# ch02 - Chapter 2 Graphical Descriptive Techniques 1 2.1...

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1 Graphical Descriptive Techniques Chapter 2

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2 2.1 Introduction Descriptive statistics involves the arrangement, summary, and presentation of data, to enable meaningful interpretation, and to support decision making. Descriptive statistics methods make use of graphical techniques numerical descriptive measures. The methods presented apply to both the entire population the population sample
3 2.2 Types of data and information A variable - a characteristic of population or sample that is of interest for us. Cereal choice Capital expenditure The waiting time for medical services Data - the actual values of variables Interval data are numerical observations Nominal data are categorical observations Ordinal data are ordered categorical observations

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4 Types of data - examples Interval data Age - income 55 75000 42 68000 . . . . Weight gain +10 +5 . . Nominal Person Marital status 1 married 2 single 3 single . . . . Computer Brand 1 IBM 2 Dell 3 IBM . . . .
5 Types of data - examples Interval data Age - income 55 75000 42 68000 . . . . Nominal data With nominal data, all we can do is, calculate the proportion of data that falls into each category. IBM Dell Compaq Other Total 25 11 8 6 50% 22% 16% 12% Weight gain +10 +5 . .

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6 Types of data – analysis Knowing the type of data is necessary to properly select the technique to be used when analyzing data. Type of analysis allowed for each type of data Interval data – arithmetic calculations Nominal data – counting the number of observation in each category Ordinal data - computations based on an ordering process
7 Cross-Sectional/Time-Series Data Cross sectional data is collected at a certain point in time Marketing survey (observe preferences by gender, age) Test score in a statistics course Starting salaries of an MBA program graduates Time series data is collected over successive points in time Weekly closing price of gold Amount of crude oil imported monthly

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8 2.3 Graphical Techniques for Interval Data Example 2.1 : Providing information concerning the monthly bills of new subscribers in the first month after signing on with a telephone company. Collect data Prepare a frequency distribution Draw a histogram
9 Largest observation Collect data Bills 42.19 38.45 29.23 89.35 118.04 110.46 0.00 72.88 83.05 . . (There are 200 data points Prepare a frequency distribution How many classes to use? Number of observations Number of classes Less then 50 5-7 50 - 200 7-9 200 - 500 9-10 500 - 1,000 10-11 1,000 – 5,000 11-13 5,000- 50,000 13-17 More than 50,000 17-20 Class width = [Range] / [# of classes] [119.63 - 0] / [8] = 14.95 15 Largest observation Largest observation Smallest observation Smallest observation Smallest observation Smallest observati on Largest observation Example 2.1 : Providing information

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10 0 20 40 60 80 15 30 45 60 75 90 105 120 Bills Frequency Draw a Histogram Bin Frequency 15 71 30 37 45 13 60 9 75 10 90 18 105 28 120 14 Example 2.1 : Providing information
11 0 20 40 60 80 15 30 45 60 75 90 105 120 Bills Frequency What information can we extract from this histogram About half of all the bills are small 71+37=108 13+9+10=32 A few bills are in the middle range Relatively, large number of large bills 18+28+14=60 Example 2.1 : Providing information

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## This note was uploaded on 06/06/2011 for the course ADMS 2320 taught by Professor Rochon during the Spring '08 term at York University.

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ch02 - Chapter 2 Graphical Descriptive Techniques 1 2.1...

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