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ECON 3790 Statistics for Business and Economics

ECON 3790 Statistics for Business and Economics. Prerequisite: Math 1549, 1552, 1570, or 1571. Classroom Instructor: Tod Porter. Office hours: Monday & Wednesday 1:00‐2:00, Tuesday & Thursday 10:00‐11:00, or by appointment

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ECON 3790 Statistics for Business and Economics

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  1. ECON 3790Statistics for Business and Economics Prerequisite: Math 1549, 1552, 1570, or 1571

  2. Classroom Instructor: Tod Porter Office hours: Monday & Wednesday 1:00‐2:00, Tuesday & Thursday 10:00‐11:00, or by appointment My office is located in the Economics Department suite, room 303 Computer Lab Instructor: Ross Munroe

  3. Points in Course • Homeworks and quizzes (75 pts.) • Three midterm exams (225 pts.) • Final exam (100 pts.) • Computer lab (100 pts.)

  4. Grading Scale • A, 85-100% • B, 75-84% • C, 65-74% • D, 55-64% • F, 0-54%

  5. Other • Course materials will be posted on my web site, www.as.ysu.edu/~tsporter • See me privately if you need special accommodations due to a disability • Cell phones MUST be turned off during quizzes and exams • This is a course where it is essential for you to keep up with the material

  6. Where is Statistics Used? • Accounting • Marketing • Finance • Economics

  7. For Today’s Graduate, Just One Word: Statistics By STEVE LOHR Published: August 5, 2009 MOUNTAIN VIEW, Calif. — At Harvard, Carrie Grimes majored in anthropology and archaeology and ventured to places like Honduras, where she studied Mayan settlement patterns by mapping where artifacts were found. But she was drawn to what she calls “all the computer and math stuff” that was part of the job. “I keep saying that the sexy job in the next 10 years will be statisticians,” said Hal Varian, chief economist at Google. “And I’m not kidding.” The rising stature of statisticians, who can earn $125,000 at top companies in their first year after getting a doctorate, is a byproduct of the recent explosion of digital data. In field after field, computing and the Web are creating new realms of data to explore — sensor signals, surveillance tapes, social network chatter, public records and more. And the digital data surge only promises to accelerate, rising fivefold by 2012, according to a projection by IDC, a research firm.

  8. Objectives of the Course • Teach you how to apply basic statistical techniques • Make you a knowledgeable consumer of more advanced statistical techniques

  9. Chapter 1Data and Statistics

  10. Making Inferences about Populations Population – The set of all elements of interest in a particular study Sample – A subset of the population

  11. Making Inferences about Populations Draw sample Population Sample Describe sample characteristics Infer population characteristics

  12. Descriptive vs. Inferential Statistics Descriptive statistics – Summaries of the characteristics of data Inferential statistics – Techniques used to infer the characteristics of the population using the sample data

  13. Data and Data Sets Data – The set of all elements of interest in a particular study Data set – All of the data collected for a particular study

  14. Components of a Data Set Element – The entities on which data are collected Variable – A characteristic of interest for the elements Observation – Set of measurements for a specific element

  15. Example of a Data Set

  16. Scales of Measurement Nominal Scale – When the data for a variable consists of labels or names used to identify some attribute Ordinal Scale – Nominal data where the order or rank of the data is meaningful

  17. Scales of Measurement, cont. Interval Scale – When the data show the properties of ordinal data the interval between values is express in terms of a fixed unit of measure Ratio Scale – The data have all the properties of interval data and the ratio of two variables is meaningful (must have a zero value)

  18. Example of Interval Scale A size 0 dress would correspond to a 8 inch waist

  19. Scales of Measurement, cont. • What scale of measurement would be used for the following variables? • Distance from car to class • Football jersey number • Temperature • Ranking of satisfaction (5 = extremely satisfied, 1 = extremely dissatisfied)

  20. Qualitative vs. Quantitative Data Qualitative – Labels or names used to identify the attribute of each element (Nominal or ordinal measurement) Quantitative – Numeric values that indicate how much or how many of something (Interval or ratio measurement)

  21. Types of Data Cross-sectional – Data collected at approximately the same point in time Time series – Aggregated values collected at different points in time Panel – Data collected from the same elements at different points in time

  22. Types of Statistical Studies Experimental – Researcher has direct control over the variable being studied Observational – The researcher can only observe the variable being studied

  23. Practice Homework Pages 20-21, #10, 12, 13 Practice homework will not be graded, the answers are in the back of the book

  24. Chapter 2Descriptive Statistics:Tabular and Graphical Presentations

  25. Frequency Distribution A tabular summary of data showing the number (frequency) of items in non-overlapping classes

  26. Frequency Distribution

  27. Relative and PercentFrequency Distributions Relative Frequency of a Class = (Frequency of the class)/n Percent Frequency of a Class = 100 x (Frequency of the class)/n

  28. Frequency Distribution

  29. Bar Graph of Frequency Distribution

  30. Pie Chart of Frequency Distribution

  31. Frequency Distributions and Quantitative Data Building a frequency distribution for quantitative data: Choose number of classes Determine the width of each class Define the class limits, the classes must include all values and be mutually exclusive

  32. Frequency Distributions and Quantitative Data Approximate class width = (Largest data value – Smallest data value) (Number of classes)

  33. Frequency Distributions and Quantitative Data Class limits Lower class limit – the smallest possible data value assigned to the class Upper class limit – the largest possible data value assigned to the class ___________________________________ Class midpoint = (Lower class limit + Upper class limit)/2

  34. Frequency Distributions and Quantitative Data Assuming five classes, the approximate class width would be ($95,000 - $25,000)/5 = $14,000, round to $15,000

  35. Frequency Distributions and Quantitative Data General principles for creating classes: Minimize empty classes and classes with very low values, but don’t make classes so large important information is obscured (4 to 20 classes) Choose class limits that are rounded to some easy-to-read value Make sure the classes include all values are mutually exclusive

  36. Histogram

  37. Frequency Distributions and Quantitative Data What to do in the case of extreme values? Class width = ($1,025,000 - $25,000)/5 = $200,000

  38. Frequency Distributions and Quantitative Data For data sets with extreme values: - Use classes of unequal width - Use open-ended classes

  39. Ogive A graphical representation of a cumulative frequency distribution.

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