Exploring or The Elements of Statistics with Applications to Economics and the Social Sciences

Exploring: Getting Started with Microsoft Office

Author: Robert Grauer

 

For Introductory Computer courses in Microsoft Office 2003 or courses in Computer Concepts with a lab component for Microsoft Office 2003 applications.

Master the How and Why of Office 2003!  Students master the "How and Why" of performing tasks in Office and gain a greater understanding of how to use the individual applications together to solve business problems.

Interesting textbook: More Natural Cures Revealed or A Tooth from the Tigers Mouth

The Elements of Statistics with Applications to Economics and the Social Sciences

Author: James B Ramsey

Designed for readers who want to stress the understanding of basic concepts and the development of "statistical intuition," this book demonstrates that statistical reasoning is everywhere and that statistical concepts are as important to readers' personal lives as they are to their future professional careers. Ramsey aims to develop statistically literacy—from the ability to read and think critically about statistics published in popular media to the ability to analyze and act upon statistics gathered in the business world. The underlying philosophy of this book is that given a reasonable level of depth in the analysis, the reader can later acquire a much more extensive, and even more intensive, exposure to statistics on their own or in the context of the work environment. Some use of calculus is included. Use of the computer is integrated throughout.

Booknews

Written for sophomore-level undergraduates, this textbook provides an introduction to statistics, especially as they are used in the social sciences. It explains fundamental ideas and principles, including descriptive statistics, probability, distribution, inference, bivariate distributions, regression, and ANOVA. Appendixes review basic mathematical concepts and conventions, offer directions for the student version of S-Plus 4.5, and provide supplementary information on nonparametric measures and Bayesian inference. Ramsey teaches at New York University. Annotation c. Book News, Inc., Portland, OR (booknews.com)

Table of Contents:

