Explain The Significance Of The Following Quotes

Now it is very striking, and well worth investigating, that such trifling, nay, apparently childish, means as metre and rhyme produce so powerful an effect. I explain it to myself in the following manner: That which is given directly to the sense of hearing, thus the mere sound of the words, receives from rhythm and rhyme a certain completeness and significance in itself for it thereby becomes a kind of music; therefore it seems now to exist for its own sake, and no longer as a mere means, mere signs of something signified, the sense of the words. To please the ear with its sound seems to be its whole end, and therefore with this everything seems to be attained and all claims satisfied. But that it further contains a meaning, expresses a thought, presents itself now as an unexpected addition, like words to music - as an unexpected present which agreeably surprises us - and therefore, since we made no demands of this kind, very easily satisfies us; and if indeed this thought is such that, in itself, thus said in prose, it would be significant, then we are enchanted.

There must be, and, if we are honest, there always will be at least one situation in our lives that we cannot fix, control, explain, change, or even understand. For Jesus and for his followers, the crucifixion became the dramatic symbol of that necessary and absurd stumbling stone. Yet we have no positive theology of such necessary suffering, for the most part. Many Christians even made the cross into a mechanical “substitutionary atonement theory” to fit into their quid pro quo worldview, instead of suffering its inherent tragedy, as Jesus did himself. They still want some kind of order and reason, instead of cosmic significance and soulful seeing.1

It wasn’t until nearly 400 years later [since capitalist privatizations at home in Britain, i.e. the Enclosures starting in 1500s] that life expectancies in Britain finally began to rise. […] It happened slightly later in the rest of Europe, while in the colonised world longevity didn’t begin to improve until the early 1900s [decolonization]. So if [capitalist economic] growth itself does not have an automatic relationship with life expectancy and human welfare, what could possibly explain this trend? Historians today point out that it began with a startlingly simple intervention […]: [public] sanitation. In the middle of the 1800s, public health researchers had discovered that health outcomes could be improved by introducing simple sanitation measures, such as separating sewage from drinking water. All it required was a bit of public plumbing. But public plumbing requires public works, and public money. You have to appropriate private land for things like public water pumps and public baths. And you have to be able to dig on private property in order to connect tenements and factories to the system. This is where the problems began. For decades, progress towards the goal of public sanitation was opposed, not enabled, by the capitalist class. Libertarian-minded landowners refused to allow officials to use their property [note: the Enclosures required state violence to privatize land], and refused to pay the taxes required to get it done. The resistance of these elites was broken only once commoners won the right to vote and workers organised into unions. Over the following decades these movements, which in Britain began with the Chartists and the Municipal Socialists, leveraged the state to intervene against the capitalist class. They fought for a new vision: that cities should be managed for the good of everyone, not just for the few. These movements delivered not only public sanitation systems but also, in the years that followed, public healthcare, vaccination coverage, public education, public housing, better wages and safer working conditions. According to research by the historian Simon Szreter, access to these public goods – which were, in a way, a new kind of commons – had a significant positive impact on human health, and spurred soaring life expectancy through the twentieth century.

There was a time when I thought that my life's significant work would be to write a history of the Six Duchies. I made a start on it any number of times, but always seemed to slide sideways from that grand tale into a recounting of the days and details of my own small life. The more I studied the accounts of others, both written and told, the more it seemed to me that we attempt such histories not to preserve knowledge, but to fix the past in a settled way. Like a flower pressed flat and dried, we try to hold it still and say, this is exactly how it was the day I first saw it. But like the flower, the past cannot be trapped that way. It loses its fragrance and its vitality, its fragility becomes brittleness and its colors fade. And when next you look on the flower, you know that it is not at all what you sought to capture, that that moment has fled forever. I wrote my history and my observations. I captured my thoughts and ideas and memories in words on vellum and paper. So much I stored, and thought it was mine. I believed that by fixing it down in words, I could force sense on all that had happened, that effect would follow cause, and the reason for each event come clear to me. Perhaps I sought to justify myself, not just all I had done, but who I had become. For years, I wrote faithfully nearly every evening, carefully explaining my world and my life to myself. I put my scrolls on a shelf, trusting that I had captured the meaning of my days. But then I returned one day, to find all my careful scribing gone to fragments of vellum lying in a trampled yard with wet set, snow blowing over them. I sat my horse, looking down on them, and knew that, as it always would, the past had broken free of my effort to define and understand it. History is no more fixed and dead than the future. The past is no further away than the last breath you took.

Another aspect of this – one that he makes into an extended, if slightly ghoulish, case study – was to be found in the funerals of ‘distinguished men’. Again, Polybius must have witnessed enough of these to draw out their deeper significance. The body, he explains, was carried into the Forum and placed on the rostra, normally propped up somehow in an upright position, so it was visible to a large audience. In the procession that followed, family members wore masks made in the likeness of the dead man’s ancestors and dressed in the costume appropriate to the offices each had held (purple-bordered togas and so on), as if they were all present ‘living and breathing’. The funeral address, delivered by a family member, started with the achievements of the corpse on the rostra but then went through the careers of all the other characters, who by this time were sitting on ivory, or at least ivory-veneered, chairs lined up next to the dead man. ‘The most important upshot of this,’ Polybius concludes, ‘is that the younger generation is inspired to endure all suffering for the common good, in the hope of winning the glory that belongs to the brave.

He does not care for me. He brought me to the village to feed his child." "He gave you Red Star." Jesse denied its significance. "That was only so that I would not shame him." "He brought many skins for a new tepee. He brought you elk skins for a new dress." Jesse explained. "We needed those things because of the fire.All of the people needed new tepees, new clothing." "He sits with you every evening outside the tepee." "That is so I can read from the Book." Prairie Flower grew impatient. "Walks the Fire! I tell you truth.Rides the Wind wishes you to be his wife.You know nothing of Lakota ways.I will tell you!" Jesse started to protest, but Prairie Flower interrupted. "No! You listen! When a man wishes to show he wants a woman, he dresses in his finest clothing and comes to her outside her tepee.They sit and talk.He gives gifts to her parents. Not every custom is followed, because you are not a young Lakota woman. But I tell you, Rides the Wind cares for you. After the fire, when Medicine Hawk came-when you were as one dying-you did not see him. I saw him. Rides the Wind did not eat. He did not sleep.He thought only of Walks the Fire.He hunted healing herbs.He hunted the elk for your dress.He took Two Mothers to Yellow Bird's tepee so that his cries would not disturb your rest.He trusted no one but Old One, and himself, and me to care for you.

