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During the first half of the twentieth century, the psychoanalytic approach founded by Freud and, to a much lesser extent, the behavioral conditioning approach founded by John Watson served as the eminent theoretical and treatment approaches to mental illness. During the 1950s, 1960s, and 1970s, many new approaches were developed as alternatives to the traditional psychodynamic approach. Psychologists were becoming well established in psychotherapy, augmenting their already acknowledged testing services. The humanistic, behavioral, cognitive-behavioral, and family systems approaches to treatment emerged as compelling and popular alternatives to the more traditional theories and interventions. Furthermore, the rise of the community mental health movement in the 1960s as well as the introduction of psychotropic medication in treating mental illness had powerful influences on clinical psychology. During the turbulent yet optimistic 1960s and early 1970s, clinical psychology continued to expand with increasing knowledge, tools, and professional resources. Finally, integrative approaches, such as the biopsychosocial perspective emerged, adding to the sophistication of thought and practice in the field. This chapter examines these alternatives to the psychodynamic viewpoint from a historical perspective. I introduce the persons responsible for these theories and how they emerged over time. The next two chapters highlight these perspectives
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Reviewing the OBJECT element How plug-ins work Plug-in pluses and minuses Adding plug-ins Testing your plug-in Configuring your server Introducing ActiveX ActiveX pluses and minuses Understanding the ActiveX security model Finding ActiveX controls Incorporating ActiveX controls Defining options (parameters) Getting around the Mac problem Testing your control
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If True, a separator bar appears before the control. Read-only. True if the control is one of Excel s built-in controls. The text that is displayed for the control. If the control shows only an image, the caption appears when you move the mouse pointer over the control. If True, the control can be clicked. A number that represents a graphic image displayed next to the control s text. Read-only. A code number for a predefined Excel command. The name of a VBA procedure to be executed when the user clicks the control. Determines whether a control appears pressed. . This property is available only for a CommandBarButton control. Determines whether the control appears with a caption and/or image. This property is available only for CommandBarButton and CommandBarComboBox controls. Text that appears when the user moves the mouse pointer over the control. An integer that determines the type of the control.
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From Table 3.1 we can see that the value of R 2 is 80.8%, which means that 80.8% of the variability in nutritional rating is accounted for by the linear relationship (the plane) between rating and the set of predictors, sugar content and ber content. Would we expect R 2 to be greater than the value for the coef cient of determination we got from the simple linear regression of nutritional rating on sugars alone The answer is yes. Whenever a new predictor variable is added to the model, the value of R 2 always goes up. If the new variable is useful, the value of R 2 will increase signi cantly; if the new variable is not useful, the value of R 2 may barely increase at all. Table 2.7 provides us with the coef cient of determination for the simple linear regression case, r 2 = 58%. Thus, by adding the new predictor, ber content, to the model, we can account for an additional 80.8% 58% = 22.8% of the variability in the nutritional rating. This seems like a signi cant increase, but we shall defer this determination until later. The typical error in estimation is provided by the standard error of the estimate, s. The value of s here is about 6.24 rating points. Therefore, our estimation of the nutritional rating of the cereals, based on sugar and ber content, is typically in error by about 6.24 points. Would we expect this error to be greater or less than the value for s obtained by the simple linear regression of nutritional rating on sugars alone In general, the answer depends on the usefulness of the new predictor. If the new variable is useful, s will decrease; but if the new variable is not useful for predicting the target variable, s may in fact increase. This type of behavior makes s, the standard error of the estimate, a more attractive indicator than R 2 of whether a new variable should be added to the model, since R 2 always increases when a new variable is added, regardless of its usefulness. Table 2.7 shows that the value for s from the regression of rating on sugars alone was 9.16. Thus, the addition of ber content as a predictor decreased the typical error in estimating nutritional content from 9.16 points to 6.24 points, a decrease of 2.92 points. With respect to outliers, Table 3.1 shows that there are three outliers in this data set, as indicated by R in the list of unusual observations: r Observation 8: Spoon Size Shredded Wheat r Observation 41: Frosted Mini-Wheats r Observation 76: Golden Crisp Since the residuals associated with these observations are all positive, we say that the observed nutritional rating is higher than the regression estimate given the sugar content and ber content of the cereal. This may be due to other factors not yet included in the model, such as sodium content.
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CCITT de nes relative power level as the ratio, generally expressed in dB, between the power of a signal at a point in a transmission channel and the same power at another point in the channel chosen as a reference point, generally at the origin of the channel. Unless otherwise speci ed (CCITT Recs. G.101, 223), the relative power level is the ratio of the power of a sinusoidal test signal (800 Hz or 1000 Hz) at a point in the channel to the power of that reference signal at the transmission reference point.
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