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<p>Last updated on<strong>November 18, 2025</strong></p>
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<p>Last updated on<strong>November 18, 2025</strong></p>
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<p>Variance and standard deviation help us see whether a group of numbers stays close together or spreads out, just like checking whether friends stand in a tight group or scatter around the playground.</p>
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<p>Variance and standard deviation help us see whether a group of numbers stays close together or spreads out, just like checking whether friends stand in a tight group or scatter around the playground.</p>
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<h2>What is Standard Deviation?</h2>
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<h2>What is Standard Deviation?</h2>
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<p>Standard deviation is a measure<a>of</a>how far the values in a<a>data</a><a>set</a>are from the<a>mean</a>. In simple<a>terms</a>, it tells us whether the data points are close to the<a>average</a>or widely scattered. A slight standard deviation means the values are close to the mean, while a large one means the values are more spread out. This idea is used in<a>descriptive statistics</a>to understand variation within a sample, a population, or a probability distribution.</p>
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<p>Standard deviation is a measure<a>of</a>how far the values in a<a>data</a><a>set</a>are from the<a>mean</a>. In simple<a>terms</a>, it tells us whether the data points are close to the<a>average</a>or widely scattered. A slight standard deviation means the values are close to the mean, while a large one means the values are more spread out. This idea is used in<a>descriptive statistics</a>to understand variation within a sample, a population, or a probability distribution.</p>
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<p>To calculate standard deviation, we first look at how each value differs from the mean. If we have n observations \(x_1,x_2,...,x_n\). We find the squared differences from the mean and add them: \(\ \sum_{i=1}^{n} (x_i - \bar{x})^{2} \ \). </p>
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<p>To calculate standard deviation, we first look at how each value differs from the mean. If we have n observations \(x_1,x_2,...,x_n\). We find the squared differences from the mean and add them: \(\ \sum_{i=1}^{n} (x_i - \bar{x})^{2} \ \). </p>
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<p>This sum shows how much the data varies. A small sum means low dispersion, while a large sum means high dispersion. Because the sum of squared deviations alone is not a complete measure, we take its square root. This gives us the standard deviation formula, also known as the standard deviation equation, which is the square root of variance. This value is represented using the standard deviation symbol (σ for population, s for sample).</p>
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<p>This sum shows how much the data varies. A small sum means low dispersion, while a large sum means high dispersion. Because the sum of squared deviations alone is not a complete measure, we take its square root. This gives us the standard deviation formula, also known as the standard deviation equation, which is the square root of variance. This value is represented using the standard deviation symbol (σ for population, s for sample).</p>
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<p>Understanding variance vs. standard deviation helps us see why taking the square root gives a clearer picture of how spread out the data is. Whether you're learning how to find standard deviation, how to calculate standard deviation, or specifically how to find sample standard deviation, these steps remain essential. Standard deviation is often displayed in a standard deviation chart or computed using tools like a standard deviation calculator.</p>
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<p>Understanding variance vs. standard deviation helps us see why taking the square root gives a clearer picture of how spread out the data is. Whether you're learning how to find standard deviation, how to calculate standard deviation, or specifically how to find sample standard deviation, these steps remain essential. Standard deviation is often displayed in a standard deviation chart or computed using tools like a standard deviation calculator.</p>
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<h2>Difference Between Variance and Standard Deviation</h2>
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<h2>Difference Between Variance and Standard Deviation</h2>
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<p>To understand and measure the risk, consistency, and distribution of data, the measures of<a>variance</a>and standard deviations are employed in the fields of finance, accounting, and<a>statistics</a>. They are used to calculate the deviation of the values from their mean and assess the<a>spread of data</a>. Some of the main differences between these two fundamental measurements are listed below: </p>
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<p>To understand and measure the risk, consistency, and distribution of data, the measures of<a>variance</a>and standard deviations are employed in the fields of finance, accounting, and<a>statistics</a>. They are used to calculate the deviation of the values from their mean and assess the<a>spread of data</a>. Some of the main differences between these two fundamental measurements are listed below: </p>
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<strong> Variance</strong><strong> Standard Deviation</strong>Variance tells us how much the<a>numbers</a>in a group change from one another. Standard deviation shows how spread out the numbers are in the same units as the data. It is the average of the squared differences from the mean. It is the<a>square</a>root of the variance. Variance is written in squared units. Standard deviation is written in the same units as the original data. It is represented as \(σ^2\). It is represented as σ. Variance helps describe how far individual values are from the group’s average. Standard deviation helps understand how tightly or loosely the numbers are grouped.<h2>Standard Deviation Formula</h2>
