M670_1(Descriptive Statistics)

M670_1(Descriptive Statistics) - Introduction and...

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1 Introduction and Descriptive Statistics k MGMT 670: Business Analytics Krannert School of Management Purdue University
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2 What is Business Statistics? The development and application of processes and methods for collecting, summarizing, analyzing, interpreting, and reporting data relevant to a business decision or situation. Sources of data: Business information: finding and using data in the information age, Jay L. Zagorsky Statistical Abstracts: U.S. Government Census Bureau: American factfinder American society of association executives: Trade organizations
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3 Applications in Business and Economics § Accounting § Finance § Marketing § Production § Human Resource Auditing, Costing Financial trends, Valuation Consumer preferences, advertising strategies, Demand Forecasting Quality control, Product/Process design Recruiting, retaining, benefits, etc.
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4 Statistical Methods § Descriptive Statistics Ø Tabular, graphical, and numerical methods used to summarize (or describe) data Ø Involves collecting, presenting, and characterizing data. § Inferential Statistics Ø Process of using sample data to draw conclusions or make decisions about the characteristics of the population. Ø Two forms: Estimation Hypothesis testing
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5 Examples of Inferential Statistics 1. A financial expert wishes to figure out the impact of the launch of an online internet channel for marketing on the stock price. 2. A car insurance company wishes to identify whether having a graduate degree is related to fewer accidents 3. A manufacturer has just received a large shipment of modified machine components. Is the defective rate more than 1%?
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6 Descriptive Statistics: Summary of Tabular and Graphical Methods Data Numerical Data Tabular Methods Methods Graphical Methods Graphical Methods Frequency Distribution Rel. Freq. Dist. Bar Graph Pie Chart Pareto Chart Frequency Distribution Rel. Freq. Dist. Cum. Freq. Dist. Cum. Rel. Freq. Distribution Stem-and-Leaf Display Histogram Ogive Time Plot Categorical Data
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7 Categorical (Qualitative) versus Quantitative Variables Element Categorical Quantitative Person Religion Height Land Flower Area House Style Size Firm Industry Sales Worker Gender Age Computer Brand RAM
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8 Numerical Representation: Categorical Variables A typical technique is to use g-1 dummy variables to replace a categorical variable with g categories/classes For example: Consider data on computers produced by HP, IBM, DELL, and Gateway. Then, we represent them as: Company / Variable Dummy_HP Dummy_IBM Dummy_DELL HP 1 0 0 IBM 0 1 0 DELL 0 0 1 Gateway 0 0 0
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9 Central Tendency (Location) Variation (Dispersion) Shape Descriptive Statistics: Numerical Methods § Numerical Data Properties link
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10 Measures of Location § Mean § Median § Mode § Percentiles § Quartiles § Midrange
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11 Measures of Location Statistic Definition Formula Mean The average of all values of a variable x¯ = 1 n i=1 n x i Median The “middle” observation when the data
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M670_1(Descriptive Statistics) - Introduction and...

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