document.write( "Question 1197972: In preparing a report on the​ economy, we need to estimate the percentage of businesses that plan to hire additional employees in the next 60 days.
\n" ); document.write( "​a) How many randomly selected employers must we contact in order to create an estimate in which we are 99​% confident with a margin of error of ​6%?
\n" ); document.write( "​b) Suppose we want to reduce the margin of error to 4​%. What sample size will​ suffice?
\n" ); document.write( "​c) Why might it not be worth the effort to try to get an interval with a margin of error of 1​%?
\n" ); document.write( "a) A sample size of ? is needed.
\n" ); document.write( "Part 2
\n" ); document.write( "​b) A sample size of ? is needed.\r
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Algebra.Com's Answer #831458 by math_tutor2020(3817)\"\" \"About 
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\n" ); document.write( "Part (a)\r
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\n" ); document.write( "\n" ); document.write( "At 99% confidence, the z critical value is roughly z = 2.576
\n" ); document.write( "Use a table like this
\n" ); document.write( "https://www.sjsu.edu/faculty/gerstman/StatPrimer/t-table.pdf
\n" ); document.write( "to get that value. Look at the bottom row labeled \"Z\" and above the 99% confidence level.\r
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\n" ); document.write( "\n" ); document.write( "You can also use a stats calculator or spreadsheet to determine this z critical value.\r
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\n" ); document.write( "\n" ); document.write( "The desired margin of error is 6%, which means we want E = 0.06 or smaller.\r
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\n" ); document.write( "\n" ); document.write( "We're not told the value of phat, which is the sample proportion of businesses that plan to hire additional employees in the next 60 days. \r
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\n" ); document.write( "\n" ); document.write( "Use phat = 0.5 as a conservative estimate. This is the default value of phat if none is stated.\r
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\n" ); document.write( "\n" ); document.write( "n = min sample size
\n" ); document.write( "n = phat*(1-phat)*(z/E)^2
\n" ); document.write( "n = 0.5*(1-0.5)*(2.576/0.06)^2
\n" ); document.write( "n = 460.817778 approximately
\n" ); document.write( "n = 461 always round UP to the nearest whole number\r
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\n" ); document.write( "\n" ); document.write( "Here's why we round up to the nearest whole number.
\n" ); document.write( "Let's try n = 460 in the margin of error formula
\n" ); document.write( "E = z*sqrt(phat*(1-phat)/n)
\n" ); document.write( "E = 2.576*sqrt(0.5*(1-0.5)/460)
\n" ); document.write( "E = 0.060053
\n" ); document.write( "We're slightly over the 6% target.
\n" ); document.write( "Now try n = 461
\n" ); document.write( "E = z*sqrt(phat*(1-phat)/n)
\n" ); document.write( "E = 2.576*sqrt(0.5*(1-0.5)/461)
\n" ); document.write( "E = 0.059988
\n" ); document.write( "Now the error is either 6% or less, which meets the goal we're after.
\n" ); document.write( "This is why we round up to the nearest whole number for min sample size problems. This is to clear the hurdle needed.\r
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\n" ); document.write( "\n" ); document.write( "Answer: 461 employers\r
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\n" ); document.write( "Part (b)\r
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\n" ); document.write( "\n" ); document.write( "Repeat the same set of steps as part (a), but this time we use E = 0.04
\n" ); document.write( "Keep everything else the same.\r
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\n" ); document.write( "\n" ); document.write( "n = phat*(1-phat)*(z/E)^2
\n" ); document.write( "n = 0.5*(1-0.5)*(2.576/0.04)^2
\n" ); document.write( "n = 1036.84
\n" ); document.write( "n = 1037 \r
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\n" ); document.write( "\n" ); document.write( "Answer: 1037 employers\r
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\n" ); document.write( "Part (c)\r
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\n" ); document.write( "\n" ); document.write( "Now use E = 0.01\r
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\n" ); document.write( "\n" ); document.write( "n = phat*(1-phat)*(z/E)^2
\n" ); document.write( "n = 0.5*(1-0.5)*(2.576/0.01)^2
\n" ); document.write( "n = 16589.44
\n" ); document.write( "n = 16590\r
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\n" ); document.write( "\n" ); document.write( "We need to sample a lot more employers at this point (more than ten times as much compared to the result of part (b)), so it's more practical to go with the 6% or 4% margin of error instead. \r
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\n" ); document.write( "\n" ); document.write( "Answer: Minimum sample size gets way too large
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