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2 mathematical proofs that A and B are independent events |
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P(A and B)=P(A)*P(B) P(A|B)=P(A) |
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"or" means "add", with an adjustment if they're not mutually exclusive P(A or B)=P(A)+P(B) P(A or B)=P(A)+P(B)-P(A and B) |
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n!/r!*(n-r)! n = total number of items r = the amount you select |
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all possible outcomes that aren't A |
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Complement of A (formula) |
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the probability that B will happen, given that A has already happened |
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variable whose value is obtained by measuring |
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in a series of events, the number of ways that the series of events can occur is a product of the number of ways each individual event can occur multiply the number of ways that each event can occur |
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number at the border of the rejection region |
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variable whose value is obtained by counting |
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based on real-life trials, tests, experiments |
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Empirical Rule, or Chebyshev's Theorem |
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68% of the values of x fall between -1 and 1 standard deviation from the mean 95% of the values of x fall between -2 and 2 standard deviations from the mean 99.7% of the values of x fall between -3 and 3 standard deviations from the mean Also known as The 68-95-99.7 Rule |
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exp=Σ[P(x)*x] exp=[P(x1)*x1]+[P(x2)*x2]+P(x3)*x3]...etc. Σ=sum x=a possible value x1=the first possible value, x2=the second possible value, etc. |
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another term for relative frequency probability in real life, in the experiment |
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multiplying in descending order 4*3*2*1 10*9*8*7*6*5*4*3*2*1 etc. |
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1!=1 2!=2 3!=6 4!=24 5!=120 6!=720 7!=5,040 8!=40,320 9!=362,880 10!=3,628,800 You can add 10! to the end of higher factorials, i.e. 15!=15*14*13*12*11*3,628,800 |
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Formula for: "If 3 people line up, what is the probability that the first one is a girl, the second one is a boy, and the third one is a girl?" |
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multiplication rule, multiply the probabilities of each outcome P(GBG)=P(G)*P(B)*P(G) |
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Four reasons why this is a binomial experiment |
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1) fixed number of trials, given by n 2) probabilities are independent from trial to trial 3) trials are independent and occur under identical conditions 4) only two possible outcomes, success or failure
acronym FIIT F = fixed (number of trials) I = independent (probabilities) I = independent (trials) T = two (possible outcomes) |
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null hypothesis, a statement about the population that is assumed to be true unless there is convincing evidence to the contrary; the "default hypothesis", the status quo We don't accept H0, we only "fail to reject" it. |
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alternative hypothesis, a statement about the population that contradicts the null hypothesis and is accepted as true only if there is significant evidence in favor of it. We don't reject Ha, we only "fail to accept" it. |
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IQR (definition and formula) |
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statistics regarding the probability that something will happen |
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as the number of repetitions of an experiment is increased, the relative frequency becomes closer and closer to the theoretical probability |
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median, Q2, 50th percentile, value that 50% of the values fall below |
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Mathematical proof of an outlier |
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outlier < Q1-(1.5)IQR outlier > Q3+(1.5)IQR |
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Mathematical proof that A an B are mutually exclusive events |
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Mean (probability distribution) |
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Mean -- binomial probability distribution |
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μ=np μ=mean n=number of trials p=probability of success, or P(success) |
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Minimum sample size for the Central Limit Theorem |
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"and" means "multiply", with an adjustment if they're dependent P(A and B)=P(A) * P(B) P(A and B)=P(A|B) * P(B) |
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Number of different orders in which you could select every item in the sample space |
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n! n=number of items in the sample space |
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P(A and B) -- independent |
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P(A or B) -- mutually exclusive |
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P(A or B) -- not mutually exclusive |
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E/S E=number of times event occurred S=sample space |
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probability distribution function |
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Permutations with repetition |
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nˆr n to the power of r n = total number of items r = the number of items you select |
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Permutations without repetition |
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nPr n!/(n-r)! n = total number of items r = the number of items you select |
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Probability of winning the lottery |
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number of tickets you bought/nCr |
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25th percentile value that 25% of the values fall below |
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75th percentile value that 75% of the values fall below |
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Relative frequency, long-term relative frequency |