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<strong> Variance</strong><strong> Standard Deviation</strong>Variance tells us how much the<a>numbers</a>in a group change from one another. Standard deviation shows how spread out the numbers are in the same units as the data. It is the average of the squared differences from the mean. It is the<a>square</a>root of the variance. Variance is written in squared units. Standard deviation is written in the same units as the original data. It is represented as \(σ^2\). It is represented as σ. Variance helps describe how far individual values are from the group’s average. Standard deviation helps understand how tightly or loosely the numbers are grouped.<h2>Standard Deviation Formula</h2>
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<p>Standard deviation helps us measure how spread out the values in a data set are. It shows how far the data points move away from the average. This idea is linked to dispersion, which tells us how much the numbers vary within a group. Variance represents the average of the squared distances between each value and the mean, while standard deviation shows how much the data values spread out around that mean. To calculate the standard deviation, we use two<a>formulas</a>: one for a sample and one for an entire population.</p>
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<p>Standard deviation helps us measure how spread out the values in a data set are. It shows how far the data points move away from the average. This idea is linked to dispersion, which tells us how much the numbers vary within a group. Variance represents the average of the squared distances between each value and the mean, while standard deviation shows how much the data values spread out around that mean. To calculate the standard deviation, we use two<a>formulas</a>: one for a sample and one for an entire population.</p>
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<p><strong>Population:</strong></p>
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<p><strong>Population:</strong></p>
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<p><strong>\(\ \sigma = \frac{\sum (X - \mu)^2}{N} \ \)</strong></p>
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<p><strong>\(\ \sigma = \frac{\sum (X - \mu)^2}{N} \ \)</strong></p>
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<p>X - The value in the data distribution μ - The population Mean N - Total number of observations</p>
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<p>X - The value in the data distribution μ - The population Mean N - Total number of observations</p>
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<p><strong>Sample:</strong></p>
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<p><strong>Sample:</strong></p>
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<p><strong>\(\ \sigma = \frac{\sum (X - \mu)^2}{N} \ \)</strong></p>
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<p><strong>\(\ \sigma = \frac{\sum (X - \mu)^2}{N} \ \)</strong></p>
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<p>X - The value in the data distribution x - The sample mean n - Total number of observations</p>
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<p>X - The value in the data distribution x - The sample mean n - Total number of observations</p>
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<p>Notice that both formulas are nearly the same, except for the<a>denominator</a>: the population standard deviation uses N, while the sample standard deviation uses n-1. When we calculate a sample mean, we do not use every value from the full population, so the sample mean is only an estimate of the actual population mean. This introduces some uncertainty or bias into the calculation. To fix this, we use n-1 instead of n in the sample formula. This adjustment is called Bessel’s correction.</p>
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<p>Notice that both formulas are nearly the same, except for the<a>denominator</a>: the population standard deviation uses N, while the sample standard deviation uses n-1. When we calculate a sample mean, we do not use every value from the full population, so the sample mean is only an estimate of the actual population mean. This introduces some uncertainty or bias into the calculation. To fix this, we use n-1 instead of n in the sample formula. This adjustment is called Bessel’s correction.</p>
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<h2>Standard Deviation for Ungrouped Data</h2>
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<h2>Standard Deviation for Ungrouped Data</h2>
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<p>When data values are not arranged in groups, we measure how much each value deviates from the average. There are three primary methods to calculate standard deviation: </p>
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<p>When data values are not arranged in groups, we measure how much each value deviates from the average. There are three primary methods to calculate standard deviation: </p>
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<ul><li>Actual mean method</li>
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<ul><li>Actual mean method</li>
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<li>Assumed mean method</li>
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<li>Assumed mean method</li>
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<li>Step deviation method </li>
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<li>Step deviation method </li>
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</ul><p><strong>Actual mean method</strong>In this method, we first find the actual mean (x) and then calculate how far each value is from it. </p>
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</ul><p><strong>Actual mean method</strong>In this method, we first find the actual mean (x) and then calculate how far each value is from it. </p>
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<p><strong>Formula:</strong></p>
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<p><strong>Formula:</strong></p>
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<p><strong>\(\ \sigma = \sqrt{ \frac{\sum (X - \mu)^2}{N} } \ \)</strong></p>
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<p><strong>\(\ \sigma = \sqrt{ \frac{\sum (X - \mu)^2}{N} } \ \)</strong></p>
