MSci_609_Introduction_to_Data_Analysis__

MSci_609_Introduction_to_Data_Analysis__ - 1/3/2011 1...

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1/3/2011 1 INTRODUCTION: DESCRIPTIVE STATISTICS & PROBABILITY CONCEPTS INSTRUCTOR: AMER OBEIDI 1 Why? 1. Collecting Data Data 2 e.g., Sampling e.g., Data acquisition 2. Evaluating Data e.g., Charts & Tables e.g., Average Data Analysis Decision- 3. Interpreting Data Making © 1984-1994 T/Maker Co.
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1/3/2011 2 Statistical 3 Methods Descriptive Statistics Inferential Statistics Summarizes the data set collected Make statements about population Descriptive Statistics - describe collected data 4 “Nearly 87% of players participating in a Speed Training Program improved their sprint times.” “Only about 3% of players participating in a Speed Training Program had decreased times.” 1. Involves Collecting Data Presenting Dat 25 50 $ Presenting Data Characterizing Data 2. Purpose Describe Data X = 30.5 S 2 = 113 0 Q1 Q2 Q3 Q4
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1/3/2011 3 Inferential Statistics - make generalizations about a group based on a subset (Sample) of that group 5 “Based on exit polls, more people voted for Candidate A.” 1. Involves Estimation Hypothesis Testing 2. Purpose Make decisions about population characteristics Experimental Unit – object of interest example – graduating senior, students in class 609 6 example graduating senior, students in class 609 Population – the set of units we are interested in learning about example – all 1450 graduating seniors at “State U” Sample – subset of population example – 100 graduating seniors at “State U” Variable – characteristic of an individual population unit example – age at graduation
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1/3/2011 4 Nominal level - data that is classified into categories and Interval level - similar to the ordinal level, with the additional 7 classified into categories and cannot be arranged in any particular order. EXAMPLES: eye color, gender, religious affiliation. Ordinal level – involves data arranged in some order, but the differences between data values cannot be determined or are property that meaningful amounts of differences between data values can be determined. There is no natural zero point. EXAMPLE: Temperature on the Fahrenheit scale. Ratio level - the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of cannot be determined or are meaningless. EXAMPLE: During a taste test of 4 soft drinks, Mellow Yellow was ranked number 1, Sprite number 2, Seven-up number 3, and Orange Crush number 4. meaningful for this level of measurement. EXAMPLES: Monthly income of surgeons, or distance traveled by manufacturer’s representatives per month. 8
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1/3/2011 5 Qualitative Data are nonnumerical Major Disciplin 9 Major Discipline Political Party Gender Eye color Quantitative Data are recorded on a meaningful numerical scale Income Sales Population Home runs The three rules of data analysis won’t be difficult to remember: 10 1.
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MSci_609_Introduction_to_Data_Analysis__ - 1/3/2011 1...

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