// Numbas version: finer_feedback_settings {"name": "Confidence interval for variance", "extensions": ["stats"], "custom_part_types": [], "resources": [], "navigation": {"allowregen": true, "showfrontpage": false, "preventleave": false, "typeendtoleave": false}, "question_groups": [{"pickingStrategy": "all-ordered", "questions": [{"name": "Confidence interval for variance", "tags": [], "metadata": {"description": "

Finding the confidence interval at either 90%, 95% or 99% for the population variance of a sample. $\\chi^2$ tables are used. A single scenario is given.

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A smartphone manufacturer claims that their new smartphone will last 24 hours on a full charge under normal usage with a standard deviation of 0.8 hours. 

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Testing 6 of these smartphones we see the following usage (in hours) until the battery dies:

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{x} hours.

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Construct a {confl}% confidence interval for the standard deviation.

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We use the $\\chi^2$ tables to find the confidence interval for the population variance and then we can square root our lower and upper points of the interval to give us the confidence interval for the standard deviation. 

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The formula for the confidence interval for the population variance is:

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\\[\\frac{(n-1)s^2}{\\chi^2_{n-1;\\alpha/2}}\\le\\sigma^2\\le\\frac{(n-1)s^2}{\\chi^2_{n-1;1-\\alpha/2}}\\]

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We first calculate the sample variance using our formula:

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\\[\\textrm{Sample Variance} = \\frac{1}{n-1}\\left(\\sum_{j=1}^{n}x_j^2 -n\\bar{x}^2\\right)=\\var{var}\\]

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We now need to find the critical values of the $\\chi^2$ distribution. Our degrees of freedom here are $n-1=\\var{n}-1=\\var{n-1}$. 

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Our confidence interval is $\\var{confl}\\%$ so our significance level is $\\alpha=\\var{alpha}$. Our critical values are then

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\\[\\chi^2_{n-1;\\alpha/2}=\\chi^2_{\\var{n-1};\\var{alpha/2}}=\\var{chi_lower}\\]

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and

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\\[\\chi^2_{n-1;1-\\alpha/2}=\\chi^2_{\\var{n-1};\\var{1-alpha/2}}=\\var{chi_upper}\\]

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Hence, inputting all of these values into our formula for the confidence interval

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\\[\\frac{(n-1)s^2}{\\chi^2_{n-1;\\alpha/2}}\\le\\sigma^2\\le\\frac{(n-1)s^2}{\\chi^2_{n-1;1-\\alpha/2}}\\]

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gives the confidence intervals for the population variance $\\sigma^2$

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Lower value of the confidence interval $=\\;\\displaystyle \\var{var_lower}$.

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Upper value of the confidence interval $=\\;\\displaystyle \\var{var_upper}$.

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Square rooting these values gives us the confidence interval for the population standard deviation $\\sigma$ (to 3 d.p.):

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\\[(\\var{lower_round},\\var{upper_round})\\]

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Calculate a {confl}% confidence interval $(a,b)$ for the population standard deviation:

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$a=\\;$[[0]] hours          $b=\\;$[[1]] hours

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Enter both to 1 decimal place.

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