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<p>For example, Data: 3, 2, 5, 6 Mean = \((3 + 2 + 5 + 6) ÷ 4 = 4\) </p>
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<p>For example, Data: 3, 2, 5, 6 Mean = \((3 + 2 + 5 + 6) ÷ 4 = 4\) </p>
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<p>Squared differences: \((4-3)2+(2-4)2+(5-4)2+(6-4)2=10\) Variance = 104 = 2.5 Standard deviation = 2.5 = 1.58 </p>
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<p>Squared differences: \((4-3)2+(2-4)2+(5-4)2+(6-4)2=10\) Variance = 104 = 2.5 Standard deviation = 2.5 = 1.58 </p>
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<p><strong>Assumed mean method</strong>When the values are large or complex to handle, we pick a simple value A as the assumed mean. </p>
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<p><strong>Assumed mean method</strong>When the values are large or complex to handle, we pick a simple value A as the assumed mean. </p>
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<p>Let \(d = x-A\) </p>
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<p>Let \(d = x-A\) </p>
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<p><strong>Formula:</strong></p>
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<p><strong>Formula:</strong></p>
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<p><strong>\(\ \sigma = \frac{\sum (X - \mu)^{2}}{N} \ \)</strong></p>
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<p><strong>\(\ \sigma = \frac{\sum (X - \mu)^{2}}{N} \ \)</strong></p>
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<p><strong>Step deviation method</strong>Used when data has a<a>common factor</a>to simplify calculations. Let</p>
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<p><strong>Step deviation method</strong>Used when data has a<a>common factor</a>to simplify calculations. Let</p>
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<ul><li>A = assumed mean</li>
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<ul><li>A = assumed mean</li>
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<li>\(d = x - A\)</li>
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<li>\(d = x - A\)</li>
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<li>\(d’ = di'\), where<a>i</a>is a common factor </li>
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<li>\(d’ = di'\), where<a>i</a>is a common factor </li>
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</ul><p><strong>Formula:</strong></p>
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</ul><p><strong>Formula:</strong></p>
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<p><strong>\(\ \sigma = \sqrt{\frac{\sum (X - \mu)^2}{N}} \ \)</strong> </p>
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<p><strong>\(\ \sigma = \sqrt{\frac{\sum (X - \mu)^2}{N}} \ \)</strong> </p>
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<p><strong>Standard Deviation for Grouped Data </strong>For grouped data, we first prepare a frequency table, then use the same three methods: </p>
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<p><strong>Standard Deviation for Grouped Data </strong>For grouped data, we first prepare a frequency table, then use the same three methods: </p>
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<ul><li>Actual Mean Method</li>
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<ul><li>Actual Mean Method</li>
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<li>Assumed Mean Method</li>
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<li>Assumed Mean Method</li>
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<li>Step Deviation Method </li>
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<li>Step Deviation Method </li>
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</ul><p>These methods work the same way as for ungrouped data, except that frequencies are included in each calculation.</p>
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</ul><p>These methods work the same way as for ungrouped data, except that frequencies are included in each calculation.</p>
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<h2>Tips and Tricks to Master Standard Deviation</h2>
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<h2>Tips and Tricks to Master Standard Deviation</h2>
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<p>Standard deviation measures how much data values differ from the mean. Understanding it helps students interpret data variability and make sense of real-world statistics.</p>
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<p>Standard deviation measures how much data values differ from the mean. Understanding it helps students interpret data variability and make sense of real-world statistics.</p>
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<ul><li>Think of standard deviation as a measure of how spread out the data is the higher it is, the more the values differ from the mean.</li>
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<ul><li>Think of standard deviation as a measure of how spread out the data is the higher it is, the more the values differ from the mean.</li>
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<li>Always start by finding the mean before calculating deviations; this makes the process simpler and organized.</li>
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<li>Always start by finding the mean before calculating deviations; this makes the process simpler and organized.</li>
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<li>Remember the formula: \(√(Σ(x - x̄)² / N)\) for population and \(√(Σ(x - x̄)² / (N - 1))\) for sample, practice both regularly.</li>
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<li>Remember the formula: \(√(Σ(x - x̄)² / N)\) for population and \(√(Σ(x - x̄)² / (N - 1))\) for sample, practice both regularly.</li>
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<li>Visualize data using graphs; seeing how far points lie from the mean helps in understanding variation better.</li>
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<li>Visualize data using graphs; seeing how far points lie from the mean helps in understanding variation better.</li>
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<li>Practice with small data sets first to get comfortable, then move to real-life examples like test scores or daily temperatures.</li>
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<li>Practice with small data sets first to get comfortable, then move to real-life examples like test scores or daily temperatures.</li>
