Let's say you own a clothing retail shop and are trying to price a prom dress. You compute a mean of X = $485 and a standard deviation of S = $15 using a sample of n = 35 regional store prices. You're thinking of raising the price to $490 and would like to know if it's possible to have gotten a sample mean of $485 when really the population mean is $490. You decide to test the following hypothesis using a = 0.05. Could the average dress price in your region actually be this high?

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7) Let's say you own a clothing retail shop and are trying to price a prom dress. You compute a
mean of X = $485 and a standard deviation of S = $15 using a sample of n = 35 regional store
prices. You're thinking of raising the price to $490 and would like to know if it's possible to have
gotten a sample mean of $485 when really the population mean is $490. You decide to test the
following hypothesis using a = 0.05. Could the average dress price in your region actually be
this high?
Ho:H< $490
Ho:H < $490
Xbar=485 S=15 n=35 df334 µ= 490 Tcrit=2.0322, -2.0322
485-490/(15/sqrt35)= -1.97
-1.97 is withing the region of nonrejection
We do not have enough information to reject the hypothesis; therefore, the average dress price
could potentially be this high.
Transcribed Image Text:7) Let's say you own a clothing retail shop and are trying to price a prom dress. You compute a mean of X = $485 and a standard deviation of S = $15 using a sample of n = 35 regional store prices. You're thinking of raising the price to $490 and would like to know if it's possible to have gotten a sample mean of $485 when really the population mean is $490. You decide to test the following hypothesis using a = 0.05. Could the average dress price in your region actually be this high? Ho:H< $490 Ho:H < $490 Xbar=485 S=15 n=35 df334 µ= 490 Tcrit=2.0322, -2.0322 485-490/(15/sqrt35)= -1.97 -1.97 is withing the region of nonrejection We do not have enough information to reject the hypothesis; therefore, the average dress price could potentially be this high.
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