Statistics : Preview of Statistics 2.0 Program,9780030178887

Statistics : Preview of Statistics 2.0 Program

by ;
Edition: 5th
ISBN13: 9780030178887
ISBN10: 0030178886
Format: Hardcover
Pub. Date: 1999-06-01
Publisher(s): Thomson Learning
List Price: $86.95

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Table of Contents

Introduction
1(12)
Why Statistics?
2(1)
What Is Statistics?
3(1)
Descriptive Statistics
3(1)
Inferential Statistics
3(1)
Importance of Perspective
3(2)
Two Types of Data
4(1)
Quantitative Data
5(1)
Qualitative Data
5(1)
Quantitative or Qualitative?
6(1)
Two Types of Variables
6(1)
General Definition
6(1)
Independent and Dependent Variables
7(2)
How to Use This Book
9(4)
Summary
10(1)
Review Exercises
11(2)
PART I Descriptive Statistics Organizing and Summarizing Data 13(162)
Describing Data with Tables
15(24)
Frequency Distributions for Quantitative Data
16(1)
Ungrouped Data
16(1)
Grouped Data
17(2)
Guidelines
19(1)
How Many Classes?
19(1)
Gaps between Classes
20(1)
Doing It Yourself
20(3)
Outliers
23(1)
Other Types of Frequency Distributions
24(1)
Relative Frequency Distributions
24(1)
Percents or Proportions?
25(1)
Cumulative Frequency Distributions
26(2)
Percentile Ranks
28(1)
Exact Percentile Ranks (from Ungrouped Data)
28(1)
Frequency Distributions for Qualitative Data
29(2)
Interpreting Distributions Constructed by Others
31(8)
Summary
32(1)
Review Exercises
33(6)
Describing Data with Graphs
39(20)
Graphs for Quantitative Data
40(1)
Histograms
40(2)
Frequency Polygons
42(2)
Stem and Leaf Displays
44(1)
Typical Shapes
45(3)
A Graph for Qualitative Data
48(1)
Bar Graphs
48(2)
Misleading Graphs
50(1)
Some Tricks
50(1)
Doing It Yourself
51(1)
Computer Printout
51(8)
Summary
56(1)
Review Exercises
57(2)
Describing Data with Averages
59(14)
Averages for Quantitative Data
60(1)
Mole
60(1)
Median
61(2)
Mean
63(3)
Which Average?
66(2)
Special Status of the Mean
68(1)
Averages for Qualitative Data
68(1)
Using the Word Average
69(4)
Summary
70(1)
Review Exercises
70(3)
Describing Variability
73(18)
Quantitative Measures of Variability
74(1)
Intuitive Approach
74(1)
Range
75(1)
Interquartile Range (IQR)
75(2)
Variance
77(1)
Definition Formula for Variance
78(2)
Computation Formula for Sample Variance
80(1)
Weakness of Variance
80(2)
Standard Deviation
82(1)
Standard Deviation: An Interpretation
83(1)
Standard Deviation: Some Generalizations
83(2)
Standard Deviation: A Measurement of Distance
85(2)
Measures of Variability for Qualitative Data
87(4)
Summary
87(1)
Review Exercises
88(3)
Normal Distributions: (I): Basics
91(12)
The Theoretical Normal Curve
92(1)
Properties of the Normal Curve
93(1)
z Scores
94(2)
Standard Normal Curve
96(1)
Standard Normal Table
97(2)
Example: FBI Applicants
99(1)
Key Facts to Remember
100(3)
Summary
101(1)
Review Exercises
101(2)
Normal Distributions: (II): Applications
103(16)
Finding Proportions
104(1)
Example: Finding Proportion below a Score (to Left of Mean)
104(1)
Example: Finding Proportion below a Score (to Right of Mean)
104(2)
Example: Finding Proportion between Two Scores
106(2)
Example: Finding Proportions beyond Pairs of Scores
108(2)
Finding Scores
110(1)
Example: Finding a Score (to Right of Mean)
110(3)
Example: Finding Pairs of Scores (on Both Sides of Mean)
113(6)
Summary
116(1)
Review Exercises
116(3)
More about z Scores
119(10)
z Scores for Non-normal Distributions
119(2)
Standard Scores
121(1)
Transformed Standard Scores
121(1)
Converting to Transformed Standard Scores
121(2)
Percentile Ranks Again
123(6)
Summary
124(1)
Review Exercises
125(4)
Describing Relationships: Correlation
129(24)
An Intuitive Approach
130(2)
Scatterplots
132(4)
A Correlation Coefficient for Quantitative Data: r
136(1)
z Score Formula for r
137(4)
Computation Formula for r
141(1)
Interpretation of r
142(2)
Interpretation of r2 (See Section 10.9)
144(1)
Correlation Not Necessarily Cause-Effect
144(1)