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<li>Children think of standard deviation as how “spread out” your numbers are, just like friends standing close together or far apart on a playground.</li>
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<li>Children think of standard deviation as how “spread out” your numbers are, just like friends standing close together or far apart on a playground.</li>
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<li>Teachers can help students find the mean first; this builds a strong foundation before moving to deviations and formulas. </li>
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<li>Teachers can help students find the mean first; this builds a strong foundation before moving to deviations and formulas. </li>
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<li>Parents should encourage their children to practice with small, simple data sets at home so they become comfortable before handling larger numbers.</li>
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<li>Parents should encourage their children to practice with small, simple data sets at home so they become comfortable before handling larger numbers.</li>
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</ul><h2>Common Mistakes and How to Avoid Them in Variance and Standard Deviation</h2>
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</ul><h2>Common Mistakes and How to Avoid Them in Variance and Standard Deviation</h2>
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<p>Variance and standard deviation play an important role in measuring the deviation and spread of data in a given dataset. However, students make some errors during their calculations. Understanding these mistakes helps make the process less prone to errors. </p>
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<p>Variance and standard deviation play an important role in measuring the deviation and spread of data in a given dataset. However, students make some errors during their calculations. Understanding these mistakes helps make the process less prone to errors. </p>
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<h2>Real-Life Applications of Variance and Standard Deviation</h2>
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<h2>Real-Life Applications of Variance and Standard Deviation</h2>
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<p>The real-world applications of variance and standard deviation are countless. They help measure the spread and deviation of the given data from its average or mean.</p>
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<p>The real-world applications of variance and standard deviation are countless. They help measure the spread and deviation of the given data from its average or mean.</p>
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<ul><li>Standard deviation is a tool used by investors and finance professionals to assess stock price volatility. Bigger risk and possible<a>profit</a>are indicated by a bigger standard deviation. By selecting assets with a low<a>correlation</a>to lower risk, variance assists in investment balancing.</li>
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<ul><li>Standard deviation is a tool used by investors and finance professionals to assess stock price volatility. Bigger risk and possible<a>profit</a>are indicated by a bigger standard deviation. By selecting assets with a low<a>correlation</a>to lower risk, variance assists in investment balancing.</li>
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<li>In the field of manufacturing and production, it is used to guarantee consistency. Businesses track variations in<a>product</a>parameters (such as bottle sizes and smartphone battery life) so that they can use these fundamental measures. Also, these measures assist in detecting production line variances in order to uphold quality requirements.</li>
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<li>In the field of manufacturing and production, it is used to guarantee consistency. Businesses track variations in<a>product</a>parameters (such as bottle sizes and smartphone battery life) so that they can use these fundamental measures. Also, these measures assist in detecting production line variances in order to uphold quality requirements.</li>
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<li>To comprehend the diversity of patient responses to treatments, standard deviation is employed in clinical trials. Additionally, to identify abnormalities, variance analysis is used to examine blood pressure, blood sugar, and other health indicators.</li>
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<li>To comprehend the diversity of patient responses to treatments, standard deviation is employed in clinical trials. Additionally, to identify abnormalities, variance analysis is used to examine blood pressure, blood sugar, and other health indicators.</li>
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<li>To establish grading curves and identify the distribution of student scores, schools rely on standard deviation. Also, variance contributes to determining how different populations vary in terms of intelligence or skill levels.</li>
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<li>To establish grading curves and identify the distribution of student scores, schools rely on standard deviation. Also, variance contributes to determining how different populations vary in terms of intelligence or skill levels.</li>
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<li>Standard deviation assists in the analysis of a player’s performance consistency (e.g., shooting<a>accuracy</a>in basketball, batting average in cricket, etc.) Whether a team performs consistently well or fluctuates in performance can be assessed by the variation in scores across games. </li>
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<li>Standard deviation assists in the analysis of a player’s performance consistency (e.g., shooting<a>accuracy</a>in basketball, batting average in cricket, etc.) Whether a team performs consistently well or fluctuates in performance can be assessed by the variation in scores across games. </li>
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</ul><h3>Problem 1</h3>
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</ul><h3>Problem 1</h3>
