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

	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	Title:    The Elements of Statistics with Applications to Economics and the Social Sciences Manufacturer: National Learning Corporation National Learning Corporation Item Number: 9780534371111 Publication Date: August 2001 Number: 1 Product Description: The Elements of Statistics with Applications to Economics and the Social Sciences Universal Product Code (UPC): 9780534371111 WonderClub Stock Keeping Unit (WSKU): 9780534371111 Rating: 3.5/5 based on 2 Reviews Image Location: https://wonderclub.com/images/covers/11/11/9780534371111.jpg Category: Media >> Books Weight: 0.200 kg (0.44 lbs) Width: 7.600 cm (2.99 inches) Heigh : 9.400 cm (3.70 inches) Depth: 1.200 cm (0.47 inches) Date Added: August 25, 2020, Added By: Ross Date Last Edited: August 25, 2020, Edited By: Ross Price Condition Delivery Seller Action $214.94 Digital Arrives between May 7 - Jun 6 Login to your account for a direct download. Some files are emailed from Dropbox. Continent $0.00 - World $0.00 WonderClub (9296 total ratings) Question Concerning The Elements of Statistics with Applications to Economics and the Social Sciences Ask a Question about The Elements of Statistics with Applications to Economics and the Social Sciences No Question found! Read Reviews for The Elements of Statistics with Applications to Economics and the Social Sciences Write review Mark Maciel reviewed The Elements of Statistics with Applications to Economics and the Social Sciences on April 28, 2015 The Elements of Statistics with Applications to Economics and the Social Sciences The best of this one is his discussion of the 100,000 lives campaign () which I didn't really know about until Jim put me onto this book. A previous book I had read said that hospitals were trying to do something to improve their safety record in line with that of airlines, but the previous book didn't mention this campaign as what was being proposed. I particularly like their slogan, 'Some is not a number, soon is not a time'. The discussion of this campaign alone makes this book worthwhile. I've been becoming interested in the overlapping fields of economics, human decision making, cognitive errors and statistics for quite some time now. One of the first books I read that started me off down this path was Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. There were parts of that book that I found truly fascinating. I really like the idea of people looking for bizarre correlations between sets of data that tell us something new we did not know before. For example, Freakonomics showed that there was a relationship between abortion rates in one decade and dropping crime rates a couple of decades later. I'm a sucker for that sort of thing. The writer of this one has done research with the guy that Freakonomics was written about. And this book fits neatly right into that school of thought. Let me try to sum it up for you. Humans aren't really all that great at working out the best options available to them. We think we learn from experience and that we get better at picking what will be the right outcome when it comes along by our hard won intuitions (see Blink, for example) but when push comes to shove even around things we believe require substantial human discernment (like picking potentially great sports people) are actually generally better understood and the answer better stated by applying a simple mathematical algorithm. The most interesting example given of this is deciding if a vintage of wine will be exceptionally good or not. It has been assumed until recently that this isn't something that can be decided until long after the grapes have been harvested, crushed, fermented and aged in oak. So, when someone said they could tell if a vintage was going to be good, not by tasting the wine, but by plugging in some values into the variables related to the climate while the grapes were growing in some formula - well, things were bound to get ugly. Looking at it, this shouldn't have caused as much outrage as seems to have been the case (at least, as it is reported here). I say that because wine is a natural product. And as a natural product it would seem its quality is ultimately dependant on the quality of its inputs (that is first and foremost, the grapes). If the summer has not been hot enough it is reasonable to think that the grapes may not become ripe enough, the amount of rain will probably also have an effect on the grapes. Given that all of the things a wine maker can control probably already is controlled, the 'acts of God' that cannot really be controlled are obviously going to be the main variables left to decide whether a vintage is to be exceptional or ordinary. Like I said, if it is true (as is reported in this book) that some wine critics were outraged that someone could say they knew years ahead of time if a vintage was going to be exceptional, it seems very silly of them to do so. So, what this book is saying is that the best decision are often made not by 'experts' (like wine experts) but by objective mathematical algorithms in which you plug in your data and wait for the answer to pop out the other side. Clean, clear, untouched by human hands - we are talking of the truth; unalloyed and pure. My problem