	Preface	xiii
Part 1	Introduction and Fundamental Ideas	1
Chapter 1	Statistics as Science	2
1.1	What You Will Learn in This Chapter	2
1.2	Introduction	2
1.3	Statistics: A Framework for Decision Making	3
1.4	Statistics and the Methodology of Science	7
1.5	Statistics as a Science	9
	The Subject Matter of Statistics	9
	Statistics and Science Interwined	10
1.6	Summary	12
	Case Study: Was There Age Discrimination in a Public Utility?	13
1.7	Addendum for the Reader	14
	Exercises	17
Chapter 2	Types of Variables, Measurements, and Explanation	20
2.1	What You Will Learn in This Chapter	20
2.2	Introduction	20
2.3	Types of Variables	21
	Cardinal Measurement	23
	Ordinal Measurement	24
	Categorical Variables	25
	Indices	25
	Time Series	26
2.4	Random and Deterministic Variables	26
2.5	Summary	32
	Case Study: Was There Age Discrimination in a Public Utility?	32
	Exercises	33
Part 2	Descriptive Statistics	37
Chapter 3	How to Describe and Summarize Random Data by Graphical Procedures	38
3.1	What You Will Learn in This Chapter	38
3.2	Introduction	38
3.3	Describing Data by Box-and-Whisker Plots	40
	The Median	41
	The Range	43
	Quartiles	44
	Box-and-Whisker Plots	45
3.4	Plotting Relative Frequencies	48
3.5	Cumulative Frequencies	51
3.6	Histogram	53
3.7	Summary	63
	Case Study: Was There Age Discrimination in a Public Utility?	65
	Exercises	69
Chapter 4	Moments and the Shape of Histograms	77
4.1	What You Will Learn in This Chapter	77
4.2	Introduction	77
4.3	The Mean, a Measure of Location	77
	An Aside on Notation	79
	Averaging Grouped Data	81
	Interpreting the Mean	83
4.4	The Second Moment as a Measure of Spread	85
4.5	General Definition of Moments	88
	The Third Moment as a Measure of Skewness	90
	The Fourth Moment as a Measure of Peakedness, or "Fat Tails"	92
4.6	Standardized Moments	93
	Some Practical Uses for Higher Moments	99
4.7	Standardization of Variables	104
	The Higher Moments about the Origin	105
	Higher Moments and Grouped Data	106
4.8	Summary	106
	Case Study: Was There Age Discrimination in a Public Utility?	107
	Exercises	110
Chapter 5	The Description of Bivariate Data	120
5.1	What You Will Learn in This Chapter	120
5.2	Introduction	120
5.3	Three-Dimensional Histograms	121
5.4	Scatter Plots	122
5.5	Standardization for Pairs of Random Variables	125
5.6	Covariation and m[subscript 11], the First Cross Product Moment	126
5.7	Linear Statistical Relationships and the Correlation Coefficient	135
5.8	The Correlation Coefficient and Slope	140
5.9	Rank Correlation	142
5.10	Bivariate Categorical Data	144
	Row Comparisons	145
	Column Comparisons	147
	Joint Comparisons	148
5.11	Summary	152
	Case Study: Was There Age Discrimination in a Public Utility?	153
	Exercises	159
Part 3	Probability and Distribution Theory	169
Chapter 6	The Theory of Statistics: An Introduction	170
6.1	What You Will Learn in This Chapter	170
6.2	Introduction	171
6.3	The Theory: First Steps	173
	The Sample Space	173
	Introducing Probabilities	175
	Probabilities of Unions and Joint Events	177
	A Mathematical Digression	180
	Calculating the Probabilities of the Union of Events	182
	The Definition of Probability for Sample Spaces of Discrete Events	184
6.4	Conditional Probability	185
	Summing Up the Many Definitions of Probability	190
6.5	Random Variables: Intuition Made Formal	191
	An Example Using Two Random Variables	193
6.6	Statistical Independence	197
	Application of the Results to Continuous Random Variables	199
	Consequences of the Equally Likely Principle	200
6.7	Summary	202
	Case Study: Was There Age Discrimination in a Public Utility?	203
	Excercises	205
Chapter 7	The Generation and Description of Discrete Probability Distributions	214
7.1	What You Will Learn in This Chapter	214
7.2	Introduction	215
7.3	Combinations and Permutations	215
7.4	Generating Binomial Probabilities	220
	The Convolution Sum	222
	Deriving the Binomial Distribution	222
	Parameters and the Shape of the Probability Distribution	228
	Theoretical Moments and the Shape of the Probability Distribution	230
7.5	Expectation	236
	Moment-Generating Functions for Discrete Variables	242
7.6	The Cumulative Distribution Function	246
7.7	The Poisson Probability Distribution	246
7.8	Summary	253
	Case Study: Was There Age Discrimination in a Public Utility?	254
	Exercises	254
Chapter 8	The Generation of Some Continuous Probability Distributions	267
8.1	What You Will Learn in This Chapter	267
8.2	Introduction	267
8.3	How to Express Probability in Terms of Continuous Random Variables	268
8.4	Theoretical Moments and Density Functions	277
8.5	The Uniform Distribution	278
8.6	The Normal, or Gaussian, Density Function and the Central Limit Theorem	281
	Standard Deviation and the Nonstandard Gaussian	286
	The Gaussian, or Normal, Distribution as an Approximation to the Binomial Distribution	288
	Moment-Generating Functions for Continuous Variables	296