Kant is sometimes considered to be an advocate of reason. Kant was in favor of science, it is argued. He emphasized the importance of rational consistency in ethics. He posited regulative principles of reason to guide our thinking, even our thinking about religion. And he resisted the ravings of Johann Hamann and the relativism of Johann Herder. Thus, the argument runs, Kant should be placed in the pantheon of Enlightenment greats. That is a mistake. The fundamental question of reason is its relationship to reality. Is reason capable of knowing reality - or is it not? Is our rational faculty a cognitive function, taking its material form reality, understanding the significance of that material, and using that understanding to guide our actions in reality - or is it not? This is the question that divides philosophers into pro- and anti-reason camps, this is the question that divides the rational gnostics and the skeptics, and this was Kant’s question in his Critique of Pure Reason. Kant was crystal clear about his answer. Reality - real, noumenal reality - is forever closed off to reason, and reason is limited to awareness and understanding of its own subjective products… Kant was the decisive break with the Enlightenment and the first major step toward postmodernism. Contrary to the Enlightenment account of reason, Kant held that the mind is not a response mechanism but a constitute mechanism. He held that the mind - and not reality - sets the terms for knowledge. And he held that reality conforms to reason, not vice versa. In the history of philosphy, Kant marks a fundamental shift from objectivity as the standard to subjectivity as the standard. What a minute, a defender of Kant may reply. Kant was hardly opposed to reason. After all, he favored rational consistency and he believed in universal principles. So what is anti-reason about it? The answer is that more fundamental to reason than consistency and universality is a connection to reality. Any thinker who concludes that in principle reason cannot know reality is not fundamentally an advocate of reason… Suppose a thinker argued the following: “I am an advocate of freedom for women. Options and the power to choose among them are crucial to our human dignity. And I am wholeheartedly an advocate of women’s human dignity. But we must understand that a scope of a women’s choice is confined to the kitchen. Beyond the kitchen’s door she must not attempt to exercise choice. Within the kitchen, however, she has a whole feast of choices[…]”. No one would mistake such a thinker for an advocate of women’s freedom. Anyone would point out that there is a whole world beyond the kitchen and that freedom is essentially about exercising choice about defining and creating one’s place in the world as a whole. The key point about Kant, to draw the analogy crudely, is that he prohibits knowledge of anything outside our skulls. The gives reasons lots to do withing the skull, and he does advocate a well-organized and tidy mind, but this hardly makes him a champion of reason… Kant did not take all of the steps down to postmodernism, but he did take the decisive one. Of the five major features of Enlightenment reason - objectivity, competence, autonomy, universality, and being an individual faculty - Kant rejected objectivity.

Only what is that thing? Why am I made the way I am? Why do I care about all the wrong things, and nothing at all for the right ones? Or, to tip it another way: how can I see so clearly that everything I love or care about is illusion, and yet—for me, anyway—all that’s worth living for lies in that charm? A great sorrow, and one that I am only beginning to understand: we don’t get to choose our own hearts. We can’t make ourselves want what’s good for us or what’s good for other people. We don’t get to choose the people we are. Because—isn’t it drilled into us constantly, from childhood on, an unquestioned platitude in the culture—? From William Blake to Lady Gaga, from Rousseau to Rumi to Tosca to Mister Rogers, it’s a curiously uniform message, accepted from high to low: when in doubt, what to do? How do we know what’s right for us? Every shrink, every career counselor, every Disney princess knows the answer: “Be yourself.” “Follow your heart.” Only here’s what I really, really want someone to explain to me. What if one happens to be possessed of a heart that can’t be trusted—? What if the heart, for its own unfathomable reasons, leads one willfully and in a cloud of unspeakable radiance away from health, domesticity, civic responsibility and strong social connections and all the blandly-held common virtues and instead straight towards a beautiful flare of ruin, self-immolation, disaster? Is Kitsey right? If your deepest self is singing and coaxing you straight toward the bonfire, is it better to turn away? Stop your ears with wax? Ignore all the perverse glory your heart is screaming at you? Set yourself on the course that will lead you dutifully towards the norm, reasonable hours and regular medical check-ups, stable relationships and steady career advancement, the New York Times and brunch on Sunday, all with the promise of being somehow a better person? Or—like Boris—is it better to throw yourself head first and laughing into the holy rage calling your name? It’s not about outward appearances but inward significance. A grandeur in the world, but not of the world, a grandeur that the world doesn’t understand. That first glimpse of pure otherness, in whose presence you bloom out and out and out. A self one does not want. A heart one cannot help.

During the chaos of the Hundred Years’ War, when northern France was decimated by English troops and the French monarchy was in retreat, a young girl from Orléans claimed to have divine instructions to lead the French army to victory. With nothing to lose, Charles VII allowed her to command some of his troops. To everyone’s shock and wonder, she scored a series of triumphs over the English. News rapidly spread about this remarkable young girl. With each victory, her reputation began to grow, until she became a folk heroine, rallying the French around her. French troops, once on the verge of total collapse, scored decisive victories that paved the way for the coronation of the new king. However, she was betrayed and captured by the English. They realized what a threat she posed to them, since she was a potent symbol for the French and claimed guidance directly from God Himself, so they subjected her to a show trial. After an elaborate interrogation, she was found guilty of heresy and burned at the stake at the age of nineteen in 1431. In the centuries that followed, hundreds of attempts have been made to understand this remarkable teenager. Was she a prophet, a saint, or a madwoman? More recently, scientists have tried to use modern psychiatry and neuroscience to explain the lives of historical figures such as Joan of Arc. Few question her sincerity about claims of divine inspiration. But many scientists have written that she might have suffered from schizophrenia, since she heard voices. Others have disputed this fact, since the surviving records of her trial reveal a person of rational thought and speech. The English laid several theological traps for her. They asked, for example, if she was in God’s grace. If she answered yes, then she would be a heretic, since no one can know for certain if they are in God’s grace. If she said no, then she was confessing her guilt, and that she was a fraud. Either way, she would lose. In a response that stunned the audience, she answered, “If I am not, may God put me there; and if I am, may God so keep me.” The court notary, in the records, wrote, “Those who were interrogating her were stupefied.” In fact, the transcripts of her interrogation are so remarkable that George Bernard Shaw put literal translations of the court record in his play Saint Joan. More recently, another theory has emerged about this exceptional woman: perhaps she actually suffered from temporal lobe epilepsy. People who have this condition sometimes experience seizures, but some of them also experience a curious side effect that may shed some light on the structure of human beliefs. These patients suffer from “hyperreligiosity,” and can’t help thinking that there is a spirit or presence behind everything. Random events are never random, but have some deep religious significance. Some psychologists have speculated that a number of history’s prophets suffered from these temporal lobe epileptic lesions, since they were convinced they talked to God.

Page 141: Group Polarization Patterns Political anger and demands for privileges are, of course, not limited to the less privileged. Indeed, even when demands are made in the name of less privileged racial or ethnic groups, often it is the more privileged members of such groups who make the demands and who benefit from policies designed to meet such demands. These demands may erupt suddenly in the wake of the creation (or sharp enlargement) of a newly educated class which sees its path to coveted middle-class professions blocked by competition of other groups--as in India, French Canada, or Lithuania, for example. * * * A rapid expansion of education is thus a factor in producing inter-group conflict, especially where the education is of a kind which produces diplomas rather than skills that have significant economic value in the marketplace. Education of a sort useful only for being a clerk, bureaucrat, school teacher--jobs whose numbers are relatively fixed in the short run and politically determined in the long run--tend to increase politicized inter-group strife. Yet newly emerging groups, whether in their own countries or abroad, tend to specialize precisely in such undemanding fields. Malay students, for example, have tended to specialize in Malay studies and Islamic studies, which provide them with no skills with which compete with the Chinese in the marketplace, either as businessmen, independent professionals, or technicians. Blacks and Hispanics in the United States follow a very similar pattern of specializing disproportionately in easier fields which offer less in the way of marketable skills. Such groups then have little choice but to turn to the government, not just for jobs but also for group preferences to be imposed in the market place, and for symbolic recognition in various forms. *** While economic interests are sometimes significant in explaining political decisions, they are by no means universally valid explanations. Educated elites from less advanced groups may have ample economic incentives to promote polarization and preferential treatment policies, but the real question is why the uneducated masses from such groups give them the political support without which they would be impotent. Indeed, it is often the less educated masses who unleash the mob violence from which their elite compatriots ultimately benefit--as in Malaysia, Sri Lanka, or parts of India, Africa, or the United States, where such violence has led to group preference policies in employment, educational institutions, and elsewhere. The common denominator in these highly disparate societies seems to be not only resentment of other groups' success but also fear of an inability to compete with them, combined with a painful embarrassment at being so visibly "under-represented"--or missing entirely—in prestigious occupations and institutions. To remedy this within apolitically relevant time horizon requires not simply increased opportunities but earmarked benefits directly given on a racial or ethnic basis.