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<p>The weights of 5 students in a class are: 28, 30, 32, 34, and 36 kilograms. Find the variance and standard deviation.</p>
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<p>The weights of 5 students in a class are: 28, 30, 32, 34, and 36 kilograms. Find the variance and standard deviation.</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 variance is 8 and the standard deviation is approximately 2.83. </p>
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<p>The variance is 8 and the standard deviation is approximately 2.83. </p>
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<h3>Explanation</h3>
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<h3>Explanation</h3>
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<p>Here, we have to find the mean first.</p>
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<p>Here, we have to find the mean first.</p>
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<p>\(\ \text{Mean} = \frac{\text{Sum of all values}}{\text{Total number of values}} \ \)</p>
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<p>\(\ \text{Mean} = \frac{\text{Sum of all values}}{\text{Total number of values}} \ \)</p>
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<p>\(Mean = (28 + 30 + 32 + 34 + 36) / 5 = 160 / 5 = 32\)</p>
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<p>\(Mean = (28 + 30 + 32 + 34 + 36) / 5 = 160 / 5 = 32\)</p>
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<p>Therefore, 32 is the mean. </p>
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<p>Therefore, 32 is the mean. </p>
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<p>Find each value’s deviation from the mean \((x_i - μ)\):</p>
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<p>Find each value’s deviation from the mean \((x_i - μ)\):</p>
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<p>\((28 - 32 = -4) (30 - 32 = -2) (32 - 32 = 0) (34 - 32 = 2) (36 - 32 = 4)\).</p>
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<p>\((28 - 32 = -4) (30 - 32 = -2) (32 - 32 = 0) (34 - 32 = 2) (36 - 32 = 4)\).</p>
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<p>Square each deviation: </p>
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<p>Square each deviation: </p>
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<p>\((-4)^2 = 16; (-2)^2 = 4; 0^2 = 0; 2^2 = 4; 4^2 = 16\)</p>
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<p>\((-4)^2 = 16; (-2)^2 = 4; 0^2 = 0; 2^2 = 4; 4^2 = 16\)</p>
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<p>Calculate the variance using the formula:</p>
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<p>Calculate the variance using the formula:</p>
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<p>\(\sigma^{2} = \frac{\sum_{i=1}^{N} (x_{i} - \mu)^{2}}{N}\)</p>
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<p>\(\sigma^{2} = \frac{\sum_{i=1}^{N} (x_{i} - \mu)^{2}}{N}\)</p>
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<p>\(σ² = (16 + 4 + 0 + 4 + 16) / 5 = 40 / 5 = 8\)</p>
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<p>\(σ² = (16 + 4 + 0 + 4 + 16) / 5 = 40 / 5 = 8\)</p>
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<p>So the variance is 8. </p>
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<p>So the variance is 8. </p>
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<p>Find the standard deviation by taking the square root of the variance:</p>
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<p>Find the standard deviation by taking the square root of the variance:</p>
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<p>Standard deviation = √Variance </p>
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<p>Standard deviation = √Variance </p>
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<p>\(√8 = 2.83\)</p>
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<p>\(√8 = 2.83\)</p>
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<p>Thus, the standard deviation is approximately 2.83. </p>
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<p>Thus, the standard deviation is approximately 2.83. </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 variance of the given numbers: 2, 4, 6, 8, 10.</p>
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<p>Find the variance of the given numbers: 2, 4, 6, 8, 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>8 is the variance. </p>
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<p>8 is the variance. </p>
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<h3>Explanation</h3>
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<h3>Explanation</h3>
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<p>To find the variance, first we have to find the mean. </p>
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<p>To find the variance, first we have to find the mean. </p>
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<p>\(Mean = (2 + 4 + 6 + 8 + 10) / 5 \)</p>
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<p>\(Mean = (2 + 4 + 6 + 8 + 10) / 5 \)</p>
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<p>= \(\frac{30}{5}\) = 6</p>
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<p>= \(\frac{30}{5}\) = 6</p>
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<p>Next, find each number’s deviation from the mean and square it.</p>
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<p>Next, find each number’s deviation from the mean and square it.</p>
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<p>For the numbers, the deviation can be calculated by (x - Mean)</p>
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<p>For the numbers, the deviation can be calculated by (x - Mean)</p>
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<p>\(Mean = (2 + 4 + 6 + 8 + 10) / 5 \)</p>
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<p>\(Mean = (2 + 4 + 6 + 8 + 10) / 5 \)</p>
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<p>Then, we can find the squared deviation (x - Mean)2:</p>
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<p>Then, we can find the squared deviation (x - Mean)2:</p>
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<p>\((-4)^2 = 16\)</p>
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<p>\((-4)^2 = 16\)</p>
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<p>\((-2)^2 = 4\)</p>
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<p>\((-2)^2 = 4\)</p>