	The Chebyshev Inequality	299
	Terminology	301
8.7	Summary	302
	Case Study: Was There Age Discrimination in a Public Utility?	303
	Exercises	303
Part 4	Basic Principles of Inference	313
Chapter 9	Elementary Sampling Theory	314
9.1	What You Will Learn in This Chapter	314
9.2	Introduction	314
9.3	An Illustrative Example	316
9.4	An Introduction to the Theory of Simple Random Sampling	320
9.5	Stratified Random Sampling	325
9.6	Summary	329
	Case Study: Was There Age Discrimination in a Public Utility?	330
	Exercises	332
Chapter 10	Estimation of Theoretical Moments and the Parameters of Probability Distributions	337
10.1	What You Will Learn in This Chapter	337
10.2	Introduction	337
10.3	Estimating Theoretical Moments: Large Sample Results	339
10.4	Estimating Moments and Parameters: Confidence Intervals and Small Sample Results	347
	Estimating a Binomial Probability	356
	Estimating the Poisson Parameter	360
	The Student's T Distribution	363
	The Chi-square Distribution and Confidence Intervals for the Variance	372
10.5	Maximum Likelihood Estimators	375
10.6	Summary	379
	Case Study: Was There Age Discrimination in a Public Utility?	380
	Exercises	381
Chapter 11	Hypothesis Testing: How to Discriminate between Two Alternatives	393
11.1	What You Will Learn in This Chapter	393
11.2	Introduction	393
11.3	The Basic Idea of Hypotheses Tests	394
	A Digression on the Interpretation of Rejection Regions	399
	How to Choose an Optimal Decision Rule	399
	Why Type I Error Is Usually Small	407
	The Special Rofe of the Null Hypothesis	409
11.4	Simple and Composite Hypotheses Tests	411
11.5	Two-Sided Hypotheses Tests	415
11.6	Tests of Proportions	417
11.7	Hypotheses Tests When the Variance Is Unknown	418
	Testing the Difference between Two Means	421
	An Aside on Statistical Significance	425
	P Values	426
11.8	Some Practical Examples	428
11.9	Summary	433
	Case Study: Was There Age Discrimination in a Public Utility?	435
	Exercises	436
Part 5	Bivariate Distributions, Regression, and ANOVA	449
Chapter 12	The Generation of Bivariate and Conditional Probability Distributions	450
12.1	What You Will Learn in This Chapter	450
12.2	Introduction	450
12.3	Some Pragmatic Examples	454
12.4	The Generation of a Bivariate Discrete Distribution	457
12.5	The Generation of a Bivariate Continuous Distribution	458
	The Conditional Normal Density Function	468
	Moments of Joint and Conditional Density Functions	470
12.6	Bivariate and Conditional Distributions Obtained by Sampling	474
12.7	Summary	476
	Case Study: Was There Age Discrimination in a Public Unility?	476
	Exercises	477
Chapter 13	The Theory and Practice of Regression Analysis	481
13.1	What You Will Learn in This Chapter	481
13.2	Introduction	481
13.3	The Regression Model	483
13.4	Estimation and Inference: The Basics	489
	The Coefficient of Determination and the Degree of Fit	500
13.5	Estimation and Inference: Confidence Intervals and Hypotheses Tests	505
	Confidence Intervals for the Regression Parameters	506
	Predicting the Dependent Variable	508
	Confidence Intervals for the Error Term Standard Deviation	515
	The F Distribution and Measuring the Goodness of Fit	516
	Testing Hypotheses in Regression Equations	520
	Calculations	522
13.6	The "Regression" in Regression Analysis	524
13.7	Summary	527
	Case Study: Was There Age Discrimination in a Public Utility?	528
	Exercises	531
Chapter 14	Comparing Populations through the Analysis of Variance	541
14.1	What You Will Learn in This Chapter	541
14.2	Introduction	541
14.3	An Introduction to One-Way Analysis of Variance	543
	For Multiple Treatments, Which Is Best?	550
	The Link to Regression Analysis	555
14.4	Summary	558
	Case Study: Was There Age Discrimination in a Public Utility?	559
	Exercises	561
Part 6	Retrospective	567
Chapter 15	Retrospective	568
15.1	What You Will Learn in This Chapter	568
15.2	Introduction	568
15.3	A Schematic Review of What You Have Learned	568
15.4	The Role of Statistics in Everyday Life	572
	Case Study: Was There Age Discrimination in a Public Utility?	574
15.5	The Relationship between Science and Statistics	575
15.6	What Might You Learn Next in Statistics?	576
	Exercises	577
Part 7	Appendixes	581
Appendix A	Mathematical Appendix: Review of Concepts and Conventions	582
A.1	Notational Conventions	583
A.2	Indexing	586
A.3	Sigma Notation	587
A.4	Elementary Set Theory	593
A.5	Elements of Calculus	596
	Exercises	616
Appendix B	Directions for Using the Student Version of S-Plus 4.5	620
B.1	Installing, Starting, and Closing S-Plus	620
B.2	Using S-Plus in This Text	621
B.3	General Notes about S-Plus	621
B.4	Data Files	622
B.5	Windows in S-Plus	622
B.6	Menu Bar Commands	624
B.7	Probability and Density Calculations	628
B.8	Statistical Tables	636
	Supplemental Material
Supplement C	Nonparametric Measures
Supplement D	Bayesian Inference
	Index	643