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Business Statistics
Business Statistics
Business Statistics
Business Statistics
Business Statistics
Business Statistics
Business Statistics
Business Statistics
Business Statistics
Business Statistics
Business Statistics
Business Statistics
Business Statistics
Business Statistics
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Business Statistics

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Lecture 1, Business Statistics

Lecture 1, Business Statistics

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  • 1. Business Statistics<br />
  • 2. The course<br /> This courses introduces students to a range of statistical techniques that are appropriate in business practice and decision making. Students will learn how to make appropriate use of statistical techniques.<br />
  • 3. Learning outcomes<br /> Demonstrate the use of appropriate software for converting data into meaningful information.<br /> Selected, defend, and use appropriate statistical tools for analysis of data.<br />
  • 4. Learning outcomes<br /> Analyze and present business data.<br /> Demonstrate an understanding of the role of statistics for business-focused research<br />
  • 5. Topics<br />Data and statistics<br />Descriptive statistics<br /> Tables and charts<br /> Numerical methods (measures of centrality and measures of disperal)<br />Types of data<br />Probability and Probability Sampling<br />
  • 6. Topics<br />Interval Estimation<br />Hypothesis testing<br />Means comparison, 2 groups and 3 groups<br />Regression analysis<br />Review<br />
  • 7. Assessments<br />Midterm Examination 20%<br />Project 30%<br />Final examination 50%<br />
  • 8. Statistics is:<br /> The science of organizing and analyzing information to make that information more easily understood.<br /> Statistics describes a set of tools that help you organize, describe, and interpret information<br /> Test scores, patient complaints, test one drug against another.<br />
  • 9. Statistics are part<br /> Of critical thinking skills which call for people to use quantitative and quantative information to make decisions.<br /> They let us make judgments about the world around us.<br />
  • 10. Categories of stats<br />Descriptive<br /> Look at the characteristics of a data set<br /> Age<br /> Gender<br />Inferential statistics<br /> Let us make inferences about the data and populations.<br />
  • 11. Data sets<br />
  • 12. Descriptive statistics<br />Measures of centrality<br /> Mean<br /> Median<br /> Mode<br />
  • 13. Descriptive statistics<br />Measures of Dispersal<br /> Range<br /> Variance<br /> Standard Deviation<br />
  • 14. Inferential statistics<br /> You are interested in finding out which is the most appealing name for a new brand of potato chips. You find a group of potato chip eaters that is representative of all potato chip eaters and ask these people to tell which names for potato chips that they like best. Then you extrapolate (infer) the findings to a huge group of potato chip eaters<br />

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