Answer: Option B is the cheaper deal (the 12 batteries for $14.76)
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Explanation:
For the first deal we can say
3 batteries = 4.80 dollars
3/3 batteries = 4.80/3 dollars .... divide both sides by 3
1 battery = 1.60 dollars
The unit price for the first deal is $1.60 per battery.
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For the second deal we could say
12 batteries = 14.76 dollars
12/12 batteries = 14.76/12 dollars .... divide both sides by 12
1 battery = 1.23 dollars
The unit price for the first deal is $1.23 per battery.
This is the cheaper deal.
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So in short, you're dividing the total cost over the number of items to get the unit price.
Answer:
To work out the multiplier, first add or subtract the percentage from 100, then convert to a decimal. Example: we want to add 20% to £110. To work out the multiplier, add 20 to 100, to get 120, and then change it to a decimal (divide by 100) to get 1.2.
meiabatten191 helped me out on this question too.
Answer:
90 an right angle
Step-by-step explanation:
Answer:

Step-by-step explanation:
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<u><em>You can also put this in another way:</em></u>
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Answer:
Answer = d. Chi-Square Goodness of Fit
Step-by-step explanation:
A decision maker may need to understand whether an actual sample distribution matches with a known theoretical probability distribution such as Normal distribution and so on. The Goodness-of-fit Test is a type of Chi-Square test that can be used to determine if a data set follows a Normal distribution and how well it fits the distribution. The Chi-Square test for Goodness-of-fit enables us to determine the extent to which theoretical probability distributions coincide with empirical sample distribution. To apply the test, a particular theoretical distribution is first hypothesized for a given population and then the test is carried out to determine whether or not the sample data could have come from the population of interest with hypothesized theoretical distribution. The observed frequencies or values come from the sample and the expected frequencies or values come from the theoretical hypothesized probability distribution. The Goodness-of-fit now focuses on the differences between the observed values and the expected values. Large differences between the two distributions throw doubt on the assumption that the hypothesized theoretical distribution is correct and small differences between the two distributions may be assumed to be resulting from sampling error.