Descriptive Statistics: Textual & Graphical (pdf)

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Can’t interpret raw data • Clearly presenting these data in their raw form would tell the administrator little or nothing about trash collection in Sampleton • For example: • How many tons of trash do most teams collect? • Do the teams seem to collect about the same amount, or does their performance vary?
Frequency Distributions • The most basic restructuring of raw data to facilitate understanding • Definition: a table that pairs data values (or ranges of data values) with their frequency of occurrence
Page 1 and 2: Descriptive Statistics: Textual and
Page 3 and 4: Descriptive Statistics • Descript
Page 5: Example Number of Tons of Trash col
Page 9 and 10: Example, cont. • The data values
Page 11 and 12: Constructing a Frequency Distributi
Page 13 and 14: Grouped Frequency Distributions •
Page 15 and 16: Grouped Frequency Distribution •
Page 17 and 18: Percentage Distributions • Suppos
Page 19 and 20: Must convert to compare • Easiest
Page 21 and 22: Cumulative Frequency Distributions
Page 23 and 24: In-Class Task • The fire chief of
Page 25 and 26: Solution Response Time Response Tim
Page 27 and 28: Bad Table - What’s wrong? Percept
Page 29 and 30: Simplicity vs. Detail • In making
Page 31 and 32: Feature 2 - Divides the data in a w
Page 33 and 34: Feature 5 - Clear Percentages Grade
Page 35 and 36: Graphical Representation of Tabular
Page 37 and 38: Graph Basics • A vertical axis or
Page 39 and 40: The Bar Graph • A bar graph’s b
Page 41 and 42: Bar Graph Example • Next, the tea
Page 43 and 44: The Histogram • A histogram is si
Page 45 and 46: Clinic Example • To organize thei
Page 47 and 48: Clinic Example • Next, to create
Page 49 and 50: The Frequency Polygon (Line Graph)
Page 51 and 52: Clinic Example • Next, they decid
Page 53 and 54: Pie Chart • A pie chart is a very
Page 55 and 56: Task • Using the majors frequency

Descriptive Statistics: Textual & Graphical (pdf)

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