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Descriptive Statistics

Descriptive Statistics. MSIT3000 Lecture 1. Objectives. Introduction to statistics. Descriptive Statistics within statistics. Compare and contrast Descriptive Statistics and Inferential Statistics. Text Reference: Ch 1; Ch 2 [1,2,10]. Scan 2.3. Homework (HLS) Reference: Chapters 1 & 2.

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Descriptive Statistics

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  1. Descriptive Statistics MSIT3000 Lecture 1

  2. Objectives • Introduction to statistics. • Descriptive Statistics within statistics. • Compare and contrast Descriptive Statistics and Inferential Statistics. Text Reference: Ch 1; Ch 2 [1,2,10]. Scan 2.3. Homework (HLS) Reference: Chapters 1 & 2.

  3. Statistics and decisions • Statistics provides the link between empirical evidence and theory. • A simplified but more general version of the “Managerial Decision Making” illustration in the text [Figure 1.5 p20]:

  4. Descriptive Statistics • Overview: • Terminology • Data • Graphical & quantitative methods

  5. Terminology:Population vs Sample • Population: A set of units (people, objects, transactions, events) that we are interested in studying. • Sample: A subset of the population. • Representative • Random • Simple Random Sample

  6. Data • Kinds of data – depending on the variable, data are either qualitative or quantitative. • Sources of data – determined by: • Limits based on the variables • Availability • Price

  7. Kinds of Data: Qualitative vs Quantitative • Qualitative data describe classes. • These classes have no natural ordering. • Examples include: gender, major field, political affiliation, native country, whether or not you had breakfast. • How much you had for breakfast is NOT qualitative. Nor is your GPA, the number of votes a party gets in an election or the population of a given country.

  8. Kinds of Data: Qualitative vs Quantitative (cont.) • Quantitative data can be ordered on a “meaningful numerical scale”. • Examples include: • Number of children • Class ranking • Weight • Temperature • Velocity

  9. Sources of data • Published • E.g. Wall Street Journal, the internet. • Designed experiments • E.g. medical trials. • Surveys • Consumer reports, employee, the long form of the U.S. Census. • Direct observations • Observations of behavior in airports, malls, etc. Also, how firms or the economy behave in given situations.

  10. Qualitative data - Key terms • Class: a category into which qualitative data can be classified. • Class frequency: the number of observations in the data set falling into a particular class. • Class relative frequency: the class frequency divided by the number of observations in the data set.

  11. Describing Qualitative data • Summary tables • Bar graphs • Pie charts

  12. Summary tables

  13. Pie Chart

  14. Bar Graph

  15. Describing Quantitative Data • Dot plots, stem-and-leaf diagrams, frequency histograms & relative frequency histograms are all the same. • Cumulative frequency histograms describe total change in a variable.

  16. Frequency Table

  17. Relative Frequency Histogram

  18. Cumulative Frequency Histogram

  19. Time Series • The data can be ordered in time as well as magnitude. • How do you detect patterns? • How can you show correlation? • Correlation (rough definition) measures how variable ‘move around together’.

  20. GDP Data

  21. Time Series Plot

  22. Topics in time series • Lags & frequency. • Real or imaginary patterns. • Trends • Seasonality

  23. Inferential Statistics • Inferential statistics deals with drawing valid conclusions from data. • This is the most important part of MSIT3000. • There are two main approaches: • Confidence intervals, where we estimate and specify our degree of certainty, and • Hypothesis testing, where we evaluate a claim using relevant data.

  24. Conclusion • Objectives addressed: • The role of statistics. • Overview of Descriptive Statistics: • Compare and contrast Population vs Sample. • Be able to use descriptive methods for qualitative data; specifically frequency tables, bar graphs and pie charts. • Be able to construct and interpret diagrams for quantitative data. • Be able to construct and interpret time series plots. • Describe the different kinds and sources of data. • Descriptive Statistics vs Inferential Statistics. • Future lectures will have problems for you to do, preferably prior to class, listed at the end of the lecture notes. Extra problems that are good practice but that we probably will not cover in class will be in parentheses.

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