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Welcome to BUAD 310

Welcome to BUAD 310. Instructor: Kam Hamidieh Lecture 1, Monday January 13, 2014. Agenda. Brief introductions Go over the syllabus Today: Chapters 1,2, 3, and a bit of 4. “Critical Success Factors”. Spend time studying. Practice! Do the homework problems carefully .

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Welcome to BUAD 310

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  1. Welcome to BUAD 310 Instructor: Kam Hamidieh Lecture 1, Monday January 13, 2014

  2. Agenda • Brief introductions • Go over the syllabus • Today: Chapters 1,2, 3, and a bit of 4. BUAD 310 - Kam Hamidieh

  3. “Critical Success Factors” • Spend time studying. • Practice! Do the homework problems carefully. • Keep in mind that the statistical definitions will NOT always mean the same things as the English words. • Attend class, take extra notes, read the book and repeat! (Note: The solutions to in class exercise will not be posted!) • Seek help: come to office hours, see TA, etc. BUAD 310 - Kam Hamidieh

  4. What is Statistics? • Statistics – the discipline – is the science and art of extracting answers from data. • Data, data, data, ….it’s all about data! • Don’t forget the art part! • Statistics = Data Sciences? BUAD 310 - Kam Hamidieh

  5. Some Shameless Promotion… • “For Today’s Graduate, Just One Word: Statistics”. NY Times, August 2009 • Hal Varian, famous economist and now with Google, thinks statistics will be “sexy”! BUAD 310 - Kam Hamidieh

  6. Some Applications • Finance & Economics: forecasting asset values • Clinical trials: testing effectiveness of drugs • Marketing: purchasing trends • Business: fraud detection • Government: analysis of current economic situation, opinion polling • Sports: did anyone see MoneyBall? BUAD 310 - Kam Hamidieh

  7. Data • Data are collection of numbers, labels, or symbols, and the context of those values. • Most data are collected in data tables (like spreadsheets) with rows and columns. BUAD 310 - Kam Hamidieh

  8. More on Data Tables • Variables: These are the column headings that describe a common attribute shared by the rows. • Observations: These are the rows. Other names include: cases, items, units, individuals, and records. • The number of rows is often denoted by n. BUAD 310 - Kam Hamidieh

  9. More Definitions • A categorical variable places an observation into one of several groups or categories. • A categorical variable is called ordinal if its categories can be ordered. If not, then nominal. • A numerical variable takes numerical values for which arithmetic operations such as adding and averaging makes sense. The values usually have been recorded with some unit of measurements such as seconds or kilograms. • Another name for a numerical variable is a quantitative variable. • Two types of numerical variables: • Discrete: only whole numbers are possible, order and magnitude matters • Continuous: any value is conceivable BUAD 310 - Kam Hamidieh

  10. Why Care about Variable Types? • The type of statistical analysis you will use, will greatly depend on the type of variables you have. • You’ll be able to recognize mistakes in data - which consists of variables! BUAD 310 - Kam Hamidieh

  11. In Summary Types of Variable(Data) Categorical Numerical Nominal Ordinal Discrete Continuous BUAD 310 - Kam Hamidieh

  12. Example • Song, Artist, and Genre are categorical. Ordinal? • Size and length are numerical. • Here, n = 5. BUAD 310 - Kam Hamidieh

  13. Some More Examples Numerical • Age (years) • Car Manufacturer (GM, Ford, etc.) • Starting Salary in Dollars • Starting Salary (Low, Med., High) • Calcium Level (microgram per liters) • Current Smoker (yes or no) • Number on the flip of a die Categorical Numerical Categorical/Ordinal Numerical Categorical Numerical BUAD 310 - Kam Hamidieh

  14. In Class Exercise 1 Here’s a small part of a data set that describes the fuel economy (in miles per gallon) of 2008 model cars: Answer the following questions: • What are the individuals in this data? • What are the variables? • Which of these variables are categorical and which are numerical? BUAD 310 - Kam Hamidieh

  15. Time Series • A time series is a sequence of data that records a variable over time. • A time plot is just a graph of a time series: x-axis has time, y-axis has the variable value. • Example: • Go to http://finance.yahoo.com/ • Look up Apple stock (AAPL) BUAD 310 - Kam Hamidieh

  16. More on Time Series • Frequency – time spacing of data in a time series (e.g., daily, monthly, etc.) • Cross-sectional – data observed at the same time • Examples? BUAD 310 - Kam Hamidieh

