Part 2 book “Pearson new international edition” has contents: Multiple regression analysis, regression with time series data, regression with time series data, judgmental forecasting and forecast adjustments, the Box-Jenkins (ARIMA) methodology.
www.downloadslide.net MULTIPLE REGRESSION ANALYSIS In simple linear regression, the relationship between a single independent variable and a dependent variable is investigated The relationship between two variables frequently allows one to accurately predict the dependent variable from knowledge of the independent variable Unfortunately, many real-life forecasting situations are not so simple More than one independent variable is usually necessary in order to predict a dependent variable accurately Regression models with more than one independent variable are called multiple regression models Most of the concepts introduced in simple linear regression carry over to multiple regression However, some new concepts arise because more than one independent variable is used to predict the dependent variable Multiple regression involves the use of more than one independent variable to predict a dependent variable SEVERAL PREDICTOR VARIABLES As an example, return to the problem in which sales volume of gallons of milk is forecast from knowledge of price per gallon Mr Bump is faced with the problem of making a prediction that is not entirely accurate He can explain almost 75% of the differences in gallons of milk sold by using one independent variable Thus, 25% 11 - r22 of the total variation is unexplained In other words, from the sample evidence Mr Bump knows 75% of what he must know to forecast sales volume perfectly To a more accurate job of forecasting, he needs to find another predictor variable that will enable him to explain more of the total variation If Mr Bump can reduce the unexplained variation, his forecast will involve less uncertainty and be more accurate A search must be conducted for another independent variable that is related to sales volume of gallons of milk However, this new independent, or predictor, variable cannot relate too highly to the independent variable (price per gallon) already in use If the two independent variables are highly related to each other, they will explain the same variation, and the addition of the second variable will not improve the forecast.1 In fields such as econometrics and applied statistics, there is a great deal of concern with this problem of intercorrelation among independent variables, often referred to as multicollinearity 1Interrelated predictor variables essentially contain much of the same information and therefore not contribute “new” information about the behavior of the dependent variable Ideally, the effects of separate predictor variables on the dependent variable should be unrelated to one another From Chapter of Business Forecasting, Ninth Edition John E Hanke, Dean W Wichern Copyright © 2009 by Pearson Education, Inc All rights reserved 235 www.downloadslide.net Multiple Regression Analysis The simple solution to the problem of two highly related independent variables is merely not to use both of them together The multicollinearity problem will be discussed later in this chapter CORRELATION MATRIX Mr Bump decides that advertising expense might help improve his forecast of weekly sales volume He investigates the relationships among advertising expense, sales volume, and price per gallon by examining a correlation matrix The correlation matrix is constructed by computing the simple correlation coefficients for each combination of pairs of variables An example of a correlation matrix is illustrated in Table The correlation coefficient that indicates the relationship between variables and is represented as r12 Note that the first subscript, 1, also refers to the row and the second subscript, 2, also refers to the column in the table This approach allows one to determine, at a glance, the relationship between any two variables Of course, the correlation between, say, variables and is exactly the same as the correlation between variables and 1; that is, r12 = r21 Therefore, only half of the correlation matrix is necessary In addition, the correlation of a variable with itself is always 1, so that, for example, r11 = r22 = r33 = Mr Bump runs his data on the computer, and the correlation matrix shown in Table results An investigation of the relationships among advertising expense, sales volume, and price per gallon indicates that the new independent variable should contribute to improved prediction The correlation matrix shows that advertising expense has a high positive relationship 1r13 = 892 with the dependent variable, sales volume, and a moderate negative relationship 1r23 = -.652 with the independent variable, price per gallon This combination of relationships should permit advertising expenses to explain some of the total variation of sales volume that is not already being explained by price per gallon As will be seen, when both price per gallon and advertising expense are used to estimate sales volume, R increases to 93.2% The analysis of the correlation matrix is an important initial step in the solution of any problem involving multiple independent variables TABLE Correlation Matrix Variables Variables 3 r11 r21 r31 r12 r22 r32 r13 r23 r33 TABLE Correlation Matrix for Mr Bump’s data Variables Variables Sales, Price, Advertising, 236 Sales Price Advertising 1.00 -.86 1.00 89 -.65 1.00 www.downloadslide.net Multiple Regression Analysis MULTIPLE REGRESSION MODEL In simple regression, the dependent variable can be represented by Y and the independent variable by X In multiple regression analysis, X’s with subscripts are used to represent the independent variables The dependent variable is still represented by Y, and the independent variables are represented by X1, X2, , Xk Once the initial set of independent variables has been determined, the relationship between Y and these X’s can be expressed as a multiple regression model In the multiple regression model, the mean response is taken to be a linear function of the explanatory variables: mY = b + b 1X1 + b 2X2 + Á + b kXk (1) This expression is the population multiple regression function As was the case with simple linear regression, we cannot directly observe the population regression function because the observed values of Y vary about their means Each combination of values for all of the X’s defines the mean for a subpopulation of responses Y We assume that the Y’s in each of these subpopulations are normally distributed about their means with the same standard deviation, σ The data for simple linear regression consist of observations 1Xi, Yi2 on the two variables In multiple regression, the data for each case consist of an observation on the response and an observation on each of the independent variables The ith observation on the jth predictor variable is denoted by Xi j With this notation, data for multiple regression have the form given in Table It is convenient to refer to the data for the ith case as simply the ith observation With this convention, n is the number of observations and k is the number of predictor variables Statistical Model for Multiple Regression The response, Y, is a random variable that is related to the independent (predictor) variables, X1,X2, Á ,Xk, by Y = b + b 1X1 + b 2X2 + Á + b kXk + where For the ith observation, Y = Yi and X1,X2, Á ,Xk are set at values Xi 1,Xi 2, Á ,Xik Data Structure for Multiple Regression TABLE Predictor Variables Response Case X1 X2 Xk Y i n X11 X21 Xi1 Xn1 X12 X22 Xi2 Xn2 X1k X2k Xik Xnk Y1 Y2 Yi Yn 237 www.downloadslide.net Multiple Regression Analysis The ’s are error components that represent the deviations of the response from the true relation They are unobservable random variables accounting for the effects of other factors on the response The errors are assumed to be independent, and each is normally distributed with mean and unknown standard deviation σ The regression coefficients, b 0,b 1, Á ,b k, that together locate the regression function are unknown Given the data, the regression coefficients can be estimated using the principle of least squares The least squares estimates are denoted by b0, b1, Á , bk and the estimated regression function by N = b + bX + Á + bX Y 1 k k (2) The residuals, e = Y - YN , are estimates of the error component and similar to the simple linear regression situation; the correspondence between population and sample is Population: Y = b + b 1X1 + b 2X2 + Á + b kXk + Sample: Y = b0 + b1X1 + b2X2 + Á + bkXk + e The calculations in multiple regression analysis are ordinarily performed using computer software packages such as Minitab and Excel (see the Minitab and Excel Applications sections at the end of the chapter) Example For the data shown in Table 4, Mr Bump considers a multiple regression model relating sales volume (Y) to price 1X12 and advertising 1X22: Y = b + b 1X1 + b 2X2 + Mr Bump determines the fitted regression function: YN = 16.41 - 8.25X1 + 59X2 The least squares values— b0 = 16.41, b1 = -8.25 , and b2 = 59 —minimize the sum of squared errors: SSE = a 1Yi - b0 - b1Xi1 - b2Xi222 = a 1Yi - YN i22 i i for all possible choices of b0 , b1 , and b2 Here, the best-fitting function is a plane (see Figure 