This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: C22.0103: Statistics for Business Control: Regression and Forecasting Hong Luo Section 003, Spring 2009 Tue/Thu/Fri, 11:00  12:15pm, Tisch 200 Stern School of Business New York University Slides by Hong Luo, in collaboration with Bill Greene, Gary Simon, and Matt Grennan. UNDERSTANDING AND PRESENTING DATA Data and Figures Sampling and Descriptive Statistics Data and Figures Sampling and Descriptive Statistics What is DATA? ◮ a collection of facts ◮ “ . . . Data are often viewed as a lowest level of abstraction from which information and knowledge are derived” (http://en.wikipedia.org/wiki/Data, 01/11/09) ◮ elementary facts −→ useful (valuable) knowledge A “simple” collection of facts Pizza sales by type ◮ First step to considering a data set: ◮ What is the unit of observation ? ◮ Next steps: ◮ What do the data tell you? ◮ How can you use this information? ◮ What additional information would make these data more useful? A more complicated collection of facts US Crime Statistics ◮ What do these data tell you? Types of Data ◮ Univariate (one fact per observation) vs. Multivariate (many facts per observation) ◮ Quantitative (unit of measurement makes sense) ◮ Discrete: e.g. count data, Number of shootings by city during a period of time ◮ Continuous: e.g. Housing prices ◮ Qualitative (no unit of measurement, and arithmetic manipulation is usually meaningless ) ◮ Categorical: car brand, travel mode ◮ Ordinal: Satisfaction level from 0 to 10 ◮ Frameworks: Cross section, Time series, Longitudinal Ways to Organize/Present Data The first steps towards developing useful facts: ◮ Graphical ◮ Qualitative and Count Data ◮ pie chart ◮ bar chart ◮ Pareto chart ◮ Quantitative ◮ histogram ◮ box plot (next time) ◮ scatter plot ◮ Numerical: descriptive statistics (next time) MBA student data ◮ What is the unit of observation? ◮ What type of data is each variable? Some Basic Concepts ◮ Frequency: the number of observations in the data set falling into a particular category. e.g. 10 students with engineer major ◮ Relative frequency: the frequency divided by the total number of observations in the data set: 10 63 = . 159 ◮ Percentage: the relative frequency multiplied by 100. . 159 × 100 = 15 . 9 Pie and Bar Charts Which chart is easier to understand? ◮ the height of each bar is either the frequency, relative frequency or percentage of observations in each category. ◮ the size of each slice of the pie is proportional to the relative frequency of each category. Bar Charts with Groups ◮ What does this chart tell you? Pareto Chart ◮ 8020 rule or Pareto principal : often, about 80% of the effects come from about 20% of the causes Histogram ◮ Width of bin size matters!...
View
Full
Document
This note was uploaded on 11/07/2010 for the course ECON 0001 taught by Professor Kitsikopoulos during the Spring '08 term at NYU.
 Spring '08
 KITSIKOPOULOS

Click to edit the document details