with this is that too many of these examples seemed to depend entirely on there being a right answer to the question at hand - and, in many of them, I wasn't quite sure I understood what the 'right' answer was or how you could verify the answer the formula threw up was actually the right one. I've just finished reading another book, for example, (Why We Make Mistakes: How We Look Without Seeing, Forget Things in Seconds, and Are All Pretty Sure We Are Way Above Average) in which people invariably preferred the bottle of wine labelled $90 over the one labelled as costing $15, even when they were the exact same wine. Having said that, although I can see the logic of knowing how ripe the grapes are when they are harvested and how much rain fell in the weeks leading up to harvest should make a difference to the quality of the wine once it is made- I also know that I've been fooled before by stories about subjects I know remarkably little about, particularly when those stories seem to make lots of perverse and twisted logical sense. A case in point. Years ago I told my daughter (while she was still young enough to believe I was some kind of God) that it was likely that T-Rex didn't go stomping around ripping other dinosaurs apart, but rather crawled around on his belly and ate mostly carrion. An article I had read somewhere explained this in fascinating detail. You see, T-Rex was cold blooded and big - so he was going to be at a disadvantage in having the energy needed to do the whole Jurassic Park thing. Also, there were those pathetic little forearms (think Charles Atlas just before the sand-in-face moment) which don't seem to be of any use at all if he walked around upright, but might have made much more sense if he crawled about on all fours. Fi had a plastic T-Rex dinosaur and we used to have it crawling around on its belly as it dragged itself to dolls for a feed. As they say in Mythbusters, the guy who wrote the article had done the maths and the myth of the big, fast, angry T-Rex was busted. Except, of course, that was before they worked out dinosaurs were less like lizards and more like birds. As a lizard, T-Rex wouldn't have had the energy to run and fight and kill, but as a bird none of that was a problem. My point is that the presentation of this stuff as simply objective truth is a bit dangerous - not least because we humans (particularly we boy humans) just love numbers, especially either big numbers or numbers with decimal points. Tell a boy it happened 165 million years ago or it will cost 15 trillion dollars to fix or that you are 43.7 per cent sure of something and, in what is the closest sensation to post-coital bliss, he will become so excited he won't even know you made this stuff up on the spot. The most obvious reason why creationism is bound to fail is that any idea that stops men (well, boys) saying '9.8 billion years ago' and replaces it with 'around10,000 years ago and that's tops' really doesn't stand a sporting chance. Sorry, off topic. There are very interesting discussions in this book on data mining, data warehousing and corporate manipulation of customers that, even though the author did not feel were all that bad, ought to be read so that you have an idea of what is being done to squeeze money out of you. The discussion on how casinos work out your 'pain point' so they can stop you from losing beyond that pain point and thus get you to come back again later is disgusting. The most useful message to come from this book is that we should all be afraid when a corporation is being nice to us - it almost invariably means we are paying too much for the service they are providing us. The most disgusting lesson presented here is that if you are black or female in America and you need a new car then you really ought to think about becoming white and male. And don't be fooled if the dealer gets you someone to bargain with you of your own sex or race. Become white and become male - even if you have to pay someone both white and male a couple of hundred dollars to be you - it might save you thousands. I worried while reading this book that a lot of the questions the author felt were settled by crunching numbers were still somewhat open for debate - I struggle with the idea that direct instruction (where teachers spend their days instructing kids literally by reading from a script with no discretion at all) can be the great boon to education that it is presented in this book to be. Figures were given that made direct instruction sound like the only sensible way to teach kids anything - however, even though I'm not a teacher, I still have strong doubts that this kind of rote learning (measurable as it is) will produce kids able to respond to the challenges of the modern world. This book suffers from the same self-congratulation Freakonomics struggled with. I have a fairly natural aversion to people who pat themselves on the back quite so fulsomely. All the same, there were a number of things I learnt from this book and it was worth reading for its interestingly simply method of explaining regression and standard deviations. Login   |   Complaints   |   Blog   |   Games   |   Digital Media   |   Souls   |   Obituary   |   Contact Us   |   FAQ CAN'T FIND WHAT YOU'RE LOOKING FOR? CLICK HERE!!! 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