In many fields—literature, music, architecture—the label ‘Modern’ stretches back to the early 20th century. Philosophy is odd in starting its Modern period almost 400 years earlier. This oddity is explained in large measure by a radical 16th century shift in our understanding of nature, a shift that also transformed our understanding of knowledge itself. On our Modern side of this line, thinkers as far back as Galileo Galilei (1564–1642) are engaged in research projects recognizably similar to our own. If we look back to the Pre-Modern era, we see something alien: this era features very different ways of thinking about how nature worked, and how it could be known. To sample the strange flavour of pre-Modern thinking, try the following passage from the Renaissance thinker Paracelsus (1493–1541): The whole world surrounds man as a circle surrounds one point. From this it follows that all things are related to this one point, no differently from an apple seed which is surrounded and preserved by the fruit … Everything that astronomical theory has profoundly fathomed by studying the planetary aspects and the stars … can also be applied to the firmament of the body. Thinkers in this tradition took the universe to revolve around humanity, and sought to gain knowledge of nature by finding parallels between us and the heavens, seeing reality as a symbolic work of art composed with us in mind (see Figure 3). By the 16th century, the idea that everything revolved around and reflected humanity was in danger, threatened by a number of unsettling discoveries, not least the proposal, advanced by Nicolaus Copernicus (1473–1543), that the earth was not actually at the centre of the universe. The old tradition struggled against the rise of the new. Faced with the news that Galileo’s telescopes had detected moons orbiting Jupiter, the traditionally minded scholar Francesco Sizzi argued that such observations were obviously mistaken. According to Sizzi, there could not possibly be more than seven ‘roving planets’ (or heavenly bodies other than the stars), given that there are seven holes in an animal’s head (two eyes, two ears, two nostrils and a mouth), seven metals, and seven days in a week. Sizzi didn’t win that battle. It’s not just that we agree with Galileo that there are more than seven things moving around in the solar system. More fundamentally, we have a different way of thinking about nature and knowledge. We no longer expect there to be any special human significance to natural facts (‘Why seven planets as opposed to eight or 15?’) and we think knowledge will be gained by systematic and open-minded observations of nature rather than the sorts of analogies and patterns to which Sizzi appeals. However, the transition into the Modern era was not an easy one. The pattern-oriented ways of thinking characteristic of pre-Modern thought naturally appeal to meaning-hungry creatures like us. These ways of thinking are found in a great variety of cultures: in classical Chinese thought, for example, the five traditional elements (wood, water, fire, earth, and metal) are matched up with the five senses in a similar correspondence between the inner and the outer. As a further attraction, pre-Modern views often fit more smoothly with our everyday sense experience: naively, the earth looks to be stable and fixed while the sun moves across the sky, and it takes some serious discipline to convince oneself that the mathematically more simple models (like the sun-centred model of the solar system) are right.

There happens to be a coffee bar in the lobby of the hotel. One afternoon while on a business trip in Las Vegas, I went to buy myself a cup of coffee. The barista working that day was a young man named Noah. Noah was funny and engaging. It was because of Noah that I enjoyed buying that cup of coffee more than I generally enjoy buying a cup of coffee. After standing and chatting for a while, I finally asked him, “Do you like your job?” Without skipping a beat Noah immediately replied, “I love my job!” Now, for someone in my line of business, that’s a significant response. He didn’t say, “I like my job,” he said, “I love my job.” That’s a big difference. “Like” is rational. We like the people we work with. We like the challenge. We like the work. But “love,” love is emotional. Love is something harder to quantify. It’s like asking someone “Do you love your spouse,” and they respond, “I like my spouse a lot.” It’s a very different answer. You get my point, love is a higher standard. So when Noah said, “I love my job,” I perked up. From that one response, I knew Noah felt an emotional connection to the Four Seasons that was bigger than the money he made and the job he performs. Immediately, I asked Noah a follow-up question. “Tell me specifically what the Four Seasons is doing that you would say to me that you love your job.” Again without skipping a beat, Noah replied, “Throughout the day, managers will walk past me and ask me how I’m doing, ask me if there is anything I need, anything they can do to help. Not just my manager … any manager. I also work for [another hotel],” he continued. He went on to explain that at his other job the managers walk past and try to catch people doing things wrong. At the other hotel, Noah lamented, “I keep my head below the radar. I just want to get through the day and get my paycheck. Only at the Four Seasons,” Noah said, “do I feel I can be myself.” Noah gives his best when he’s at the Four Seasons. Which is what every leader wants from their people. So it makes sense why so many leaders, even some of the best-intentioned ones, often ask, “How do I get the most out of my people?” This is a flawed question, however. It’s not a question about how to help our people grow stronger, it’s about extracting more output from them. People are not like wet towels to be wrung out. They are not objects from which we can squeeze every last drop of performance. The answers to such a question might yield more output for a time, but it often comes at a cost of our people and to the culture in the longer term. Such an approach will never generate the feelings of love and commitment that Noah has for the Four Seasons. A better question to ask is, “How do I create an environment in which my people can work to their natural best?

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different perspective. Matthew looks at Him through the perspective of His kingdom; Mark through the perspective of His servanthood; Luke through the perspective of His humanness; and John through the perspective of His deity. The Book of Acts chronicles the impact of the life, death, and Resurrection of Jesus Christ, the Lord and Savior—from His Ascension, the consequent coming of the Holy Spirit, and the birth of the church, through the early years of gospel preaching by the apostles and their associates. Acts records the establishment of the church in Judea, Samaria, and into the Roman Empire. The twenty-one epistles were written to churches and individuals to explain the significance of the person and work of Jesus Christ, with its implications for life and witness until He returns. The NT closes with Revelation, which starts by picturing the current church age, and culminates with Christ’s return to establish His earthly kingdom, bringing judgment on the ungodly and glory and blessing for believers. Following the millennial reign of the Lord Savior will be the last judgment, leading to the eternal state. All believers of all history enter the ultimate eternal glory prepared for them, and all the

Die-Face Analysis In the 1930s, J. B. Rhine and his colleagues recognized and took into account the possibility that some dice studies may have been flawed because the probabilities of die faces are not equal. With some dice, it is slightly more likely that one will roll a 6 face than a 1 face because the die faces are marked by scooping out bits of material. The 6 face, for example, has six scoops removed from the surface of that side of the die, so it has slightly less mass than the other die faces. On any random toss, that tiny difference in mass will make the 6 slightly more likely to land face up, followed in decreasing probability by the 5, 4, 3, 2, and 1 faces. Thus, an experiment that relied exclusively upon the 6 face as the target may have been flawed because, unless there were also control tosses with no mental intention applied, we could not tell whether above-chance results were due to a mind-matter interaction or to the slightly higher probability of rolling a 6. To see whether this bias was present in these dice studies, we sifted out all reports for which the published data allowed us to calculate the effective hit rate separately for each of the six die faces used under experimental and control conditions. In fact, the suspected biases were found, as shown in figure 8.3. The hit rates for both experimental and control tosses tended to increase from die faces 1 to 6. However, most of the experimental hit rates were also larger than the corresponding control hit rates, suggested some thing interesting beyond the artifacts caused by die-face biases. For example, for die face 6 the experimental condition was significantly larger than the control with odds against chance of five thousand to one. Figure 8.3. Relationship between die face and hit rates for experimental and control conditions. The error bars are 65 percent confidence intervals. Because of the evidence that the die faces were slightly biased, we examined a subset of studies that controlled for these dice biases—studies using design protocols where die faces were equally distributed among the six targets. We referred to such studies as the “balanced-protocol subset.” Sixty-nine experiments met the balanced-protocol criteria. Our examination of those experiments resulted in three notable points: there was still highly significant evidence for mind-matter interaction, with odds against chance of greater than a trillion to one; the effects were constant across different measures of experimental quality; and the selective-reporting “file drawer” required a twenty-to-one ratio of unretrieved, nonsignificant studies for each observed study. Thus chance, quality, and selective reporting could not explain away the results. Dice Conclusions Our meta-analysis findings led us to conclude that a genuine mind-matter interaction did exist with experiments testing tossed dice. The effect had been successfully replicated in more than a hundred experiments by more than fifty investigators for more than a half-century.