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<p>\(0^2 = 0\)</p>
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<p>\(0^2 = 0\)</p>
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<p>\(2^2 = 4\)</p>
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<p>\(2^2 = 4\)</p>
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<p>\(4^2 = 16\)</p>
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<p>\(4^2 = 16\)</p>
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<p>Now, we can find the variance by using the formula: </p>
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<p>Now, we can find the variance by using the formula: </p>
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<p>\(\sigma^{2} = \frac{\sum_{i=1}^{N} (x_{i} - \mu)^{2}}{N}\)</p>
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<p>\(\sigma^{2} = \frac{\sum_{i=1}^{N} (x_{i} - \mu)^{2}}{N}\)</p>
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<p>\(σ² = (16 + 4 + 0 + 4 + 16) / 5 \)</p>
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<p>\(σ² = (16 + 4 + 0 + 4 + 16) / 5 \)</p>
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<p>=\( 40 / 5 = 8 \). </p>
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<p>=\( 40 / 5 = 8 \). </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>The heights (in cm) of 3 students in a class are: 150, 160, 170. Find the variance and standard deviation.</p>
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<p>The heights (in cm) of 3 students in a class are: 150, 160, 170. Find the variance and standard deviation.</p>
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<p>Okay, lets begin</p>
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<p>Okay, lets begin</p>
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<p>Variance (𝜎²) = 66.67.</p>
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<p>Variance (𝜎²) = 66.67.</p>
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<p>Standard Deviation (𝜎) = 8.165 cm. </p>
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<p>Standard Deviation (𝜎) = 8.165 cm. </p>
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<h3>Explanation</h3>
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<h3>Explanation</h3>
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<p> Find the mean. </p>
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<p> Find the mean. </p>
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<p> Mean = Sum of all values / Total number of values</p>
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<p> Mean = Sum of all values / Total number of values</p>
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<p>\(Mean = (150 + 160 + 170) / 3 = 480 / 3 = 160\)</p>
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<p>\(Mean = (150 + 160 + 170) / 3 = 480 / 3 = 160\)</p>
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<p>So, 160 is the mean height.</p>
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<p>So, 160 is the mean height.</p>
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<p>Next, we can calculate the squared differences from the mean. </p>
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<p>Next, we can calculate the squared differences from the mean. </p>
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<p>The formula for finding variance is:</p>
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<p>The formula for finding variance is:</p>
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<p>\(\sigma^{2} = \frac{\sum_{i=1}^{N} (x_{i} - \mu)^{2}}{N}\)</p>
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<p>\(\sigma^{2} = \frac{\sum_{i=1}^{N} (x_{i} - \mu)^{2}}{N}\)</p>
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<p>Here, we have to find the \((xi - μ) and (xi - μ)^2\): </p>
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<p>Here, we have to find the \((xi - μ) and (xi - μ)^2\): </p>
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<p>\( (xi - μ) = (150 - 160 = -10); (160 - 160 = 0); (170 - 160 = 10)\)</p>
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<p>\( (xi - μ) = (150 - 160 = -10); (160 - 160 = 0); (170 - 160 = 10)\)</p>
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<p>\((xi - μ)^2 = (-10)^2 = 100; 0^2 = 0; (10)^2 = 100\)</p>
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<p>\((xi - μ)^2 = (-10)^2 = 100; 0^2 = 0; (10)^2 = 100\)</p>
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<p>\(Variance = σ² = ∑Ni = 1(xi - μ)^2 / N \)</p>
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<p>\(Variance = σ² = ∑Ni = 1(xi - μ)^2 / N \)</p>
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<p>\(100 + 0 + 100 = 200\)</p>
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<p>\(100 + 0 + 100 = 200\)</p>
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<p>\(So, 200 / 3 = 66.67\)</p>
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<p>\(So, 200 / 3 = 66.67\)</p>
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<p>Now, we can calculate the standard deviation: </p>
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<p>Now, we can calculate the standard deviation: </p>
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<p>Standard deviation = √Variance </p>
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<p>Standard deviation = √Variance </p>
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<p>= √66.667 \(\approx \) 8.167</p>
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<p>= √66.667 \(\approx \) 8.167</p>
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<p>So, the standard deviation is 8.17 cm.</p>
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<p>So, the standard deviation is 8.17 cm.</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>2 friends took a math test, and their scores were 90 and 95. How much do their scores vary from the average score?</p>
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<p>2 friends took a math test, and their scores were 90 and 95. How much do their scores vary from the average score?</p>
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<p>Okay, lets begin</p>
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<p>Okay, lets begin</p>
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<p>Variance (𝜎²) = 6.25</p>
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<p>Variance (𝜎²) = 6.25</p>
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<p>Standard Deviation (𝜎) = 2.5. </p>
