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            <video:description>This video explores how scaling data (multiplying each observation by a constant &#39;a&#39;) affects its mean, variance, and standard deviation. We first solve a specific problem where observations are doubled, observing the change in variance. Then, we&#39;ll provide a general proof to show that if a dataset has a certain mean and variance, scaling each observation by &#39;a&#39; results in a new mean of &#39;a&#39; times the old mean, and a new variance of &#39;a squared&#39; times the old variance.</video:description>
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            <video:description>In this video, we investigate the effect of shifting data (adding a constant &#39;a&#39; to each observation) on its mean, variance, and standard deviation. We will prove that while the mean shifts by the constant &#39;a&#39; (i.e., new mean equals old mean plus &#39;a&#39;), the variance of the dataset remains unchanged. This means the standard deviation also stays the same, as shifting data points doesn&#39;t alter their spread.</video:description>
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            <Attribute name="description">This video demonstrates how to find missing observations in a dataset when the mean and variance are known, along with some of the existing observations. We&#39;ll tackle a problem by setting up two equations using the formulas for mean and variance, involving the unknown entries. We then solve this system of equations to determine the values of the missing data points.</Attribute>
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