To make this point, I often do the same coin flipping exercise that I explained during the probability discussion. In a class of forty students or so, I’ll have each student flip a coin. Any student who flips tails is eliminated; the rest flip again. In the second round, those who flip tails are once again eliminated. I continue the rounds of flipping until one student has flipped five or six heads in a row. You may recall some of the silly follow-up questions: “What’s your secret? Is it in the wrist? Can you teach us to flip heads all the time? Maybe it’s that Harvard sweatshirt you’re wearing.” Obviously the string of heads is just luck; the students have all watched it happen. However, that is not necessarily how the result could or would be interpreted in a scientific context. The probability of flipping five heads in a row is 1/32, or .03. This is comfortably below the .05 threshold we typically use to reject a null hypothesis. Our null hypothesis in this case is that the student has no special talent for flipping heads; the lucky string of heads (which is bound to happen for at least one student when I start with a large group) allows us to reject the null hypothesis and adopt the alternative hypothesis: This student has a special ability to flip heads. After he has achieved this impressive feat, we can study him for clues about his flipping success—his flipping form, his athletic training, his extraordinary concentration while the coin is in the air, and so on. And it is all nonsense. This phenomenon can plague even legitimate research. The accepted convention is to reject a null hypothesis when we observe something that would happen by chance only 1 in 20 times or less if the null hypothesis were true. Of course, if we conduct 20 studies, or if we include 20 junk variables in a single regression equation, then on average we will get 1 bogus statistically significant finding.

the Mayo Clinic explains, “Studies show that a lifelong diet rich in soy foods reduces the risk of breast cancer in women . . . Soy contains protein, isoflavones and fiber, all of which provide health benefits.”17 Even women who have breast cancer can benefit from eating more soy. After following tens of thousands of breast cancer patients, a study in the journal Cancer found that women with breast cancer who ate the most soy lived significantly longer.18 That’s great news for soul-food lovers; some of the best southern-inspired plant-based recipes feature delicious soy foods like tofu and edamame.

The contrast between the subject and the object in the theory of knowledge corresponds ontologically to the contrast between form and matter. It is no more than a translation of this distinction from the theory of experience to metaphysics. Matter, which in itself is absolutely unindividualised and so can assume any form, of itself has no definite and lasting qualities, and has as little essence as mere perception, the matter of experience, has in itself any existence. If the Platonic conception is followed out, it will be apparent that that great thinker asserted to be nothing what the ordinary Philistine regards as the highest form of reality. According to Plato, the negation of existence is no other than matter. Form is the only real existence. Aristotle carried the Platonic conception into the regions of biology. For Plato form is the parent and creator of all reality. For Aristotle, in the sexual process the male principle is the active, formative agent, the female principle the passive matter on which the form is impressed. In my view, the significance of woman in humanity is explained by the Platonic and Aristotelian conception. Woman is the material on which man acts. Man as the microcosm is compounded of the lower and higher life.

It follows from these results that wealthy people who are exposed to the suffering of others should exhibit less compassion than their poorer counterparts do, and this has been confirmed in the lab. When we experience compassion, though nobody knows why, our hearts slow down. Piff’s colleagues Michael Kraus and Jennifer Stellar hooked volunteers up to EKG devices and showed them two short videos, a “neutral” video of a woman explaining how to construct a patio wall and a “compassion” video of cancer-stricken children undergoing chemotherapy. Relative to the wealthier subjects, the poor ones not only reported higher levels of compassion for the children, they had a significantly larger slowdown in heart rate between the neutral video and the compassion video than their wealthy peers.

Given the obvious “will to power” (as Friedrich Nietzsche called it) of the human race, the enormous energy put into its expression, the early emergence of hierarchies among children, and the childlike devastation of grown men who tumble from the top, I’m puzzled by the taboo with which our society surrounds this issue. Most psychology textbooks do not even mention power and dominance, except in relation to abusive relationships. Everyone seems in denial. In one study on the power motive, corporate managers were asked about their relationship with power. They did acknowledge the existence of a lust for power, but never applied it to themselves. They rather enjoyed responsibility, prestige, and authority. The power grabbers were other men. Political candidates are equally reluctant. They sell themselves as public servants, only in it to fix the economy or improve education. Have you ever heard a candidate admit he wants power? Obviously, the word “servant” is doublespeak: does anyone believe that it’s only for our sake that they join the mudslinging of modern democracy? Do the candidates themselves believe this? What an unusual sacrifice that would be. It’s refreshing to work with chimpanzees: they are the honest politicians we all long for. When political philosopher Thomas Hobbes postulated an insuppressible power drive, he was right on target for both humans and apes. Observing how blatantly chimpanzees jockey for position, one will look in vain for ulterior motives and expedient promises. I was not prepared for this when, as a young student, I began to follow the dramas among the Arnhem chimpanzees from an observation window overlooking their island. In those days, students were supposed to be antiestablishment, and my shoulder-long hair proved it. We considered power evil and ambition ridiculous. Yet my observations of the apes forced me to open my mind to seeing power relations not as something bad but as something ingrained. Perhaps inequality was not to be dismissed as simply the product of capitalism. It seemed to go deeper than that. Nowadays, this may seem banal, but in the 1970s human behavior was seen as totally flexible: not natural but cultural. If we really wanted to, people believed, we could rid ourselves of archaic tendencies like sexual jealousy, gender roles, material ownership, and, yes, the desire to dominate. Unaware of this revolutionary call, my chimpanzees demonstrated the same archaic tendencies, but without a trace of cognitive dissonance. They were jealous, sexist, and possessive, plain and simple. I didn’t know then that I’d be working with them for the rest of my life or that I would never again have the luxury of sitting on a wooden stool and watching them for thousands of hours. It was the most revelatory time of my life. I became so engrossed that I began trying to imagine what made my apes decide on this or that action. I started dreaming of them at night and, most significant, I started seeing the people around me in a different light.

The fact that earth is not our ultimate home explains why, as followers of Jesus, we experience difficulty, sorrow, and rejection in this world.11 It also explains why some of God’s promises seem unfulfilled, some prayers seem unanswered, and some circumstances seem unfair. This is not the end of the story. In order to keep us from becoming too attached to earth, God allows us to feel a significant amount of discontent and dissatisfaction in life — longings that will never be fulfilled on this side of eternity. We’re not completely happy here because we’re not supposed to be! Earth is not our final home; we were created for something much better.

After all, we also suffer from a range of biases that inhibit our ability to accurately assess strengths and weaknesses (which explains why 90 percent of drivers self-report being above average). When we ask most companies what allows them to win in business, the most common response, after a spell of awkward silence, is, “Our brands.” Then we innocently follow up by asking the last time an industry upstart called, looking to license a corporate brand (typical answer: “Never”), or we ask them their estimated net promoter score (typical answer: either “What’s that?” or a negative number). The next contender typically is scale. But even though scale can create significant advantage, it also can carry downsides, such as molasses-like decision-making processes or inflexibility.