Other Types of Correlation Coefficients
145(1)
Computer Printout
146(7)
Summary
149(1)
Review Exercises
150(3)
Prediction
153(22)
Two Rough Predictions
153(2)
A Prediction Line
155(2)
Least Squares Prediction Line
157(1)
Least Squares Equation
158(3)
Graphs or Equations?
161(1)
Standard Error of Prediction, Sy/x
161(2)
Assumptions
163(1)
More Complex Prediction Equations
164(1)
Interpretation of r2
165(5)
r Revisited
170(5)
Summary
171(1)
Review Exercises
172(3)
PART II Inferential Statistics Generalizing Beyond Data 175(392)
Populations and Samples
177(12)
Why Samples?
178(1)
Populations
178(1)
Samples
179(1)
Random Samples
180(1)
Tables of Random Numbers
181(2)
Some Complications
183(1)
Random Assignment of Subjects
183(2)
An Overview: Surveys or Experiments?
185(4)
Summary
186(1)
Review Exercises
186(3)
Probability
189(12)
Definition
190(1)
Addition Rule
190(2)
Multiplication Rule
192(3)
Probability and Statistics
195(6)
Summary
197(1)
Review Exercises
197(4)
Sampling Distribution of the Mean
201(16)
An Example
201(2)
Creating a Sampling Distributions from Scratch
203(3)
Some Important Symbols
206(1)
Mean of All Sample Means (μx)
207(1)
Standard Error of the Mean (σx)
208(2)
Shape of the Sampling Distribution
210(1)
Why the Central Limit Theorem Works
211(1)
Other Sampling Distributions
212(5)
Summary
213(1)
Review Exercises
214(3)
Introduction to Hypothesis Testing: The z Test
217(16)
Testing a Hypothesis about SAT Scores
218(2)
z Test for a Population Mean
220(3)
Step--by--Step Procedure
223(1)
Statement of the Research Problem
223(1)
Null Hypothesis (H0)
224(1)
Alternative Hypothesis (H1)
225(1)
Decision Rule
226(1)
Calculations
227(1)
Decision
227(1)
Interpretation
228(5)
Summary
229(1)
Review Exercises
230(3)
More about Hypothesis Testing
233(14)
Hypothesis Testing: An Overview
233(2)
Strong or Weak Decisions
235(1)
Why the Research Hypothesis Isn't Tested Directly
236(1)
One--tailed and Two--tailed Tests
237(4)
Choosing a Level of Significance (α)
241(6)
Summary
243(1)
Review Exercises
244(3)
Controlling Type I and Type II Errors
247(16)
Testing a Hypothesis about Vitamin C
247(2)
Four Possible Outcomes
249(1)
If H0 Is True
250(2)
If H0 Is False Because of a Large Effect
252(2)
If H0 Is False Because of a Small Effect
254(1)
Influence of Sample Size
255(2)
Selection of Sample Size
257(1)
Power Curves
258(5)
Summary
259(1)
Review Exercises
260(3)
Estimation
263(14)
Estimating μ for SAT Scores
263(1)
Point Estimate for μ
264(1)
Confidence Interval for μ
264(1)
Why Confidence Intervals Work
265(3)
Confidence Interval for μ Based on z
268(1)
Interpretation of a Confidence Interval
269(1)
Level of Confidence
270(1)
Effect of Sample Size
271(1)
Hypothesis Tests or Confidence Intervals?
271(1)
Confidence Interval for Population Percent
272(2)
Other Types of Confidence Intervals
274(3)
Summary
274(1)
Review Exercises
275(2)
t Test for One Sample
277(16)
t Test for a Population Mean
277(2)
t Sampling Distribution
279(1)
t Tables
280(1)
Estimating the Population Standard Deviation
281(3)
t Ratio
284(1)
Confidence Intervals for μ Based on t
285(2)
Assumptions
287(1)
Degrees of Freedom
287(2)
Hypothesis Tests: An Overview
289(4)
Summary
289(1)
Review Exercises
290(3)
t Test for Two Independent Samples
293(18)
Blood--Doping Experiment
294(1)
Two Independent Samples
294(1)
Two Hypothetical Populations
294(1)
Statistical Hypotheses
295(2)
Sampling Distribution of X1-X2
297(1)
Mean of the Sampling Distribution of X1-X2
297(1)
Standard Error of the Sampling Distribution of X1-X2
298(1)
z Test
298(1)
t Ratio
298(1)
Calculating the Estimated Standard Error
299(3)
t Test for the Blood-Doping Experiment
302(1)
Confidence Interval for μ1-μ2
303(3)
Assumptions
306(5)
Summary
306(1)
Review Exercises
307(4)
t Test for Two Matched Samples
311(20)
Two Population Means
312(1)
Matching Pairs of Athletes in the Blood--Doping Experiment
312(1)
Two Matched Samples
312(1)
Difference Scores (D)
313(1)
