Statistics trips people up because the vocabulary hides some fairly simple ideas. These guides translate the jargon — p-values, confidence intervals, hypothesis tests — into plain English you can actually use.
What a p-value actually measures, the three things it is not, and how to interpret one correctly on an exam.
The single question that decides between a t-test and a z-test, with the formulas and a worked example for each.
Convert a value to a z-score, find it in the grid, and handle below, above, and between problems.
Why a strong correlation does not prove cause — confounders, reverse causation, and the design that does.
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What the 95% really refers to, how to build a confidence interval, and the interpretation that loses points.
Type I versus Type II error made memorable with a courtroom analogy, plus how alpha, beta, and power connect.
A five-step routine that takes a hypothesis-test word problem from the first sentence to a written conclusion.
How to compute the mean, median, and mode, and when each measure of center quietly misleads.
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A line-by-line read of a regression table: coefficients, p-values, R-squared, and standard error.
A practical plan for an intro statistics exam: build a decision sheet and drill problem types.
A full one-sample t-test worked from word problem to plain-English conclusion, with the formula, df, and p-value.
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An independent two-sample t-test from hypotheses to conclusion, including the pooled vs. unpooled decision and a full numerical example.
A paired t-test from setup to conclusion: collapse pairs into a difference column, run the one-sample test, and read the result.
Why ANOVA replaces multiple t-tests, how MSB and MSW build the F-statistic, and a worked three-group example.
Build expected counts from row and column totals, compute chi-square, and test whether two categorical variables are independent.
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The addition rule for or, the multiplication rule for and, and the mutually-exclusive vs. independent distinction that decides which version you use.
What a sampling distribution is, why its spread is σ/√n, and how that single fact powers every confidence interval and hypothesis test.
What the central limit theorem actually says, when it kicks in, and a worked dice example showing why sample means go normal.
McGraw-Hill Statistics Solutions
Compute the expected value and variance of a discrete random variable using the weighted-sum formulas, with a dice and a lottery worked example.
Check the four BINS conditions, plug into the binomial formula, and work P(X = k) and P(X ≥ k) with a free-throw example.
When the Poisson distribution is the right model, how to compute its probabilities, and a worked customer-arrival example.
Build the five-number summary from a small data set, apply the 1.5 × IQR outlier rule, and read the boxplot for shape.
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Why variance and standard deviation both exist, the units issue, and a clear rule for which one to report.
What changes when a second predictor enters the model: partial vs. marginal slopes, adjusted R-squared, and the multicollinearity trap.
What R-squared really measures, why high R-squared is not the same as a good model, and three traps to avoid when interpreting it.