I knew before today that I would have to leave,” she said, keeping her back to Zachary. “Now, after this, I certainly can't live beneath the same roof with you.” “I don't want you to leave.” “My feelings for you don't change what I must do. I've already explained why.” He was silent for a full minute, grasping the full significance of her words. “You're still planning to marry Ravenhill,” he said tonelessly. “Even now.” “No, it's not that.” Holly felt very cold, all the pulsing warmth of their encounter finally draining away. She tried to examine her choices, but all of them left her feeling empty and strangely fearful. It was all too natural to retreat back into the habits of a lifetime, to follow the paths that had been chosen for her long ago, first by her father and then by George. “I don't know what will happen with Ravenill. I don't even know if he'll still have me.” “Oh, he'll have you.” Zachary spun her around to face him. He was huge and dark, staring at her with a sort of resigned fury. “I've had to fight for everything I've ever gotten. But I won't fight for you. You'll come to me because you want me. I'll be damned if I'll bully or beg you to have me. I suppose in the ton's view, a Ravenhill is worth about a hundred Bronsons. No one will blame you for marrying him, especially when it comes out that George wanted the match. And you might even be happy for a while. But someday you'll realize it was a mistake, when it's too late for either of us to do a damned thing about it.” Holly turned white, but managed to reply calmly. “Our agreement… I'll return the money…” “Keep the money for Rose. There's no reason for her trust to be cut in half simply because her mother is a coward.” She lowered her watery gaze to the level of his third shirt button. “You're being cruel now,” she whispered. “I think I could be a gentleman about almost anything, except for losing you. Don't expect me to take it with good grace, Holly.

learning—we have learned how to increase productivity, the outputs that can be produced with any inputs. There are two aspects of learning that we can distinguish: an improvement in best practices, reflected in increases in productivity of firms that marshal all available knowledge and technology, and improvements in the productivity of firms as they catch up to best practices. In fact, the distinction may be somewhat artificial; there may be no firm that has employed best practices in every aspect of its activities. One firm may be catching up with another in some dimension, but the second firm may be catching up with the first in others. In developing countries, almost all firms may be catching up with global best practices; but the real difference between developing and developed countries is the larger fraction of firms that are significantly below global best practices and the larger gap between their productivity and that of the best-performing firms. While we are concerned in this book with both aspects of learning, it is especially the learning associated with catching up that we believe has been given short shrift in the economics literature, and which is central to improvements in standards of living, especially in developing countries. But as we noted in chapter 1, the two are closely related; because of the improvements in best practices by the most innovative firms, most other firms are always engaged in a process of catching up. While the evidence of Solow and the work that followed demonstrated (what to many seems obvious) the importance of learning for increases in standards of living, to further explicate the role of learning, the first three sections of this chapter marshal other macro- and microeconomic evidence. In particular, we stress the pervasive gap between best practices and the productivity of most firms. We argue that this gap is far more important than the traditional allocative inefficiencies upon which most of economics has focused and is related to learning—or more accurately, the lack of learning. The final section provides a theoretical context within which to think about the sources of sustained increases in standards of living, employing the familiar distinction of movements of the production possibilities curve and movements toward the production possibilities curve. Using this framework, we explain why it is that we ascribe such importance to learning. Macroeconomic Perspectives There are several empirical arguments that can be brought to bear to support our conclusion concerning the importance of learning. The first is a simple argument: In theory, leading-edge technology is globally available. Thus, with sufficient capital and trained labor (or sufficient mobility for capital and trained labor), all countries should enjoy comparable standards of living. The only difference would be the rents associated with ownership of intellectual property rights and factor supplies. Yet there is an enormous divergence in economic performance and standards of living across national economies, far greater than can be explained by differences in factor supplies.1 And this includes many low-performing economies with high levels of capital intensity (especially among formerly socialist economies) and highly trained labor forces. Table 2.1 presents a comparison of formerly socialist countries with similar nonsocialist economies in the immediate aftermath of the collapse of the state-controlled model of economic activity. TABLE 2.1 Quality of Life Comparisons, 1992–1994 (U.S. $) Source: Greenwald and Khan (2009), p. 30. In most of these cases, at the time communism was imposed after World War II, the subsequently socialist economies enjoyed higher levels of economic development than

Nothing could be further from the truth than the myth that if we lower our cholesterol levels we might have a greater chance of living longer and healthier lives. In a recent report appearing in the prestigious medical journal the _Lancet_, researchers from the Netherlands studied 724 elderly individuals whose average age was eighty-nine years and followed them for ten years. What they found was truly extraordinary. During the study, 642 participants died. Each thirty-nine-point increase in total cholesterol corresponded to a 15 percent decrease in mortality risk. In the study, there was absolutely no difference in the risk of dying from coronary artery disease between the high- versus low-cholesterol groups, which is incredible when you consider the number of elderly folks who are taking powerful cholesterol-lowering drugs. Other common causes of death in the elderly were found to be dramatically associated with lower cholesterol. The authors reported: 'Mortality from cancer and infection was significantly lower among the participants in the highest total cholesterol category than in the other categories, which largely explains the lower all-cause mortality in this category.' In other words, people with the highest total cholesterol were less likely to die from cancer and infections -- common fatal illnesses in older folks -- than those with the lowest cholesterol levels. In fact, when you compare the lowest- and highest-cholesterol groups, the risk of dying during the study was reduced by a breathtaking 48 percent in those who had the highest cholesterol. High cholesterol can extend longevity. ~ David Perlmutter, M.D., _Grain Brain_

Germany exported its problem of a lack of competitiveness to other member states. Since 1999 this country has followed a tight policy of wage moderation while the rest of the euro zone maintained more or less constant wage increases of around 3 per cent per year. Thus, each year Germany tended to improve its competitive position vis-à-vis the rest of the euro zone – a trend partly explained by the fact that the power of German labour unions has declined significantly, more so than in other euro zone countries. Other countries with particularly close economic ties to Germany are forced to intensify their policies of wage moderation, inducing the leading country again to restrict wage increases. A vicious circle may result when everybody attempts to improve its competitiveness at the expense of the others. As in the case of the so-called race to the bottom in environmental policy, the final outcome is that these countries will not have improved their relative position, but will have adopted wage policies that do not correspond to the preferences of their citizens. At the same time, the distance between the leading group and the other member states keeps growing.

Another recent study, this one on academic research, provides real-world evidence of the way the tools we use to sift information online influence our mental habits and frame our thinking. James Evans, a sociologist at the University of Chicago, assembled an enormous database on 34 million scholarly articles published in academic journals from 1945 through 2005. He analyzed the citations included in the articles to see if patterns of citation, and hence of research, have changed as journals have shifted from being printed on paper to being published online. Considering how much easier it is to search digital text than printed text, the common assumption has been that making journals available on the Net would significantly broaden the scope of scholarly research, leading to a much more diverse set of citations. But that’s not at all what Evans discovered. As more journals moved online, scholars actually cited fewer articles than they had before. And as old issues of printed journals were digitized and uploaded to the Web, scholars cited more recent articles with increasing frequency. A broadening of available information led, as Evans described it, to a “narrowing of science and scholarship.”31 In explaining the counterintuitive findings in a 2008 Science article, Evans noted that automated information-filtering tools, such as search engines, tend to serve as amplifiers of popularity, quickly establishing and then continually reinforcing a consensus about what information is important and what isn’t. The ease of following hyperlinks, moreover, leads online researchers to “bypass many of the marginally related articles that print researchers” would routinely skim as they flipped through the pages of a journal or a book. The quicker that scholars are able to “find prevailing opinion,” wrote Evans, the more likely they are “to follow it, leading to more citations referencing fewer articles.” Though much less efficient than searching the Web, old-fashioned library research probably served to widen scholars’ horizons: “By drawing researchers through unrelated articles, print browsing and perusal may have facilitated broader comparisons and led researchers into the past.”32 The easy way may not always be the best way, but the easy way is the way our computers and search engines encourage us to take.

