sLecture0bPBS - Lecture #0 Learning Objectives 1. Review...

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Unformatted text preview: Lecture #0 Learning Objectives 1. Review the characteristics of probability distributions. a. Continuous b. Discrete 2. Review the characteristics of the Binomial probability distribution. 3. Review how to calculate probabilities for a Binomial process. a. Exact probabilities using the Binomial probability distribution. b. Approximate probabilities using the Normal probability distribution. 4. Review the characteristics of the Normal probability distribution. 5. Review how to compute probabilities using a Normal probability distribution. Understand the role of the standard Normal distribution in this process. 6. Review the rules for means and variances 7. Review summation notation “It’s not that I’m so smart; it’s just that I stay with problems longer.” – Albert Einstein 1 Variable – a characteristic of interest that can have different “values,” for example, gender, age, gross domestic product, time, etc. Data – the “values” of variables Quantitative – the values are on at least an interval scale Nominal – the values are categorical Ordinal – the values are ordered Population – the set of all possible items of interest Sample – a subset of the population items Summarizing Quantitative Data (in list form) •Numerical Measures A. Location, Central Tendency – describe the center of the data 1. Mean: n X X i ∑ = (sample) n X i ∑ = μ (population) 2. Median = 50 th percentile or the value that is exactly in the center of the data when it is arranged in ascending order B. Dispersion, Spread – describe the variation in the data 1. Variance: ( ) 1 2 2 − − = ∑ n X X s i (sample) ( ) N X i ∑ − = 2 2 μ σ (population) 2. Standard Deviation = square root of the variance 3. Range = largest data value – smallest data value 4. Interquartile Range = Q3 – Q1, where Q1 is the data value such that 25% of values are smaller than it, and Q3 is the data value such that 75% of values are smaller than it. C. Linear Association (pairs of data) – describe the strength of linear 2 relationship between two variables 1. Covariance = 1 ) )( ( − − − ∑ n Y Y X X i i i (sample) 2. Correlation = Y X XY Y X S S S S S Y X Cov = ) , ( (sample) • Graphical Representations 1. Bar graph (categorical data) 0.0000 0.0500 0.1000 0.1500 0.2000 0.2500 0.3000 0.3500 0.4000 1 2 3 4 Employee Percent of Total Recorded Not Recorded 2. Histogram Histogram of 15Sample 1 / Data Set #1 1 2 3 4 5 6 7 8 76.35 86.83 97.32 107.80 118.28 Frequency StatTools Student Version For Academic Use Only StatTools Student Version For Academic Use Only StatTools Student Version For Academic Use Only StatTools Student Version For Academic Use Only StatTools Student Version For Academic Use Only StatTools Student Version For Academic Use Only StatTools Student Version For Academic Use Only StatTools Student Version For Academic Use Only StatTools Student Version For Academic Use Only StatTools Student Version For Academic Use Only 3 3. Box-plot 4. Stem and leaf plot...
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This note was uploaded on 02/22/2009 for the course BUSMGT 330m taught by Professor Kriska during the Spring '08 term at Ohio State.

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sLecture0bPBS - Lecture #0 Learning Objectives 1. Review...

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