Statistical Hypotheses
313(1)
Sampling Distribution of D
314(1)
t Ratio
314(1)
t Test for the Blood--Doping Experiment
315(3)
Confidence Interval for μD
318(1)
To Match or Not to Match?
319(1)
Using the Same Subjects in Both Groups (Repeated Measures)
320(1)
Assumptions
321(1)
Three t Tests for Population Means: An Overview
321(3)
Population Correlation Coefficient
324(1)
t Test for the Greeting Card Exchange
324(2)
Assumptions
326(1)
A Limitation
326(5)
Summary
326(1)
Review Exercises
327(4)
Beyond Hypothesis Tests: p-Values and Effect Size
331(14)
p-Values
332(1)
Definition
332(1)
Finding p-Values
333(1)
Reading p-Values Reported by Others
334(1)
Merits of Less Structured (p-Value) Approach
335(1)
Level of Significance or p-Value?
335(1)
A Note on usage
336(1)
Computer Printout
337(1)
Effect Size
338(1)
Statistically Significant Results
338(1)
Squared Point Biserial Correlation, r2pb
339(2)
Small, Medium, or Large Effect?
341(1)
A Recommendation
341(4)
Summary
342(1)
Review Exercises
342(3)
Analysis of Variance (One Way)
345(30)
Testing a Hypothesis about Responsibility in Crowds
346(2)
Two Sources of Variability
348(2)
F Ratio
350(1)
F Test
350(3)
Variance Estimates
353(1)
Sum of Squares (SS)
354(1)
Degrees of Freedom (df)
355(2)
Mean Squares (MS) and the F Ratio
357(2)
F Tables
359(1)
Notes on Usage
360(2)
Assumptions
362(1)
Two Cautions
362(1)
Small, Medium, or Large Effect?
362(2)
Multiple Comparisons
364(1)
Scheffe's Test
365(2)
Other Multiple Comparison Tests
367(1)
F Test Is Nondirectional
367(1)
Computer Printout
368(7)
Summary
370(1)
Review Exercises
371(4)
Analysis of Variance (Two Way)
375(24)
Testing Hypotheses about Reactions of Males and Females in Crowds
376(1)
Preliminary Interpretations
376(3)
Three F Ratios
379(2)
Variance Estimates
381(1)
Sum of Squares (SS)
382(1)
Degrees of Freedom (df)
383(2)
Mean Squares (MS) and F Ratios
385(1)
F Tables
386(1)
Assumptions
387(1)
Importance of Equal Sample Sizes
387(1)
Interaction
387(3)
Describing Interactions
390(1)
Small, Medium, or Large Effect?
390(1)
More Detailed Analyses
391(1)
Other Types of ANOVA
391(1)
Computer Printout
392(7)
Summary
393(1)
Review Exercises
394(5)
Chi-Square (x2) Test for Qualitative Data
399(24)
One-Way x2 Test
400(1)
Statistical Hypotheses
400(2)
Observed and Expected Frequencies
402(1)
Calculation of x2
402(2)
X2 Tables and Degrees of Freedom
404(1)
X2 Test
405(2)
X2 Test Is Nondirectional
407(1)
Two-Way x2 Test
407(1)
Statistical Hypotheses
408(1)
Observed and Expected Frequencies
409(3)
x2 Tables and Degrees of Freedom
412(1)
X2 Test
413(1)
Some Precautions
414(1)
Checking Importance
415(1)
Computer Printout
416(7)
Summary
418(1)
Review Exercises
419(4)
Tests for Ranked Data
423(22)
Use Only When Appropriate
424(1)
A Note on Terminology
424(1)
Mann-Whitney U Test (Two Independent Samples)
425(1)
Why Not a t Test?
425(2)
Statistical Hypotheses for U
427(1)
Calculation of U
427(2)
U Tables
429(1)
Decision Rule
429(2)
Directional Tests
431(2)
Wilcoxon T Test (Two Matched Samples)
431(2)
Why Not a t Test?
433(1)
Statistical Hypotheses for T
433(1)
Calculation of T
433(1)
T Tables
434(1)
Decision Rule
434(2)
Kruskal-Wallis H Test (Three or More Independent Samples)
436(1)
Why Not an F Test?
436(1)
Statistical Hypotheses for H
437(1)
Calculation of H
438(1)
X2 Tables
438(1)
Decision Rule
439(1)
H Test is Nondirectional
440(1)
General Comment: Ties
440(5)
Summary
441(1)
Review Exercises
441(4)
Postscript: Which Test?
445(122)
Descriptive or Inferential Statistics?
445(1)
Hypothesis Tests or Confidence Intervals?
446(1)
Quantitative or Qualitative Data?
446(2)
Distinguishing between the Two Types of Data
448(1)
One, Two, or More Groups?
448(2)
Concluding Comments
450(3)
Review Exercises
450(3)
Appendices
A Math Review
453(8)
B Levels of Measurement
461(6)
C Answers to Exercises
467(78)
D Tables
545(14)
E Glossary
559(8)
Index 567

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