What Are the Best Quizlet Decks for AP Statistics?

Unit 1 Key Terms (15-23%): Exploring One-Variable Data

• Categorical Variable – Record which of several groups an individual belongs to
• Quantitative Variable – Taking numerical values for future calculations of sorting
• Describing a Distribution (SOCS) MUST include:
  • Shape – uniform/skew/peaks in context (symmetric, skewed left/right, unimodal/bimodal)
  • Outliers – mention outliers in context
  • Center – mean or median in context
  • Spread – range/standard deviation/IQR in context
• Bar Graphs, Two-way Tables, Histograms, Stemplots, Dotplots, Box plots – All methods of representing Data. It is good to know how to read information off of each of these.
• 5 Number Summary – Minimum, Quartile 1, Median, Quartile 3, Maximum
• The Empirical Rule – Tells us that if we all behave normally then about 68% of the values fall within 1 standard deviation of the mean, about 95% of the values fall within 2 standard deviations of the mean, and about 99.7%—almost all—of the values fall within 3 standard deviations of the mean.
• Z-Score – z = x - x̄ / s

Unit 2 Key Terms (5-7%): Exploring Two-Variable Data

• Scatterplots – A way to organize data. On the x-axis is the explanatory (independent) variable and on the y-axis is the response (dependent) variable.
• Form – linear or curved
• Direction – positive or negative correlation
• Strength – depends on the correlation coefficient; could be weak, moderate, or strong.
• LSRL – ŷ=a+bx where x denotes (context) and ŷ denotes predicted (context)
• Correlation Coefficient (r) – The correlation coefficient shows the degree to which there is a linear correlation between the two variables, that is, how close the points are to forming a line. The closer r is to 1 or -1, the stronger the relationship.
• Slope (b) – There is a predicted increase/decrease of ______ (slope in unit of y variable) for every 1 (unit of x variable).
• Y Intercept – The predicted value of (y in context) is _____ when (x value in context) is 0 (units in context).
• Coefficient of Determination (r^2) – ____% of the variation in (y in context) is due to its linear relationship with (x in context).
• Residuals – The difference between the actual data and the value predicted by a linear regression model, or y-ŷ. The ideal pattern is random scatter above and below the LSRL (where the residuals=0). A positive residual means the model underestimated the true value while a negative residual means the model overestimated the true value.
• Extrapolation – The use of a regression model to make predictions outside of the domain of the given data. If you go outside this domain and farther outside the domain you go, the less accurate your predictions will be.

Unit 3 Key Terms (12-15%): Collecting Data

• Experiment – Deliberately imposes treatment in order to observe the response. Causation could be proven.
• 🔭Observational Study – Observe individuals and measure variables of interest but don’t attempt to influence the responses. These studies look for association between variables  because in a study, no treatment is imposed.
• 👀Confounding – Occurs when two variables are associated in a way that their effects on the response variable cannot be deciphered from each other individually.
• Bias – Statistical studies are biased if it is likely to underestimate or overestimate the value you are looking for.
• Simple Random Sample (SRS) – Chooses a sample size “n” in a way that a group of individuals in the population has an equal chance to be selected as the sample
• Stratified Random Sample – Selects a sample by choosing an SRS from each strata and combining the SRSs into one overall sample. These reduce variability in the data and give more precise results.
• Cluster Sample –The population is divided into groups, called clusters, and an SRS of clusters is taken within each cluster. All individuals are sampled in the clusters selected.
• Systematic Random Sample – Sample members from a population selected according to a random starting number and a fixed periodic interval.

Unit 4 Key Terms (10-20%): Probability, Random Variables, and Probability Distributions

• Independent Events – The outcome of one event doesn't influence the outcome of another event.
• P(A|B) = P(A)
• P(A and B) =P(A)*P(B)
• Disjoint/Mutually Exclusive Events – Cannot occur at the same time and have no outcomes in common.
• P(A and B) = 0
• P(A or B) = P(A) + P(B)
• Conditional Probability – Probability of one event under the condition that another event is known.
• P(A|B)= P(A and B) / P (B) – The probability of A given B = Probability of A and B / Probability of B
• Discrete Random Variable – Takes a fixed set of possible values with gaps between them (cannot include decimals).
• Mean (Expected) Value – Summation(xi*pi)
• Standard Deviation – sqrt(summation(xi-mean of x)^2 * pi). You cannot add standard deviations, only variances.
• Continuous Random Variable – Can take any value in an interval on the number line (can include decimals).
• Law of Large Numbers – Says that in many repetitions of the same chance process, the simulated probability gets closer to the true probability as more trials are run.
• Binomial Setting – Arises when we perform independent trials of the same chance process and count the number of times that a particular outcome called a success (p), occurs. Failure (q) is defined as 1 minus the probability of success. Must check 10% condition.
• Binomial Setting – Arises when we perform independent trials of the same chance process and count the number of times that a particular outcome called a success (p), occurs. Failure (q) is defined as 1 minus the probability of success. Must check 10% condition.
• ⭕️ Geometric Setting – Arises when we perform independent trials of the same chance process and record the number of trials it takes to get one success. On each trial, the probability p of success must be the same. The number of trials Y that it takes to get one success in a geometric setting is a geometric random variable.