1) The data points are plotted in three dimensions along the Y, X1, and X2 axes The points fall above and below the plane in such a way that ©1Y - YN 22 is a minimum The fitted regression function can be used to forecast next week’s sales If plans call for a price per gallon of $1.50 and advertising expenditures of $1,000, the forecast is 9.935 thousands of gallons; that is, YN = 16.41 - 8.25X1 + 59X2 YN = 16.41 - 8.2511.52 + 591102 = 9.935 238 www.downloadslide.net Multiple Regression Analysis Mr Bump’s Data for Example TABLE Week Sales (1,000s) Y 10 Totals Means Price per Gallon ($) X1 Advertising ($100s) X2 10 12 10 15 12 17 20 1.30 2.00 1.70 1.50 1.60 1.20 1.60 1.40 1.00 1.10 14 15 12 10 15 21 112 11.2 14.40 1.44 114 11.4 Y Sales 17 ^ Y = 16.41 − 8.25 X1 + 59X2 16.41 B A 15 20 X2 Advertising $1.00 $1.60 $2.00 X1 Price FIGURE Point Week A Sales $1.60 Price Advertising B 17 $1.00 15 Fitted Regression Plane for Mr Bump’s Data for Example INTERPRETING REGRESSION COEFFICIENTS Consider the interpretation of b0, b1, and b2 in Mr Bump’s fitted regression function The value b0 is again the Y-intercept However, now it is interpreted as the value of YN when both X1 and X2 are equal to zero The coefficients b1 and b2 are referred to as the partial, or net, regression coefficients Each measures the average change in Y per unit change in the relevant independent variables However, because the simultaneous 239 www.downloadslide.net Multiple Regression Analysis influence of all independent variables on Y is being measured by the regression function, the partial or net effect of X1 (or any other X) must be measured apart from any influence of other variables Therefore, it is said that b1 measures the average change in Y per unit change in X1, holding the other independent variables constant The partial, or net, regression coefficient measures the average change in the dependent variable per unit change in the relevant independent variable, holding the other independent variables constant In the present example, the b1 value of -8.25 indicates that each increase of cent in the price of a gallon of milk when advertising expenditures are held constant reduces the quantity purchased by an average of 82.5 gallons Similarly, the b2 value of 59 means that, if advertising expenditures are increased by $100 when the price per gallon is held constant, then sales volume will increase an average of 590 gallons Example To illustrate the net effects of individual X’s on the response, consider the situation in which price is to be $1.00 per gallon and $1,000 is to be spent on advertising Then YN = 16.41 - 8.25X1 + 59X2 = 16.41 - 8.2511.002 + 591102 = 16.41 - 8.25 + 5.9 = 14.06 Sales are forecast to be 14,060 gallons of milk What is the effect on sales of a 1-cent price increase if $1,000 is still spent on advertising? YN = 16.41 - 8.2511.012 + 591102 = 16.41 - 8.3325 + 5.9 = 13.9775 Note that sales decrease by 82.5 gallons 114.06 - 13.9775 = 08252 What is the effect on sales of a $100 increase in advertising if price remains constant at $1.00? YN = 16.41 - 8.2511.002 + 591112 = 16.41 - 8.25 + 6.49 = 14.65 Note that sales increase by 590 gallons 114.65 - 14.06 = 592 INFERENCE FOR MULTIPLE REGRESSION MODELS Inference for multiple regression models is analogous to that for simple linear regression The least squares estimates of the model parameters, their estimated standard errors, the t statistics used to examine the significance of individual terms in the regression model, and the F statistic used to check the significance of the regression are all provided in output from standard statistical software packages Determining these quantities by hand for a multiple regression analysis of any size is not practical, and the computer must be used for calculations As you know, any observation Y can be written Observation = Fit + Residual 240 www.downloadslide.net Multiple Regression Analysis or Y = YN + 1Y - YN where YN = b0 + b1X1 + b2X2 + Á + bkXk is the fitted regression function Recall that YN is an estimate of the population regression function It represents that part of Y explained by the relation of Y with the X’s The residual, Y - YN , is an estimate of the error component of the model It represents that part of Y not explained by the predictor variables The sum of squares decomposition and the associated degrees of freedom are ©1Y - Y22 = ©1YN - Y22 + ©1Y - YN 22 SST = SSR df : n - = k + SSE + n - k - (3) The total variation in the response, SST, consists of two components: SSR, the variation explained by the predictor variables through the estimated regression function, and SSE, the unexplained or error variation