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1Feb 13, 2006BUS304 – Review chapter 1-3 Descriptive statistics1 Review of Chapter 1-3 Descriptive Statistics Descriptive Statistics Ways to collect, describe, and present data Collect data Population vs. Sample Statistical Sampling Technique a) Simple random b) Stratified c) Systematic d) Cluster Data Types Primary vs. Secondary Qualitative vs. Quantitative Measure levels: a) Nominal (group, compare, mode) b) Ordinal (everything you can do for level a, + sort, median) c) Interval (everything you can do for level b, + subtraction, zero ≠ nothing) d) Ratio (everything you can do for level c, + division, zero = nothing)
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2Feb 13, 2006BUS304 – Review chapter 1-3 Descriptive statistics2 Graphical Presentation Data Type start Bar ChartPie Chart or Frequency Relative Frequency or Qualitative Categorical (Nominal, Ordinal) Quantitative (Discrete/continuous, Interval/Ratio) Data class Bar ChartLine Chart Frequency Relative Frequency Histogram Limited Categories Continuous Range Scatter Chart Two sets of continuous variables – relationship
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3 Procedures to create a histogram Create a frequency table Identify the range of data: Range = Rounded maximum - Rounded minimum Identify the width of each category: Width = Range / Number of bars Write down the lower and upper bound for each bar Lower limitUpper limitFrequency Rounded min=lower limit + widthCount numbers =upper limit of above category=lower limit + widthCount numbers …… =upper limit of above category=lower limit + width = rounded maxCount numbers Remember to verify the total frequency = sample size
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4Feb 13, 2006BUS304 – Review chapter 1-3 Descriptive statistics4 Numerical Characterization Measure of central tendency: Mean MedianMode Measure of Variation RangeVariance Standard Deviation Other Location Related Measures (find location first) Percentiles/Quartiles (Calculate location first, then go find the value) Interquartile range
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5 Probability Theory Key concepts Experiments and events Identify elementary events and sample space Relationship among events: Complement Events, P(E 1 ) =1- P(E 2 ) Mutually Exclusive Events, P(E 1 and E 2 ) = 0 Conditional Probabilities, P(E 1 | E 2 ) = P(E 1 and E 2 ) / P(E 2 ) Independent Events. P(E 1 and E 2 ) = P(E 1 )*P(E 2 ) Mean (expected value) and SD of a random variable Mean – weighted average of all possible values (weights are the probs). Variance – weighted average of the squared deviation from the expected value.
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