In the development of its love story, Singin’ in the Rain follows a particular plotline that came to have a great deal of currency in Hollywood films, especially in “buddy” films (and most especially those directed by Howard Hawks), involving a kind of “love triangle” in which the long-standing friendship of two men (often a hero and his sidekick) is threatened by the attraction of one of them to a woman introduced early on (the ingénue, although often not exactly an innocent).26 Generally, this plot situation may be taken to carry homosexual overtones, so that the story becomes a parable about embracing heterosexual love. This interpretation is, of course, quite easily avoided, since most sidekicks have next to no discernible sex drive, at least during the film’s story,27 but it is surely significant that, in more recent times, the asexual sidekick is often replaced by a homosexual friend. And even the latter development may be explained away, given the utility of the sidekick plot situation and recent shifts in what audiences might accept as either “natural” or interesting wrinkles on the device. Nevertheless, the homoerotic tension in some of these relationships is significant enough to lay the entire tradition open to this interpretive avenue.

In the months and years following my participation in the completion of Oracle of Compassion: the Living Word of Kuan Yin, I did, in accordance with the deity’s former declaration—that she would continue sending important dreams, experience many dreams and visitations from Kuan Yin. It was after completing the transcription of some of the Kuan Yin quotes that I fell into a deep slumber. Just before awakening, I dreamt of Kuan Yin standing in my living room directly in front of the marble-tiled fireplace wherein Lena Lees and I would hold prayer circles. So real was this vision that I could hear the deity’s sweet voice explain that there is a specific meditation for connecting with the Celestial Wisdom! Witnessing Kuan Yin lie face down on the Oriental Carpet with arms outstretched over Her head, I watched as Her thumb and forefinger formed a triangle—the dhyana mudra specifically designated to Kannon called the dhyana mudra displayed by the fourth Dhyani Buddha Amitabha, also known as Amitayus Kuan Yin. It was then that Kuan Yin further explained the significance of this specific mudra; that it acts similar to a capstone on an obelisk—drawing wisdom to one who has demonstrated intention to be a teacher of wisdom. Aware that Kuan Yin was pointed in a northward direction, I surmised this particular alignment was also a significant aspect of the meditation—that this specific positioning was most ideal for receiving answers for prayers directed to the Her. Later in the dream, it was confirmed that each person attracts from the universe, realities that are aligned with their specific beliefs and values.

As crazy as all of this may sound to you, I know that our brains are able to control so many things depending on how we think about something. About twenty years ago, a business partner and I taught real estate investing seminars. One of the most significant factors that affects someone’s success in real estate, or any other endeavor, is belief. I’ve heard it said that if you believe you can or if you believe you can’t, either way, you’re right. Suppose you really honestly believe that you’ll succeed in real estate or any other endeavor. In that case, you’re about 1,000 times more likely to put in the effort and stick with it. If you don’t believe you’re going to succeed, then most people put in next to no effort to basically prove themselves right when nothing happens. At our seminars, we would demonstrate this by teaching the concept of “Spots.” We explained that according to an ancient methodology, we all have a weak spot and a strong spot. Speaking in a strong, confident voice, we’d say, “Here’s your strong spot right here,” and demonstrate this by touching the center of our forehead. “You also have a weak spot” (speaking in a softer, weaker voice). “It’s located in the soft fleshly spot right here behind your ear.” We again demonstrated and encouraged them to follow along. Then to give it a little emphasis, we added, “Careful, don’t push it too much, or you’ll get really weak!” Then we said, “We’ll show you how this actually works,” and invited one of the stronger-looking participants up onto the stage. We’d touch the person in their “strong” spot and ask them to hold their arm straight out to the side. “Now I’m going to push down on your arm, and I want you to resist me as much as you can.” We’d push down with a decent amount of effort, and our client’s arm would not budge down at all. “Now I’m going to touch your weak spot” (touching the person behind their ear). “And watch as I’m now able to push their arm completely down.” The crazy thing is that no matter how hard the subject tries to hold their arm up, after touching their “weak” spot, it drops right down with much less effort than during the first attempt. Then we said, “Now I want you to prove this to yourself. Pair up with the person next to you to test this out for yourself.” The room would buzz with the sounds of people talking as they discovered that the strong and weak spots really did, for the most part, work. Then we would switch the spots. “Isn’t it crazy that just because we told you to push on the strong spot behind your ear, that made you really strong? And when we told you to push on the weak spot in the middle of your forehead, that made you really weak?” we’d say. “No, no, you’ve got them backward!” the crowd would shout at us. At which point, we’d demonstrate that the spots worked just as well if you switched them, finally telling them, “We actually made all this up—but it works anyway!” What you tell yourself and what you believe really does make a difference. I don’t know if this helps to explain why I was hiking the Appalachian Trail. I was passionately committed to the belief that if I hiked the entire Appalachian Trail, then my foot and leg were going to have to be better. Each day that I hiked, with every mile further north that I went, heck, with every single step I took, I was reclaiming my life. I know that anything is possible. My adventure on the trail proved this to me each and every day. 14 May—Finding a Buddy You Can’t Avoid Pain, But You Can Choose to Overcome it. —Paulo Coelho Two and a half hours after leaving Shenandoah National Park, I arrived home.

These experiences, called “psychic” or psi, suggest the presence of deep, invisible interconnections among people, and between objects and people. The most curious aspect of psi experiences is that they seem to transcend the usual boundaries of time and space. For over a century, these very same experiences have been systematically dismissed as impossible, or ridiculed as delusionary, by a small group of influential academics and journalists who have assumed that existing scientific theories are inviolate and complete. This has created a paradox. Many people believe in psi because of their experiences, and yet the defenders of the status quo have insisted that this belief is unjustified. Paradoxes are extremely important because they point out logical contradictions in assumptions. The first cousins of paradoxes are anomalies, those unexplained oddities that crop up now and again in science. Like paradoxes, anomalies are useful for revealing possible gaps in prevailing theories. Sometimes the gaps and contradictions are resolved peacefully and the old theories are shown to accommodate the oddities after all. But that is not always the case, so paradoxes and anomalies are not much liked by scientists who have built their careers on conventional theories. Anomalies present annoying challenges to established ways of thinking, and because theories tend to take on a life of their own, no theory is going to lie down and die without putting up a strenuous fight. Though anomalies may be seen as nuisances, the history of science shows that each anomaly carries a seed of potential revolution. If the seed can withstand the herbicides of repeated scrutiny, skepticism, and prejudice, it may germinate. It may then provoke a major breakthrough that reshapes the scientific landscape, allowing new technological and sociological concepts to bloom into a fresh vision of “common sense.” A long-held, commonsense assumption is that the worlds of the subjective and the objective are distinct, with absolutely no overlap. Subjective is “here, in the head,” and objective is “there, out in the world.” Psi phenomena suggest that the strict subjective-objective dichotomy may instead be part of a continuous spectrum, and that the usual assumptions about space and time are probably too restrictive. The anomalies fall into three general categories: ESP (extrasensory perception), PK (psychokinesis, or mind-matter interaction), and phenomena suggestive of survival after bodily death, including near-death experiences, apparitions, and reincarnation (see the following definitions and figure 1.1). Most scientists who study psi today expect that further research will eventually explain these anomalies in scientific terms. It isn’t clear, though, whether they can be fully understood without significant, possibly revolutionary, expansions of the current state of scientific knowledge.

