chapter2 - CHAPTER 2: DISPLAYING AND DESCRIBING DATA 2.1...

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CHAPTER 2: DISPLAYING AND DESCRIBING DATA 2.1 Variables Consider a class of 30 students: Gender (male, female) Hair color (blond, brown, black,. .) Height (in feet, inches) Weight (in pounds) Age (in years) ............ ............ Variable – any characteristic of a person or thing that can be expressed as a number or a label. Value the actual number or label assigned to a particular variable. VARIABLE POSSIBLE VALUES Gender male, female Hair color blond, brown, black,… Height 5’ 10’’, 6’1” Weight 160, 176, 180,… 1
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Variables Categorical Quantitative (values are labels) (values are numbers) Categorical variables: gender, hair color, marital status Quantitative variables: weight, height, age, income Describing Variables: Categorical: Specify the number (or percentage) of observations in each category. Quantitative: Determine the center (typical value) and spread (variability) of the data. Quantitative Variables Discrete Continuous (isolated points on the number line) (possible values form an i n t e r v a l ) 2
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Example: Discrete variables: number of children in a household Continuous variables : weight, height, income. Frequency Tables Frequency Table for Categorical Variables – lists all possible values for a variable with the corresponding number of observations (and relative frequency) for each value. Gender Frequency Relative Frequency Males 18 0.60 Females 12 0.40 Frequency table for Quantitative Variable - based on counts of observations in each of several non-overlapping intervals of equal width covering the whole range of values. Weight (in pounds) Frequency Relative Frequency 140 or higher but smaller than 150 3 0.10 150 or higher but smaller than 160 6 0.20 3
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2.2 Displaying Categorical Variables Consider a class of 30 with 18 males and 12 females. (a) Bar Graph A vertical bar erected over each category; the height of the bar is the frequency or the percentage of observations in the category. Percent 60 40 Females Males (b) Pie Chart Slices represent categories; size of each slice corresponds to the percentage for the category Females Males 4
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2.3 Displaying Quantitative Variables Example: 30 examination scores: 75 79 58 73 82 94 61 77 54 77 65 67 62 61 64 45 58 86 66 83 70 91 48 78 86 66 52 80 59 55 Conclusions: The scores vary, The lowest score=45, the highest score=94
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This note was uploaded on 04/10/2008 for the course STAT 151 taught by Professor Henrykkolacz during the Fall '07 term at University of Alberta.

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chapter2 - CHAPTER 2: DISPLAYING AND DESCRIBING DATA 2.1...

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