The information in Equation can be set out in an analysis of variance (ANOVA) table, which is discussed in a later section Standard Error of the Estimate The standard error of the estimate is the standard deviation of the residuals It measures the typical scatter of the Y values about the fitted regression function.2 The standard error of the estimate is sy #x¿s = ©1Y - YN 22 Dn - k - = SSE = 1MSE Dn - k - (4) where n = the number of observations k = the number of independent variables in the regression function SSE = ©1Y - YN 22 = the residual sum of squares MSE = SSE>1n - k - 12 = the residual mean square The standard error of the estimate is the standard deviation of the residuals It measures the amount the actual values (Y) differ from the estimated values (YN ) For relatively large samples, we would expect about 67% of the differences Y - YN to be within sy #x¿s of zero and about 95% of these differences to be within sy #x¿s of zero Example The quantities required to calculate the standard error of the estimate for Mr Bump’s data are given in Table standard error of the estimate is an estimate of s, the standard deviation of the error term, , in the multiple regression model 2The 241 www.downloadslide.net Multiple Regression Analysis TABLE Residuals from the Model for Mr Bump’s Data for Example Y X1 X2 N Using Predicted Y (Y) YN ؍16.406 ؊ 8.248X1 ؉ 585X2 Residual (Y - YN ) 10 12 10 15 12 17 20 1.30 2.00 1.70 1.50 1.60 1.20 1.60 1.40 1.00 1.10 14 15 12 10 15 21 10.95 4.01 5.31 12.23 11.99 13.53 6.72 10.71 16.94 19.62 -.95 1.99 -.31 -.23 -1.99 1.47 -1.72 1.29 06 38 90 3.96 10 05 3.96 2.16 2.96 1.66 00 14 00 15.90 Totals (Y - YN ) The standard error of the estimate is sy #x¿s = 15.90 = 22.27 = 1.51 A 10 - - With a single predictor, X1 = price, the standard error of the estimate was sy #x = 2.72 With the additional predictor, X2 = advertising, Mr Bump has reduced the standard error of the estimate by almost 50% The differences between the actual volumes of milk sold and their forecasts obtained from the fitted regression equation are considerably smaller with two predictor variables than they were with a single predictor That is, the two-predictor equation comes a lot closer to reproducing the actual Y ’s than the single-predictor equation Significance of the Regression The ANOVA table based on the decomposition of the total variation in Y (SST) into its explained (SSR) and unexplained (SSE) parts (see Equation 3) is given in Table Consider the hypothesis H0: b = b = Á = b k = This hypothesis means that Y is not related to any of the X’s (the coefficient attached to every X is zero) A test of H0 is referred to as a test of the significance of the regression If the regression model assumptions are appropriate and H0 is true, the ratio F = MSR MSE has an F distribution with df = k, n - k - Thus, the F ratio can be used to test the significance of the regression TABLE Source 242 ANOVA Table for Multiple Regression Sum of Squares df Regression SSR k Error SSE n - k - Total SST n - Mean Square MSR = SSR>k MSE = SSE>(n - k - 12 F Ratio F = MSR MSE www.downloadslide.net Multiple Regression Analysis In simple linear regression, there is only one predictor variable Consequently, testing for the significance of the regression using the F ratio from the ANOVA table is equivalent to the two-sided t test of the hypothesis that the slope of the regression line is zero For multiple regression, the t tests (to be introduced shortly) examine the significance of individual X’s in the regression function, and the F test examines the significance of all the X’s collectively F Test for the Significance of the Regression In the multiple regression model, the hypotheses H0: b = b = Á = b k = H1: at least one b j Z are tested by the F ratio: F = MSR MSE with df = k, n - k - At significance level α, the rejection region is F Fa where Fa is the upper α percentage point of an F distribution with ␦1 = k, ␦2 = n - k - degrees of freedom The coefficient of determination, R2, is given by ©1YN - Y22 SSR R = = SST ©1Y - Y22 = - ©1Y - YN 22 SSE = SST ©1Y - Y22 (5) and has the same form and interpretation as r2 does for simple linear regression It represents the proportion of variation in the response, Y, explained by the relationship of Y with the X’s A value of R = says that all the observed Y’s fall exactly on the fitted regression function All of the variation in the response is explained by the regression A value of R2 = says that YN = Y—that is, SSR = 0—and none of the variation in Y is explained by the regression In practice, … R2 … 1, and the value of R must be interpreted relative to the extremes, and