p>
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<p>Upper half: [15, 18, 20, 22] → Q3 = (18+20)/2 = 19</p>
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<p>Calculate the IQR:</p>
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<p>Calculate the IQR:</p>
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<p> IQR = Q3 - Q1 = 19 - 7.5 = 11.5.</p>
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<p> IQR = Q3 - Q1 = 19 - 7.5 = 11.5.</p>
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<p>Well explained 👍</p>
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<p>Well explained 👍</p>
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<h2>FAQs on Descriptive Statistics</h2>
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<h2>FAQs on Descriptive Statistics</h2>
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<h3>1.What are descriptive statistics?</h3>
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<h3>1.What are descriptive statistics?</h3>
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<p>Descriptive statistics is the branch of statistics which involves summarizing, organizing and presenting data in an informative manner. This also involves the usage of numerical measures and graphical representations.</p>
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<p>Descriptive statistics is the branch of statistics which involves summarizing, organizing and presenting data in an informative manner. This also involves the usage of numerical measures and graphical representations.</p>
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<h3>2.What are the main types of descriptive statistics?</h3>
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<h3>2.What are the main types of descriptive statistics?</h3>
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<p>The main types of descriptive statistics include; the measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation, interquartile range) and shape descriptors (skewness and kurtosis)</p>
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<p>The main types of descriptive statistics include; the measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation, interquartile range) and shape descriptors (skewness and kurtosis)</p>
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<h3>3.How do descriptive and inferential statistics differ?</h3>
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<h3>3.How do descriptive and inferential statistics differ?</h3>
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<p>The difference is that descriptive statistics summarize data from a sample or a population, whereas inferential statistics use sample data to make generalizations or predictions about a larger population. </p>
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<p>The difference is that descriptive statistics summarize data from a sample or a population, whereas inferential statistics use sample data to make generalizations or predictions about a larger population. </p>
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<h3>4.What is mean?</h3>
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<h3>4.What is mean?</h3>
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<p>Mean is the<a>arithmetic average</a>of a data set. It is calculated by summing all the values and then dividing it by the number of observations.</p>
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<p>Mean is the<a>arithmetic average</a>of a data set. It is calculated by summing all the values and then dividing it by the number of observations.</p>
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<h3>5.What is mode?</h3>
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<h3>5.What is mode?</h3>
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<p>Mode is the value that appears most frequently in a data set.</p>
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<p>Mode is the value that appears most frequently in a data set.</p>
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<h2>Jaipreet Kour Wazir</h2>
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<h2>Jaipreet Kour Wazir</h2>
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<h3>About the Author</h3>
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<h3>About the Author</h3>
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<p>Jaipreet Kour Wazir is a data wizard with over 5 years of expertise in simplifying complex data concepts. From crunching numbers to crafting insightful visualizations, she turns raw data into compelling stories. Her journey from analytics to education ref</p>
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<p>Jaipreet Kour Wazir is a data wizard with over 5 years of expertise in simplifying complex data concepts. From crunching numbers to crafting insightful visualizations, she turns raw data into compelling stories. Her journey from analytics to education ref</p>
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<h3>Fun Fact</h3>
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<h3>Fun Fact</h3>
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<p>: She compares datasets to puzzle games-the more you play with them, the clearer the picture becomes!</p>
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<p>: She compares datasets to puzzle games-the more you play with them, the clearer the picture becomes!</p>