The Ukrainian scene remained as pluralistic at the turn of the twenty-first century as it had been after the declaration of independence. If anything, it became even more diverse. Eventually, all political forces had to accept the reality that Russian political solutions generally did not work in Ukraine. President Kuchma explained why in a book published in 2003, close to the end of his second term in office. The title was telling indeed: Ukraine Is Not Russia. THE MAJOR CHALLENGE to the democratic nature of the Ukrainian political process was the catastrophic economic decline that followed the declaration of independence and was often blamed on it, making not only the Leonid Brezhnev era but also the period of Mikhail Gorbachev’s reforms look like a paradise lost. In six years, between 1991 and 1997, Ukrainian industrial production fell by 48 percent, while the gross domestic product (GDP) lost a staggering 60 percent. The biggest loss (23 percent of the previous year’s GDP) occurred in 1994, the year of presidential elections and the signing of the first cooperation agreement with the European Union. These were figures comparable to but more significant than American economic losses during the Great Depression, when industrial production fell by 45 percent and GDP by 30 percent.

The following interpretation methods are all example-based: Counterfactual explanations tell us how an instance has to change to significantly change its prediction. By creating counterfactual instances, we learn about how the model makes its predictions and can explain individual predictions. Adversarial examples are counterfactuals used to fool machine learning models. The emphasis is on flipping the prediction and not explaining it. Prototypes are a selection of representative instances from the data and criticisms are instances that are not well represented by those prototypes. 2 Influential instances are the training data points that were the most influential for the parameters of a prediction model or the predictions themselves. Identifying and analysing influential instances helps to find problems with the data, debug the model and understand the model’s behavior better.

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The Scheffe test is the most conservative, the Tukey test is best when many comparisons are made (when there are many groups), and the Bonferroni test is preferred when few comparisons are made. However, these post-hoc tests often support the same conclusions.3 To illustrate, let’s say the independent variable has three categories. Then, a post-hoc test will examine hypotheses for whether . In addition, these tests will also examine which categories have means that are not significantly different from each other, hence, providing homogeneous subsets. An example of this approach is given later in this chapter. Knowing such subsets can be useful when the independent variable has many categories (for example, classes of employees). Figure 13.1 ANOVA: Significant and Insignificant Differences Eta-squared (η2) is a measure of association for mixed nominal-interval variables and is appropriate for ANOVA. Its values range from zero to one, and it is interpreted as the percentage of variation explained. It is a directional measure, and computer programs produce two statistics, alternating specification of the dependent variable. Finally, ANOVA can be used for testing interval-ordinal relationships. We can ask whether the change in means follows a linear pattern that is either increasing or decreasing. For example, assume we want to know whether incomes increase according to the political orientation of respondents, when measured on a seven-point Likert scale that ranges from very liberal to very conservative. If a linear pattern of increase exists, then a linear relationship is said to exist between these variables. Most statistical software packages can test for a variety of progressive relationships. ANOVA Assumptions ANOVA assumptions are essentially the same as those of the t-test: (1) the dependent variable is continuous, and the independent variable is ordinal or nominal, (2) the groups have equal variances, (3) observations are independent, and (4) the variable is normally distributed in each of the groups. The assumptions are tested in a similar manner. Relative to the t-test, ANOVA requires a little more concern regarding the assumptions of normality and homogeneity. First, like the t-test, ANOVA is not robust for the presence of outliers, and analysts examine the presence of outliers for each group. Also, ANOVA appears to be less robust than the t-test for deviations from normality. Second, regarding groups having equal variances, our main concern with homogeneity is that there are no substantial differences in the amount of variance across the groups; the test of homogeneity is a strict test, testing for any departure from equal variances, and in practice, groups may have neither equal variances nor substantial differences in the amount of variances. In these instances, a visual finding of no substantial differences suffices. Other strategies for dealing with heterogeneity are variable transformations and the removal of outliers, which increase variance, especially in small groups. Such outliers are detected by examining boxplots for each group separately. Also, some statistical software packages (such as SPSS), now offer post-hoc tests when equal variances are not assumed.4 A Working Example The U.S. Environmental Protection Agency (EPA) measured the percentage of wetland loss in watersheds between 1982 and 1992, the most recent period for which data are available (government statistics are sometimes a little old).5 An analyst wants to know whether watersheds with large surrounding populations have

regression line will have larger standard deviations and, hence, larger standard errors. The computer calculates the slope, intercept, standard error of the slope, and the level at which the slope is statistically significant. Key Point The significance of the slope tests the relationship. Consider the following example. A management analyst with the Department of Defense wishes to evaluate the impact of teamwork on the productivity of naval shipyard repair facilities. Although all shipyards are required to use teamwork management strategies, these strategies are assumed to vary in practice. Coincidentally, a recently implemented employee survey asked about the perceived use and effectiveness of teamwork. These items have been aggregated into a single index variable that measures teamwork. Employees were also asked questions about perceived performance, as measured by productivity, customer orientation, planning and scheduling, and employee motivation. These items were combined into an index measure of work productivity. Both index measures are continuous variables. The analyst wants to know whether a relationship exists between perceived productivity and teamwork. Table 14.1 shows the computer output obtained from a simple regression. The slope, b, is 0.223; the slope coefficient of teamwork is positive; and the slope is significant at the 1 percent level. Thus, perceptions of teamwork are positively associated with productivity. The t-test statistic, 5.053, is calculated as 0.223/0.044 (rounding errors explain the difference from the printed value of t). Other statistics shown in Table 14.1 are discussed below. The appropriate notation for this relationship is shown below. Either the t-test statistic or the standard error should be shown in parentheses, directly below the regression coefficient; analysts should state which statistic is shown. Here, we show the t-test statistic:3 The level of significance of the regression coefficient is indicated with asterisks, which conforms to the p-value legend that should also be shown. Typically, two asterisks are used to indicate a 1 percent level of significance, one asterisk for a 5 percent level of significance, and no asterisk for coefficients that are insignificant.4 Table 14.1 Simple Regression Output Note: SEE = standard error of the estimate; SE = standard error; Sig. = significance.

Table 14.1 also shows R-square (R2), which is called the coefficient of determination. R-square is of great interest: its value is interpreted as the percentage of variation in the dependent variable that is explained by the independent variable. R-square varies from zero to one, and is called a goodness-of-fit measure.5 In our example, teamwork explains only 7.4 percent of the variation in productivity. Although teamwork is significantly associated with productivity, it is quite likely that other factors also affect it. It is conceivable that other factors might be more strongly associated with productivity and that, when controlled for other factors, teamwork is no longer significant. Typically, values of R2 below 0.20 are considered to indicate weak relationships, those between 0.20 and 0.40 indicate moderate relationships, and those above 0.40 indicate strong relationships. Values of R2 above 0.65 are considered to indicate very strong relationships. R is called the multiple correlation coefficient and is always 0 ≤ R ≤ 1. To summarize up to this point, simple regression provides three critically important pieces of information about bivariate relationships involving two continuous variables: (1) the level of significance at which two variables are associated, if at all (t-statistic), (2) whether the relationship between the two variables is positive or negative (b), and (3) the strength of the relationship (R2). Key Point R-square is a measure of the strength of the relationship. Its value goes from 0 to 1. The primary purpose of regression analysis is hypothesis testing, not prediction. In our example, the regression model is used to test the hypothesis that teamwork is related to productivity. However, if the analyst wants to predict the variable “productivity,” the regression output also shows the SEE, or the standard error of the estimate (see Table 14.1). This is a measure of the spread of y values around the regression line as calculated for the mean value of the independent variable, only, and assuming a large sample. The standard error of the estimate has an interpretation in terms of the normal curve, that is, 68 percent of y values lie within one standard error from the calculated value of y, as calculated for the mean value of x using the preceding regression model. Thus, if the mean index value of the variable “teamwork” is 5.0, then the calculated (or predicted) value of “productivity” is [4.026 + 0.223*5 =] 5.141. Because SEE = 0.825, it follows that 68 percent of productivity values will lie 60.825 from 5.141 when “teamwork” = 5. Predictions of y for other values of x have larger standard errors.6 Assumptions and Notation There are three simple regression assumptions. First, simple regression assumes that the relationship between two variables is linear. The linearity of bivariate relationships is easily determined through visual inspection, as shown in Figure 14.2. In fact, all analysis of relationships involving continuous variables should begin with a scatterplot. When variable