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probability in real life, result of the experiment |
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the distribution of the means of all possible samples of the same size that could be taken from a population |
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Standard deviation (normal distribution) |
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Standard deviation (probability distribution) |
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σ=√{Σ[(x-μ)² * P(x)]} σ=standard deviation Σ=sum x=a value that occurred μ= mean |
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Standard deviation (sampling distribution) |
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σ=(√{Σ[(x-xbar)²]})/√n
1. solve x-xbar for all values of x
2. square these differences
3. add up these squares
4. take the square root of this sum
5. take the square root of n
6. divide the square root of the sum by the square root of n |
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Standard deviation -- binomial probability distribution |
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σ=√(npq) σ=standard deviation n=number of trials p=probability of success, or P(success) q=probability of failure, or P(failure) |
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Symmetry Rule of Z-Scores |
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The area to the left of a negative z-score -z is the same size as the area to the right of a positive z-score z. P(x<-z)=P(x>z). |
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Test statistic for a test of population mean |
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probability one would expect without conducting trials |
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Reject H0 when it is true |
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Fail to reject H0 when it is not true |
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Variance (probability distribution) |
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σ² σ=standard deviation (probability distribution) ∑[(x-μ)²*P(x)] ∑=sum x=a value that occurred μ=mean |
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Variance -- binomial probability distribution |
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σ²=npq σ²=variance (the variance doesn't have a letter or symbol of its own, is expressed only as "standard deviation squared", or you could just write variance or "var" if that's easier n=number of trials p=probability of success, or P(success) q=probability of failure, or P(failure) |
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Without replacement (playing cards, marbles, balls, etc.) |
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the sample size S will change be careful about the denominators when you apply the probability formula P=E/S the numerator (event) may change too, depending on the thing that was removed |
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Z-score (normal distribution) |
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confidence interval formula (mean) |
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confidence interval formula (proportions) |
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df=n-1 don't need to know what "degrees of freedom" actually means |
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margin of error (definition) |
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difference between the population mean or proportion and the outer limits of the confidence interval |
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margin of error for a 95% confidence interval for a population proportion (formula) |
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sp, standard deviation of sample proportions |
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test statistic (definition) |
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the z-score we use to find the critical value(s) for a hypothesis test |
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test statistic for a test of population proportion |
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T=(p-p0)/{√[(p0*q0)/n]} T=test statistic p=sample proportion p=null proportion (from the null hypothesis) q0=null proportion of failure, or 1-p0 n=sample size |
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test statistic for a test of population standard deviation |
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variance (normal distribution) |
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z-score (normal distribution) |
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z-score (sampling distribution) |
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z-scores to use for a 90% confidence interval, a 95% confidence interval, and a 99% confidence interval |
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level of significance; maximum probability of xbar occurring in which we can conclude that the occurrence is not a result of sampling error; probability of making a Type I error |
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μ = mean of the population xbar = mean of a sample |
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μp, the mean of sample proportions |
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μp=π
π=the population proportion
p=the sample proportion
similar to the formula μxbar=μ |
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the mean of all sample means, "the mean of means" |
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the standard deviation of all sample means |
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σ/√n (√{Σ[(x-μ)²]})/√n Averages computed from samples vary less than do individual measurements on the population, and averages computed from larger samples vary less than do averages computed from smaller samples. |
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a measure of the strength of the relationship between two variables |
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correlation coefficient (2 formulas) |
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r={∑[(x-xbar)(y-ybar)]}/(√{Σ[(x-xbar)²]}*{Σ[(y-ybar)²]})
r=[∑xy-(1/n)*∑x*∑y]/((√{[∑x²-(1/n)*(∑x)²]*[∑y²-(1/n)*(Σy)²]}))
PEMDAS: Parentheses, Exponents, Multiplication, Division, Addition, Subtraction
Parentheses Hierarchy: Solve () first, then [], then {}, then (()) |
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When does it make sense to calculate the r value? |
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When, based on a visual representation, like a scatter plot or a table, or even just a list of data points (x,y), there appears to be a strong relationship between x and y. If it looks like there is no relationship, you shouldn't bother calculating r. |
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