  17. In Class Exercise 2 A supermarket mailed 4,000 uniquely identifiable coupons to homes in local residential communities. Each day of the next week, it counted the number of coupons that were redeemed and the size of the purchase. Assume that when a coupon is redeemed, its redemption time is also recorded. Answer: • Identify whether the data are cross sectional or a time series. • Give a name to each variable and indicate if the variable is categorical, ordinal, or numerical. • What is n here? BUAD 310 - Kam Hamidieh

  18. Exploratory Data Analysis (EDA) • Usually one of the first steps in analyzing data is exploratory data analysis: you use simple tables and graphs to look at, describe, and summarize the data. • This is an important step: • It can help you identify interesting aspects of the data. • You can find mistakes. • It can point to new ideas. • Most of chapters 3 & 4 are on EDA. • “Data snooping”? More on this later. BUAD 310 - Kam Hamidieh

  19. Displaying & Describing Data Tools for EDA Categorical Numerical Graphical Tables Graphical “Tables” • Bar charts • Pie charts • Frequency Tables • Relative Frequency Tables • Histograms • Boxplots • Scatter plots • Time plots • Numerical summaries such as SD, mean, median, etc. BUAD 310 - Kam Hamidieh

  20. Distributions • Distribution of a variable: what possible values the variable takes and how frequently it takes those values. • There are various ways to describe the distribution of a variable: tables, lists, histograms, etc. • Chapter 3: distributions of categorical variables, chapter 4: numerical variables. • Why do we care about the distribution of a variable? BUAD 310 - Kam Hamidieh

  21. Example • Suppose you randomly ask 10 students whether they smoke at least one cigarette per day or not. • Table on the right summarizes the data. (ID is unique for each student.) • The distribution of the categorical variable is: Frequency Table Relative Frequency Table BUAD 310 - Kam Hamidieh

  22. Bar Charts • In the bar chart the height of each bar is proportional to the count (or percent) in each category • Note: count = frequency • Example: Data on young American adults from the 1999 Current Population Survey BUAD 310 - Kam Hamidieh

  23. Bar Charts Here the variable of interest is educational Level and the bar chart summarizes this variable’s distribution. Question: Is educational level an ordinal or nominal variable? BUAD 310 - Kam Hamidieh

  24. Pie Charts • In the pie chart the area of each piece is proportional to the percent of individuals in each category BUAD 310 - Kam Hamidieh

  25. More on Bar & Pie Charts • Bar chart is called a Pareto chart when the categories are sorted by frequency. • Pie charts are commonly chosen to illustrate market shares or sources of revenue for a company. • Pie charts are less useful than bar charts if we want to compare actual counts (easier to compare bars than angles of wedges.) BUAD 310 - Kam Hamidieh

  26. Example • Suppose we asked 50 people at random what their favorite soft drink was. The possible choice are: Coke Classic, Diet Coke, Dr. Pepper, Pepsi-Cola, and Sprite. • Note our variable of interest here, favorite soft drink, is a categorical variable. • Ordinal or nominal? • The data are shown on the next slide. BUAD 310 - Kam Hamidieh

  27. Example (Continued) • We may have collected the data for various reasons. Examples: • Owner of the bar wants to know which one to stock more. • We want to know which drink is most popularso we can invest in that company’s shares. • May be we want to create our own drink, and we want to know who is our main competition. • Etc. • Is staring at a list like this really helpful in answering our question? BUAD 310 - Kam Hamidieh

  28. Example(Continued) • Which is the most popular drink? • Which is (are) the least popular drink? • Which is the most popular drink? • Which is (are) the least popular drinks? • Note: the bar graph shows the distribution of favorite soft drink variable. BUAD 310 - Kam Hamidieh

  29. The Area Principle • The area occupied by a part of the graph/chart that displays data should be proportional to the amount of data it represents • Charts decorated to attract attention often violate the area principle BUAD 310 - Kam Hamidieh

  30. Example BUAD 310 - Kam Hamidieh

  31. Example Mean Salaries at a Major University, 2002 - 2005 BUAD 310 - Kam Hamidieh

  32. In Class Exercise 3 Suppose you are given the table on the right which summarizes the shares of the US wireless telephone market in 2011. Answer: • What are your variables? What kind are they? • The % share does not add up to 100%. Is there something wrong here? • Create a bar graph of the data. • Suppose we had 100 million wireless users. How many were with Sprint? Assume that you can only subscribe to one wireless company. BUAD 310 - Kam Hamidieh

  33. Next Time • Focus on Chapter 4 on describing numerical data. BUAD 310 - Kam Hamidieh

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