Unit 5 Key Terms (7-12%): Sampling Distributions

• Sampling Distribution – A distribution where we take ALL possible samples of a given size and put them together as a data set.
• A statistic is used to estimate a parameter.
• Parameters vs Statistics – Mean (𝝁, x̅); Standard Deviation (σ, s); Proportions (𝝆, p̂).
• Large Counts Condition – The number of successes and failures is at least 10.
• Central Limit Theorem – States that if n (the sample size) is ≥30, the sampling distribution is normal. The larger n is, the more normal the sample is.

Unit 6 Key Terms (12-15%): Inference for Categorical Data: Proportions

• Confidence Interval – An interval of numbers based on our sample proportion that gives us a range where we can expect to find the true population proportion.
• In repeated sampling, I am __% confident that the true population proportion (context) falls within this interval.
• Significance Test – Estimating the probability of obtaining our collected sample from the sampling distribution of our size when we assume that the given population proportion is correct.
• Large Counts Condition – The number of successes and failures is at least 10 (np≥10 and n(1-p)≥10). This condition proves normality.
• Random Condition – Reduces any bias that may be caused from taking a bad sample. When answering inference questions, it is always essential to make note that our sample was random. Without a random sample, our findings cannot be generalized to a population, meaning our scope of inference is inaccurate.
• 10% Condition – Check that the population in question is at least 10 times as large as our sample in order to prove independence.
• Margin of Error – The "buffer zone" of our confidence interval; point estimate +- (z*)(stan. dev.)
• Null Hypothesis – The hypothesis based on our claim that was given in the problem (p=___).
• Alternative Hypothesis – The hypothesis that the claim in our null is not true (p<___, p>___ or p≠___).
• Type I (𝞪) Error – When we reject our Ho, when in fact, we should have failed to reject.
• Type II (𝞫) Error – When we fail to reject our Ho, but we actually should have rejected our Ho.
• Power – How strong our test is because power = 1-P(Type II Error).

Unit 7 Key Terms (10-18%): Inference for Quantitative Data: Means

• Confidence Interval – An interval of numbers based on our sample proportion that gives us a range where we can expect to find the true population mean.
• In repeated sampling, I am __% confident that the true population mean (context) falls within this interval.
• In repeated sampling, I am ___% confident that the true difference in population means (context) falls within this interval.
• Central Limit Theorem – States that if n (the sample size) is ≥30, the sampling distribution is normal. The larger n is, the more normal the sample is.
• Random Condition – Reduces any bias that may be caused from taking a bad sample.
• 10% Condition – Check that the population in question is at least 10 times as large as our sample in order to prove independence.
• Null Hypothesis – The hypothesis based on our claim that was given in the problem (μ=___).
• Alternative Hypothesis – The hypothesis that the claim in our null is not true (μ<___, μ>___ or μ≠___).
• p<𝞪 – Since p<𝞪, we reject our Ho. We have convincing evidence at the 𝞪 level that (Ha in context).
• p<𝞪 – Since p<𝞪, we reject our Ho. We have convincing evidence at the 𝞪 level that (Ha in context).
• Degrees of Freedom – n-1

Unit 8 Key Terms (2-5%): Inference for Categorical Data: Chi-Squared

• Goodness of Fit Test – Must have random sampling. All expected counts must be greater than 5.
  • Degrees of Freedom – categories - 1
  • Null Hypothesis – There is no association between ___ and ___.
  • Alternative Hypothesis – There is association between ___ and ___.
• Test for Independence – Checks the association between two variables in a single population.
  • Expected Counts – (row total)(column total) / (table total)
  • Null Hypothesis – There is no association between ___ and ___.
  • Alternative Hypothesis – There is association between ___ and ___.
• Test for Homogeneity – Checks the distribution of a single variable in several populations to see if these populations are similar with respect to the variable.
  • Null Hypothesis – There is no difference between ___ and ___.
  • Alternative Hypothesis – There is difference between ___ and ___.

Unit 9 Key Terms (2-5%): Inference for Quantitative Data: Slopes

• LSRL – ŷ=a+bx where x denotes (context) and ŷ denotes predicted (context)
• Degrees of Freedom – n-2
• Confidence Interval – b+- t*(SEb)
• In repeated sampling, I am __% confident that the true slope falls within this interval.
• True Slope – 𝞫
• Null Hypothesis – 𝞫 = 0; changing x does nothing to y

Closing Thoughts