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<p>Standard Deviation (𝜎) = 2.5. </p>
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<h3>Explanation</h3>
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<h3>Explanation</h3>
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<p>To find the variance and standard deviation, we have to calculate the mean first:</p>
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<p>To find the variance and standard deviation, we have to calculate the mean first:</p>
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<p>\(Mean = \frac{(90 + 95)}{2} = \frac{185}{2} = 92.5 \)</p>
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<p>\(Mean = \frac{(90 + 95)}{2} = \frac{185}{2} = 92.5 \)</p>
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<p>92.5 is the mean score. </p>
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<p>92.5 is the mean score. </p>
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<p>Next, we can find the squared differences from the mean:</p>
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<p>Next, we can find the squared differences from the mean:</p>
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<p>\(Mean = \frac{(90 + 95)}{2} = \frac{185}{2} = 92.5 \)</p>
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<p>\(Mean = \frac{(90 + 95)}{2} = \frac{185}{2} = 92.5 \)</p>
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<p>Now we have to square the deviations: </p>
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<p>Now we have to square the deviations: </p>
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<p>\((-2.5)² = 6.25 \)</p>
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<p>\((-2.5)² = 6.25 \)</p>
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<p>\((2.5)² = 6.25\)</p>
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<p>\((2.5)² = 6.25\)</p>
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<p>\(\sigma^{2} = \frac{\sum_{i=1}^{N} (x_{i} - \mu)^{2}}{N}\)</p>
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<p>\(\sigma^{2} = \frac{\sum_{i=1}^{N} (x_{i} - \mu)^{2}}{N}\)</p>
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<p>\(∑(xi - 𝜇)2 = 6.25 + 6.25 = 12.5\)</p>
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<p>\(∑(xi - 𝜇)2 = 6.25 + 6.25 = 12.5\)</p>
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<p>= \(\frac{12.5}{2}\) = 6.25 </p>
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<p>= \(\frac{12.5}{2}\) = 6.25 </p>
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<p>So, the variance is 6.25</p>
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<p>So, the variance is 6.25</p>
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<p>The formula for standard deviation is: </p>
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<p>The formula for standard deviation is: </p>
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<p>Standard deviation = √Variance </p>
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<p>Standard deviation = √Variance </p>
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<p>= √6.25 = 2.5 </p>
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<p>= √6.25 = 2.5 </p>
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<p> 2.5 is the standard deviation of the given scores. </p>
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<p> 2.5 is the standard deviation of the given scores. </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>Five kids counted their steps while walking to school. They recorded 2000, 2200, 3600, 4000, and 4400 steps respectively. Find the variance.</p>
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<p>Five kids counted their steps while walking to school. They recorded 2000, 2200, 3600, 4000, and 4400 steps respectively. Find the variance.</p>
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<p>Okay, lets begin</p>
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<p>Okay, lets begin</p>
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<p>934,400. </p>
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<p>934,400. </p>
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<h3>Explanation</h3>
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<h3>Explanation</h3>
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<p>To calculate the variance, first we have to find the mean:</p>
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<p>To calculate the variance, first we have to find the mean:</p>
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<p>\(Mean = (2000 + 2200 + 3600 + 4000 + 4400) / 5 \)</p>
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<p>\(Mean = (2000 + 2200 + 3600 + 4000 + 4400) / 5 \)</p>
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<p>\(= 16200 / 5 = 3240 \)</p>
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<p>\(= 16200 / 5 = 3240 \)</p>
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<p>3240 is the mean number of steps. </p>
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<p>3240 is the mean number of steps. </p>
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<p>Next, we can calculate the squared differences from the mean:</p>
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<p>Next, we can calculate the squared differences from the mean:</p>
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<p>\( (x - 𝜇) = (2000 - 3240 = -1240)\)</p>
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<p>\( (x - 𝜇) = (2000 - 3240 = -1240)\)</p>
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<p>\((2200 - 3240 = -1040)\)</p>
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<p>\((2200 - 3240 = -1040)\)</p>
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<p>\((3600 - 3240 = 360) \)</p>
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<p>\((3600 - 3240 = 360) \)</p>
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<p>\((4000 - 3240 = 760)\)</p>
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<p>\((4000 - 3240 = 760)\)</p>
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<p>\((4400 - 3240 = 1160)\)</p>
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<p>\((4400 - 3240 = 1160)\)</p>
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<p>Now, we can calculate \((x - 𝜇)2:\) </p>
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<p>Now, we can calculate \((x - 𝜇)2:\) </p>
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<p>\((-1240)² = 1,537,600\)</p>
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<p>\((-1240)² = 1,537,600\)</p>
