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Introduction to Mathematical Literacy Second Edition [Loose-leaf]

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  • Formāts: Loose-leaf, 277 pages
  • Izdošanas datums: 06-Jul-2017
  • Izdevniecība: Kendall/Hunt Publishing Co ,U.S.
  • ISBN-10: 1524935646
  • ISBN-13: 9781524935641
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Introduction to Mathematical Literacy Second EditionPalielināt
  • Formāts: Loose-leaf, 277 pages
  • Izdošanas datums: 06-Jul-2017
  • Izdevniecība: Kendall/Hunt Publishing Co ,U.S.
  • ISBN-10: 1524935646
  • ISBN-13: 9781524935641
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781524935641.html
Introduction to Mathematical Literacy is intended primarily for non- STEM majors (i.e. non-science majors), though STEM majors can also benefit greatly from reading it. It is divided, essentially, into two parts. Chapters 1 and 2 form one unit and Chapters 3 7 form a second unit. Chapter 1 deals with elections which have more than 2 candidates. How many different ways are there to decide the winner, and which method is the fairest of them all? You'll be very surprised at the answer to this question! Chapter 2 deals with the problem of apportionment. Chapters 3-7 comprise a simple and basic unit that teaches us how to deal with data sets. Just as the famous American Express commercial, in touting the American Express card, beseeches you to ""not leave home without it"", so too, in this technological age, you cannot leave College without knowing this material.
Preface vii
Chapter 1 Voting Methods
1(80)
1.1 Plurality and Runoff Methods
3(16)
Runoff Elections
7(2)
Preference Rankings
9(10)
1.2 Borda's Method: A Scoring System
19(12)
1.3 Head-to-Head Comparisons
31(14)
Single-Peaked Preference Rankings
36(9)
1.4 Approval Voting
45(14)
1.5 The Search for an Ideal Voting System
59(22)
Writing Exercises
65(1)
Projects
66(4)
Key Terms
70(9)
Suggested Readings
79(2)
Chapter 2 Apportionment: Sharing What Cannot Be Divided Arbitrarily
81(70)
2.1 Quota Methods
82(13)
Hamilton's Method
86(3)
Lowndes' Method
89(6)
2.2 Early Divisor Methods
95(24)
Jefferson's Method
95(10)
Webster's Method
105(14)
2.3 Apportionment in Today's House of Representatives
119(13)
The Hill-Huntington Method
120(5)
Other Apportionment Methods
125(7)
2.4 The Search for an Ideal Apportionment Method
132(19)
Writing Exercises
138(1)
Projects
139(1)
Key Terms
140(1)
Review Test
141(9)
Suggested Readings
150(1)
Chapter 3 Introduction
151(18)
3.1 Overview of Course (Basic Concepts)
152(7)
Population
152(2)
Sample
154(1)
Random Sample
154(3)
Internal and External Validity
157(2)
3.2 Why We Sample
159(10)
Sampling to Determine μ
159(2)
Sampling to Determine P
161(2)
Excel Excitement
163(2)
Summary
165(1)
Exercises
166(3)
Chapter 4 Organizing and Analyzing Data
169(62)
4.1 Graphical Representations
171(7)
Histogram
171(3)
Population Histograms
174(1)
Frequency Polygon
175(1)
Circle Graph
176(2)
4.2 Measures of Central Tendency (Ungrouped Data)
178(5)
Arithmetic Mean
178(2)
Median
180(2)
Mode
182(1)
Comparison of the Mean, Median, and Mode
183(1)
4.3 Measures of Dispersion or Spread (Ungrouped Data)
183(7)
Range
184(1)
Standard Deviation
184(6)
4.4 Estimating Population Characteristics
190(1)
4.5 Measures of Central Tendency and Dispersion/Spread (Grouped Data)
191(6)
Mean
191(3)
Standard Deviation
194(2)
Modal Class
196(1)
4.6 Z Scores and the Use of the Standard Deviation
197(5)
Two Important Findings
200(2)
4.7 Additional Descriptive Topics
202(2)
Pictogram
202(1)
Stem-and-Leaf Display
202(1)
Box-and-Whisker Plot
203(1)
Quartiles
204(1)
Percentiles
204(1)
4.8 Writing Research Reports
204(27)
Background Statement
205(1)
Design and Procedures of the Study
205(1)
Results
206(1)
Analysis and Discussion
207(1)
Conclusions and Recommendations
207(1)
Excel Excitement
207(7)
Summary
214(2)
Exercises
216(10)
Research Reports
226(5)
Chapter 5 Probability
231(44)
5.1 Probability Defined: Empirically
232(4)
Subjective Probability
236(1)
5.2 Probability Defined: Classically
236(8)
Two Fundamental Properties
238(3)
AND and OR Statements
241(1)
Practice Exercises
241(2)
Use of Mathematical Formulas in Simple Experiments
243(1)
5.3 More Complex Experiments: Tree Diagram
244(4)
5.4 More Complex Experiments: Multiplication Rules
248(8)
Dependent and Independent Events
249(5)
Counting Principle
254(2)
5.5 Early Gambling Experiments Leading to Discovery of the Normal Curve
256(6)
5.6 Additional Probability Topics
262(13)
Mean and Standard Deviation of a Discrete Probability Distribution
262(1)
Expected Value
263(2)
Permutations and Combinations
265(2)
Summary
267(3)
Exercises
270(5)
Chapter 6 Normal Distribution
275(52)
6.0 Origins of the Concept
276(3)
6.1 Idealized Normal Curve
279(7)
Characteristics of the Normal Curve
280(1)
Use of the Normal Curve Table
281(5)
6.2 Applications: Idealized Normal Curve
286(5)
6.3 Working Backward with the Normal Curve Table
291(4)
Applications
292(3)
6.4 Binomial Distribution: An Introduction to Sampling
295(8)
Sampling from a Two-Category Population
296(4)
Normal Curve Approximation to the Binomial Sampling Distribution
300(2)
Terminology
302(1)
6.5 Binomial Sampling Distribution: Applications
303(24)
Importance of Random Selection
306(1)
Importance of a Large Population
306(4)
Excel Excitement
310(3)
Summary
313(2)
Exercises
315(8)
Endnotes
323(4)
Chapter 7 Central Limit Theorem
327(26)
7.1 Central Limit Theorem
328(5)
7.2 Applying the Central Limit Theorem
333(5)
Random Selection
337(1)
7.3 How n and o Affect σx
338(3)
How σ Affects σx
338(1)
How n Affects σx
338(3)
7.4 Central Limit Theorem Applied to Nonnormal Populations
341(12)
Excel Excitement
342(4)
Summary
346(1)
Excel
347(1)
Exercises
347(3)
Endnotes
350(3)
Answer key 353(40)
Index 393