![]() ![]() The interviews and information offer the most comprehensive look yet at how the secretive firm has won and lost big-name clients, why it's branched out from the family office business, and which growing pains have accompanied a firm that invests in and supports other "rocketship" enterprises. Insider talked to more than a dozen Iconiq insiders, obtained confidential fund documents, reviewed public filings, and spoke with others in the industry to learn the story of Makan and Iconiq's rapid rise to Silicon Valley preeminence. "If you think 'The Wizard of Oz,' he wants to be the guy behind the curtain that nobody sees. "He wants to be the most influential person in the world," one former colleague said of Makan. Membership in this super-exclusive club means access to a world of private deals, business connections, and VIP privileges made possible because of the stature of Iconiq's client roster - a feedback loop that strengthens the value of the Iconiq network and provides the fuel for Makan to grow the firm. In its short life, Iconiq has expanded from the basics of family-office activities to a dizzying array of investment arenas, from venture capital to real estate, that now verge on overshadowing its original business.Īt the center of it all is the remarkable network of money and power, carefully curated by Makan. ![]() By leveraging an early connection to Facebook's founding team, the South African-born businessman transformed himself from a rising star wealth manager at Morgan Stanley into a free-wheeling counselor to billionaires, responsible for $40 billion in funds under management.Īs Iconiq Capital has ballooned, its ambitions have too. Over the past decade, Makan has quietly built an unrivalled network of billionaire and celebrity clients through his high-end wealth-management firm, Iconiq Capital. Other celebrities have had far more success. Let’s focus on the solid line in Figure 5.4.Account icon An icon in the shape of a person's head and shoulders. The goal of a linear regression is to find the mathematical model, in this case a straight-line, that best explains the data. : Illustration showing three data points and two possible straight-lines that might explain the data. How do we decide how well these straight-lines fit the data, and how do we determine the best straight-line? Figure 5.4.2 , which shows three data points and two possible straight-lines that might reasonably explain the data. To understand the logic of a linear regression consider the example shown in Figure 5.4.2 In such circumstances the first assumption is usually reasonable. ![]() When we prepare a calibration curve, however, it is not unusual to find that the uncertainty in the signal, S std, is significantly larger than the uncertainty in the analyte’s concentration, C std. In particular the first assumption always is suspect because there certainly is some indeterminate error in the measurement of x. The validity of the two remaining assumptions is less obvious and you should evaluate them before you accept the results of a linear regression. The second assumption generally is true because of the central limit theorem, which we considered in Chapter 4. For this reason the result is considered an unweighted linear regression. that the indeterminate errors in y are independent of the value of xīecause we assume that the indeterminate errors are the same for all standards, each standard contributes equally in our estimate of the slope and the y-intercept.that indeterminate errors that affect y are normally distributed.that the difference between our experimental data and the calculated regression line is the result of indeterminate errors that affect y. ![]()
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