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# Ch4 Categorical Data, Displaying and Describing.pdf

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Department
Course Code
BMGT 230
Professor
Kazim Ruhi

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Ch4 Categorical Data, Displaying and Describing Saturday,February 1,2014 9:35 PM Displaying and Describing Categorical Data Summarizing categorical data Two-way tables Relationships between categorical variables Marginal distributions Conditional distributions Simpson’s paradox Counts • Each category has a number of occurrences (frequency tables) • Percentages are useful (relative frequency tables) ↑ frequency-f ↑ relative frequency-Rf counts can be organized into frequency table / relative frequency tables Charts area principle: the area occupied by a part of the graph should correspond to the magnitude of the value it represents-if not,fraud! Constructing Bar and Pie Charts 1. Deﬁne categories for variables of interest 2. Determine the appropriate measure for each category a. For pie charts,the value assigned is the proportion of the total for all categories 3. Develop the chart a. For pie charts,the size of the slice is proportional to value and the sum must equal 100% Contingency Table aka Joint Table -the table shows how the individuals are distributed along each variable,depending on,or contingent on,the value of the other variables. • The marginal distribution of a variable in a contingency table is the total count that occurs when the value of that variable is held constant. • Here the marginal distribution indicated shows that there were 1502 respondents from China. • Each cell of a contingency table (any intersection of a row and column of the table) gives the count for a combination of values of the two variables. • Here the indicated cell shows that 4 respondents from India didn’t know how they felt about the question asked. • Rather than displaying the count in a contingency table,we may display the data as a percentage–as
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