COEFFICIENT The nonparametric alternative, Spearman’s rank correlation coefficient (r, or “rho”), looks at correlation among the ranks of the data rather than among the values. The ranks of data are determined as shown in Table 14.2 (adapted from Table 11.8): Table 14.2 Ranks of Two Variables In Greater Depth … Box 14.1 Crime and Poverty An analyst wants to examine empirically the relationship between crime and income in cities across the United States. The CD that accompanies the workbook Exercising Essential Statistics includes a Community Indicators dataset with assorted indicators of conditions in 98 cities such as Akron, Ohio; Phoenix, Arizona; New Orleans, Louisiana; and Seattle, Washington. The measures include median household income, total population (both from the 2000 U.S. Census), and total violent crimes (FBI, Uniform Crime Reporting, 2004). In the sample, household income ranges from $26,309 (Newark, New Jersey) to $71,765 (San Jose, California), and the median household income is $42,316. Per-capita violent crime ranges from 0.15 percent (Glendale, California) to 2.04 percent (Las Vegas, Nevada), and the median violent crime rate per capita is 0.78 percent. There are four types of violent crimes: murder and nonnegligent manslaughter, forcible rape, robbery, and aggravated assault. A measure of total violent crime per capita is calculated because larger cities are apt to have more crime. The analyst wants to examine whether income is associated with per-capita violent crime. The scatterplot of these two continuous variables shows that a negative relationship appears to be present: The Pearson’s correlation coefficient is –.532 (p < .01), and the Spearman’s correlation coefficient is –.552 (p < .01). The simple regression model shows R2 = .283. The regression model is as follows (t-test statistic in parentheses): The regression line is shown on the scatterplot. Interpreting these results, we see that the R-square value of .283 indicates a moderate relationship between these two variables. Clearly, some cities with modest median household incomes have a high crime rate. However, removing these cities does not greatly alter the findings. Also, an assumption of regression is that the error term is normally distributed, and further examination of the error shows that it is somewhat skewed. The techniques for examining the distribution of the error term are discussed in Chapter 15, but again, addressing this problem does not significantly alter the finding that the two variables are significantly related to each other, and that the relationship is of moderate strength. With this result in hand, further analysis shows, for example, by how much violent crime decreases for each increase in household income. For each increase of $10,000 in average household income, the violent crime rate drops 0.25 percent. For a city experiencing the median 0.78 percent crime rate, this would be a considerable improvement, indeed. Note also that the scatterplot shows considerable variation in the crime rate for cities at or below the median household income, in contrast to those well above it. Policy analysts may well wish to examine conditions that give rise to variation in crime rates among cities with lower incomes. Because Spearman’s rank correlation coefficient examines correlation among the ranks of variables, it can also be used with ordinal-level data.9 For the data in Table 14.2, Spearman’s rank correlation coefficient is .900 (p = .035).10 Spearman’s p-squared coefficient has a “percent variation explained” interpretation, similar

Thus, multiple regression requires two important tasks: (1) specification of independent variables and (2) testing of the error term. An important difference between simple regression and multiple regression is the interpretation of the regression coefficients in multiple regression (b1, b2, b3, …) in the preceding multiple regression model. Although multiple regression produces the same basic statistics discussed in Chapter 14 (see Table 14.1), each of the regression coefficients is interpreted as its effect on the dependent variable, controlled for the effects of all of the other independent variables included in the regression. This phrase is used frequently when explaining multiple regression results. In our example, the regression coefficient b1 shows the effect of x1 on y, controlled for all other variables included in the model. Regression coefficient b2 shows the effect of x2 on y, also controlled for all other variables in the model, including x1. Multiple regression is indeed an important and relatively simple way of taking control variables into account (and much easier than the approach shown in Appendix 10.1). Key Point The regression coefficient is the effect on the dependent variable, controlled for all other independent variables in the model. Note also that the model given here is very different from estimating separate simple regression models for each of the independent variables. The regression coefficients in simple regression do not control for other independent variables, because they are not in the model. The word independent also means that each independent variable should be relatively unaffected by other independent variables in the model. To ensure that independent variables are indeed independent, it is useful to think of the distinctively different types (or categories) of factors that affect a dependent variable. This was the approach taken in the preceding example. There is also a statistical reason for ensuring that independent variables are as independent as possible. When two independent variables are highly correlated with each other (r2 > .60), it sometimes becomes statistically impossible to distinguish the effect of each independent variable on the dependent variable, controlled for the other. The variables are statistically too similar to discern disparate effects. This problem is called multicollinearity and is discussed later in this chapter. This problem is avoided by choosing independent variables that are not highly correlated with each other. A WORKING EXAMPLE Previously (see Chapter 14), the management analyst with the Department of Defense found a statistically significant relationship between teamwork and perceived facility productivity (p <.01). The analyst now wishes to examine whether the impact of teamwork on productivity is robust when controlled for other factors that also affect productivity. This interest is heightened by the low R-square (R2 = 0.074) in Table 14.1, suggesting a weak relationship between teamwork and perceived productivity. A multiple regression model is specified to include the effects of other factors that affect perceived productivity. Thinking about other categories of variables that could affect productivity, the analyst hypothesizes the following: (1) the extent to which employees have adequate technical knowledge to do their jobs, (2) perceptions of having adequate authority to do one’s job well (for example, decision-making flexibility), (3) perceptions that rewards and recognition are distributed fairly (always important for motivation), and (4) the number of sick days. Various items from the employee survey are used to measure these concepts (as discussed in the workbook documentation for the Productivity dataset). After including these factors as additional independent variables, the result shown in Table 15.1 is

Since The Great Recession, the global financial crash of 2008-09, the debt-fuelled post-recession recovery has been the weakest in the post-war era (since the end of World War Two). Whereas total outstanding credit in the US after the Wall Street Crash grew from 160% to 260% of GDP between 1929 and 1932, the figure rose from 365% in 2008 to 540% in 2010. (And this does not include derivatives, whose nominal outstanding value is at least four times GDP).[34] A long depression and rising right-wing populism have followed, including the stunning ascendency of property tycoon and TV celebrity demagogue Donald Trump as the President of the US in 2016.[35] The British public’s vote in June 2016 to leave the EU delivered another shock of global significance. A chronic drift towards trade wars and protectionism is accelerating and in January 2018, US Defence Secretary Jim Mattis said that “great power competition, not terrorism, is now the primary focus of US national security”, putting Russia, China and – yes – Europe in the crosshairs of the world’s long-time dominant economic and military power. Adding to this age of anxiety is the accelerating automation revolution. What should be an emancipatory and utopian development only generates insecurity at the prospect of unprecedented mass unemployment. It can be no coincidence that all these crises are converging at exactly the same time. They cannot be explained away by cynical and shallow generalisations about ‘human nature’. In the course of this investigation we will see that in fact all of these crises have a common root cause: the decaying nature of capitalism and its tendency towards breakdown. Indeed, average Gross Domestic Product (GDP) growth rates in the world’s richest countries have fallen in every decade since the 1960s and are clearly closing in on zero. Rates of profit, manufacturing costs and commodity prices are also trending towards zero. Drawing on Henryk Grossman’s vital clarification of Karl Marx’s methodology, we shall see that capitalism is heading inexorably towards a final, insurmountable breakdown that is destined to strike much earlier than a zero rate of profit. Indeed, we shall also see that the next, imminent economic crash will result in worldwide hyperinflation. We will also show that the economic crisis is intensifying competition between nation-states, forcing them into a situation which threatens the most destructive world war to date.

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