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29 Φεβ 2024 · median: find \(\pi_{.5}\), such that \(F(\pi_{.5}) = 0.5 \Rightarrow \pi_{.5} = 1\) (from graph in Figure 1) 1st quartile: find \(Q_1 = \pi_{.25}\), such that \(F(\pi_{.25}) = 0.25\). For this, we use the formula and the graph of the cdf in Figure 2:
CHAPTER 8. INTRODUCTION TO STATISTICAL ANALYSIS. LEARNING OBJECTIVES: After studying this chapter, a student should understand: notation used in statistics; how to represent variables in a mathematical form for statistical purposes; how to construct frequency distributions, histograms, and bar graphs;
1 1 12 22 ed proportion is and 1 / ; /ˆˆ p rr p qp nn p rn p r n + = = − + = = Chapter 9 1 2 Difference of means μ-μ (independent samples) 12 12 1 2 12 22 12 /2 12 12 22 12 12 Confidence Interval when and are known ()() ( ) where Hypothesis Test when and are known ( )( ) x x E x x E Ez n n x x z n n α σσ µµ σσ σσ µµ σσ −− ...
CATEGORY 1 CATEGORY 2 CATEGORY 3 4.3 2.4 CATEGORY 4 CATEGORY 5 CATEGORY 6 x 1 1 s 1 1 3 SS between within 0 MS G) Single Sample t-Statistic (Ch. 7) t= Q−μ sM s Q= s √n OR s Q=√ s2 n H) Independent Measures & Two Samples t-Statistic (Ch. 8) t= :M1−M2 ;− :μ1−μ2 ; s Q− Q 2 x If sample sizes are the same: s Q 1− Q2=√ s1 2 n +s2 ...
Sample mean formula: Σx x = Σ = “sum of” n = number of x’s. Sample variance formula: s 2 Σ(x−x) 2. = n−1. Sample standard deviation formula: √s 2. Combinations rule: ( N) n = N! n!(N−n)! Probability of an event: number of P (event) ways Event can happen = all possible outcomes. Rule of complements: P(A) + P(Ac) = 1.
Instructor: Meimei Liu. Chapter 4 sections. 4.1 Expectation. 4.2 Properties of Expectations. 4.3 Variance. 4.4 Moments. 4.5 The Mean and the Median. 4.6 Covariance and Correlation. 4.7 Conditional Expectation SKIP: 4.8 Utility. Summarizing distributions.
It enables you to specify and fit statistical models to your data, assess the goodness of fit and display the estimates, standard errors and predicted values derived from the model.