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<p>\((-1040)² = 1,081,600\)</p>
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<p>\((-1040)² = 1,081,600\)</p>
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<p>\((360)² = 129,600\)</p>
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<p>\((360)² = 129,600\)</p>
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<p>\((760)² = 577,600\)</p>
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<p>\((760)² = 577,600\)</p>
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<p>\((1160)² = 1,345,600\)</p>
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<p>\((1160)² = 1,345,600\)</p>
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<p>The formula for calculating variance is: </p>
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<p>The formula for calculating variance is: </p>
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<p>\(\sigma^{2} = \frac{\sum_{i=1}^{N} (x_{i} - \mu)^{2}}{N}\)</p>
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<p>\(\sigma^{2} = \frac{\sum_{i=1}^{N} (x_{i} - \mu)^{2}}{N}\)</p>
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<p>\((1,537,600 + 1,081,600 + 129,600 + 577,600 + 1,345,600) / 5 \)</p>
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<p>\((1,537,600 + 1,081,600 + 129,600 + 577,600 + 1,345,600) / 5 \)</p>
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<p>\(σ² = 4,672,000 / 5 = 934,400\)</p>
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<p>\(σ² = 4,672,000 / 5 = 934,400\)</p>
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<p>The variance is 934,400. </p>
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<p>The variance is 934,400. </p>
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<p>Well explained 👍</p>
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<p>Well explained 👍</p>
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<h2>FAQs on Variance and Standard Deviation</h2>
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<h2>FAQs on Variance and Standard Deviation</h2>
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<h3>1.Why is standard deviation more commonly used than variance?</h3>
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<h3>1.Why is standard deviation more commonly used than variance?</h3>
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<p>Because it is expressed in the same units as the data, making it easier to interpret.</p>
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<p>Because it is expressed in the same units as the data, making it easier to interpret.</p>
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<h3>2.Can variance ever be negative?</h3>
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<h3>2.Can variance ever be negative?</h3>
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<p>No, because squared deviations are always non-negative.</p>
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<p>No, because squared deviations are always non-negative.</p>
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<h3>3.Explain the formula for population and sample variance.</h3>
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<h3>3.Explain the formula for population and sample variance.</h3>
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<p>The formula for<a>population variance</a>is: </p>
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<p>The formula for<a>population variance</a>is: </p>
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<p>σ² = ∑Ni = 1(xi - μ)2 / N </p>
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<p>σ² = ∑Ni = 1(xi - μ)2 / N </p>
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<p>σ² is the variance of the population</p>
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<p>σ² is the variance of the population</p>
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<p>μ is the mean of the population</p>
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<p>μ is the mean of the population</p>
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<p>The formula for measuring the sample variance is:</p>
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<p>The formula for measuring the sample variance is:</p>
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<p>\(s^2 = \frac{\sum_{i=1}^{N} (x_i - \bar{x})^2}{n - 1} \)</p>
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<p>\(s^2 = \frac{\sum_{i=1}^{N} (x_i - \bar{x})^2}{n - 1} \)</p>
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<p>Here, s2 is the sample variance. </p>
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<p>Here, s2 is the sample variance. </p>
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<p>x̄ is the sample mean and n is the number of values in the sample data. </p>
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<p>x̄ is the sample mean and n is the number of values in the sample data. </p>
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<p>N is the number of values in the population. </p>
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<p>N is the number of values in the population. </p>
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<p>xi is the first data point in the population. </p>
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<p>xi is the first data point in the population. </p>
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<h3>4.What is the formula for standard deviation?</h3>
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<h3>4.What is the formula for standard deviation?</h3>
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<p>\(σ = √σ²\)</p>
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<p>\(σ = √σ²\)</p>
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<p>The standard deviation is the<a>square root</a>of the variance. </p>
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<p>The standard deviation is the<a>square root</a>of the variance. </p>
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<h3>5.What does a standard deviation of zero mean?</h3>
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<h3>5.What does a standard deviation of zero mean?</h3>
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<p>It means all values in the dataset are identical.</p>
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<p>It means all values in the dataset are identical.</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>