If you imagine a number line where 0 is in the middle, and all the negative numbers are on the left side of 0 and all the positive numbers are on the right side of 0, -28 will be 28 units across on the left side.
Therefore, to get back to 0, you need to go back 28 units (moving to the right), which is positive. Therefore, to make the sum 0, you need to add 28 to -28.
To the nearest
0.1: 643
1: 643
10: 640
100: 600
1,000: 1,000
Those are the answers to the nearest tenth, one, ten, hundred and thousand.
Law of Large NumbersLarge samples will be representative of the population from which they are scoredSampling ErrorNatural discrepancy, or amount of error, existing between a sample statistic and its corresponding population parameterSampling Error<span>- a consequence of the fact that samples vary in their estimates of the population mean
- expected, and why we use random, and repeated sampling of populations</span>Distribution of Sample MeansThe set of sample means from all the possible random means populationDistribution of Sample MeansThe collection of sample means for all the possible random samples of a particular size (n) that can be obtained from a populationDistribution of Sample MeansThis is NOT a distribution of scores (X's), rather a distribution of statistics, means in particular (M's).Sampling DistributionDistribution of statistics obtained by selecting all of the possible samples of a specific size from a populationSampling Distributiongeneralized version of distribution of sample meansSampling Distributionconstructed by conducting several random samples of a population, each of size n, calculating the M of each of the samples, and plotting them in a frequency distributionsample means<span>If we construct a Sampling Distribution correctly, then...
Our ____________ should pile around our population mean</span>normal<span>If we construct a Sampling Distribution correctly, then...
Our sample means should form a ______________ dsitribution</span>larger<span>If we construct a Sampling Distribution correctly, then...
the __________ the sample size, the closer the means should be to the population mean</span>Central Limit Theoremfor any population with a mean μ and a standard deviation σ, the distribution of sample means for the sample size n will have a mean of μ and standard deviation of σM = σ /√(n)Central Limit TheormThe __________________ holds for the distribution of any population, no matter the original shape, mean, or standard deviationdistribution of meansThe ___________ approaches normal very rapidllyThe Expected Value of Mthe mean of the sample distribution of mean, and is taken to be equal to the population μσMThe standard deviation for the distribution of sample means. Referred to as the standard error of MσMThis provides a measure of how much distance is expected, on average, between the sample mean M and the population mean μdescribes<span>Standard error of M:
similar to the standard deviation, it ____________________ the distribution of sample means</span>represents<span>Standard error of M:
standard error measures how ell and individual sample mean _____________the entire distribution</span>lower<span>Standard error of M:
the larger the sample size, the _____________ the standard error</span>probabilitythe distribution of sample means finds the _______________ associated with a specific sampleStandard Deviationmeasures standard distance between a score and a population meanStandard Errormeasures the standard difference between a sample mean and the population meanstandard deviationsWhat can approximate a measure of variability, when working with distributions of scores?standard errorWhen you have questions concerning samples, what is the appropriate measures of variabilitywill notSample error operates on the idea that a sample (will/will not) provide a perfect representation of a populationstandard errothe error between the estimates of the samples taken from the population, or average distance between a sample and population meanstandard errorWhat provides a measure for both measuring and defining the sampling error for a distributionInferential Statisticsmethods that use sample data as the basis for drawing general conclusions about the population
Answer:
25 tickets
Step-by-step explanation:
Let x = the number of guests
The fixed expense is $125 for the rental.
The variable expense is the insurance, which is $3 per guest.
For x number of guests, the insurance cost is 3x.
The total expense is the fixed expense plus the variable expense, or
3x + 125
The money he receives is a variable amount depending on the number of guests. He charges $8 per guest, so, for x number of guests, he will receive 8x amount of money.
To break even, the revenue and the expenses must be equal. Now we set the amount he receives equal to the amount he spends, and we solve for x, the number of guests.
8x = 3x + 125
Subtract 3x from both sides.
5x = 125
Divide both sides by 5.
x = 25
Answer: 25 tickets
Answer: 33
Step-by-step explanation:
Angles inscribed in the same arc are congruent, so
![13+4x=-7+8x\\\\20+4x=8x\\\\20=4x\\\\x=5\\\\\implies m\angle CDE=-7+5(8)=33^{\circ}](https://tex.z-dn.net/?f=13%2B4x%3D-7%2B8x%5C%5C%5C%5C20%2B4x%3D8x%5C%5C%5C%5C20%3D4x%5C%5C%5C%5Cx%3D5%5C%5C%5C%5C%5Cimplies%20m%5Cangle%20CDE%3D-7%2B5%288%29%3D33%5E%7B%5Ccirc%7D)