| Concept | Formula | |---------|---------| | Sample mean | $\barx = \frac\sum x_in$ | | Sample variance | $s^2 = \frac\sum (x_i - \barx)^2n-1$ | | Probability of event A | $P(A) = \frac\textfavorable outcomes\texttotal outcomes$ | | Addition rule | $P(A \cup B) = P(A) + P(B) - P(A \cap B)$ | | Conditional probability | $P(A|B) = \fracP(A \cap B)P(B)$ | | Binomial probability | $P(X=k) = \binomnk p^k (1-p)^n-k$ | | Standard error (mean) | $SE = \fracs\sqrtn$ | | Confidence interval (mean) | $\barx \pm t_\alpha/2 \cdot \fracs\sqrtn$ | | Test statistic for one-sample t-test | $t = \frac\barx - \mus / \sqrtn$ | | Pearson correlation | $r = \frac\sum (x_i - \barx)(y_i - \bary)\sqrt\sum (x_i - \barx)^2 \sum (y_i - \bary)^2$ |
Every chapter is packed with solved problems that mirror the types of questions found in university exams. | Concept | Formula | |---------|---------| | Sample
Focusing on scenarios with countable outcomes using Binomial and Poisson distributions. Finding "An Introduction to Statistics and Probability by
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While primarily designed for undergraduate and postgraduate students in statistics and mathematics, it is also highly applicable to engineering, economics, and social sciences.