The quantity R = 2R (6) is called the multiple correlation coefficient and is the correlation between the responses, Y, and the fitted values, YN Since the fitted values predict the responses, R is always positive, so that … R … 243 www.downloadslide.net Multiple Regression Analysis For multiple regression, F = R2 n - k - ¢ ≤ k - R (7) so, everything else equal, significant regressions (large F ratios) are associated with relatively large values for R2 The coefficient of determination 1R22 can always be increased by adding an additional independent variable, X, to the regression function, even if this additional variable is not important.3 For this reason, some analysts prefer to interpret R2 adjusted for the number of terms in the regression function The adjusted coefficient of determination, R 2, is given by R = - 11 - R22 ¢ n - ≤ n - k - (8) Like R2, R2 is a measure of the proportion of variability in the response, Y, explained by the regression It can be shown that … R2 … R2 When the number of observations (n) is large relative to the number of independent variables (k), R2 L R2 If k = 0, YN = Y and R2 = R2 In many practical situations, there is not much difference between the magnitudes of R2 and R2 Example Using the total sum of squares in Table and the residual sum of squares from Example 3, the sum of squares decomposition for Mr Bump’s problem is SST = SST + SSE N N a 1Y - Y2 = a 1Y - Y2 + a 1Y - Y2 233.6 = 217.7 + 15.9 2 Hence, using both forms of Equation to illustrate the calculations, R2 = 217.7 15.9 = = 932 233.6 233.6 and the multiple correlation coefficient is R = 2R2 = 2.932 = 965 Here, about 93% of the variation in sales volume is explained by the regression, that is, the relation of sales to price and advertising expenditures In addition, the correlation between sales and fitted sales is about 965, indicating close agreement between the actual and predicted values A summary of the analysis of Mr Bump’s data to this point is given in Table Individual Predictor Variables The coefficient of an individual X in the regression function measures the partial or net effect of that X on the response, Y, holding the other X’s in the equation constant If the regression is judged significant, then it is of interest to examine the significance of the individual predictor variables The issue is this: Given the other X’s, is the effect of this particular X important, or can this X term be dropped from the regression function? This question can be answered by examining an appropriate t value 3Here, “not important” means “not significant.” That is, the coefficient of X is not significantly different from zero (see the Individual Predictor Variables section that follows) 244 www.downloadslide.net Appendix: Data Sets and Databases Thousands of Freight Car Loadings, Forest Products Year 2002 January February March April May June July August September October November December 17.6 19.0 19.1 18.7 19.1 19.9 18.3 19.1 18.6 18.8 16.7 17.3 2003 16.9 18.5 18.6 17.9 18.0 17.4 16.4 16.5 16.7 17.0 12.4 16.9 2004 16.1 15.8 16.0 16.6 16.2 15.8 16.6 15.8 15.6 15.6 14.4 14.3 2005 15.6 16.7 17.2 16.7 16.3 16.4 13.9 15.9 15.5 15.2 13.9 14.0 2006 13.2 14.5 14.7 14.1 15.2 15.3 14.9 14.0 15.3 14.4 14.2 15.0 Industrial Price/Earnings Ratio Year Quarter 4 4 1999 2000 2001 2002 P/E 17.56 18.09 16.47 18.73 22.16 21.80 20.82 14.09 13.61 13.29 12.50 12.35 12.46 13.30 14.73 15.40 Year 2003 2004 2005 Quarter 4 P/E 15.80 17.00 14.80 15.90 18.80 20.40 23.70 29.20 28.40 26.80 26.00 26.00 DATABASES Campsites A group of businesspeople in Spokane is planning to develop a number of campsites in the state One of their problems is to decide the amount to be charged per day According to their observations, the fee depends on a number of variables such as whether a swimming pool is accessible and the size of the campground In an effort to be objective, the following information was collected on 25 campgrounds in Washington (from the Rand McNally Campground Guide for Washington) and a computer analysis completed The variables analyzed were Y X1 X2 X3 X4 X5 X6 = = = = = = = Daily fee 1FEE2 Number of acres 1ACRES2 Number of sites 1SITES2 Whether there were flush toilets or not 1TOILET2 Whether there was a swimming pool or not 1POOL2 Whether there were electric hookups or not 1HOOKUP2 Number of additional recreational facilities 1ADD2 491 www.downloadslide.net Appendix: Data Sets and Databases Site 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Y 7.00 8.50 9.00 8.00 8.00 7.00 7.75 8.00 8.50 8.50 9.00 7.00 9.00 8.50 9.00 7.50 8.50 9.00 8.00 9.50 7.50 7.50 7.50 9.00 7.50 X1 X2 X3 X4 X5 X6 40 20 45 110 30 50 35 18 23 52 25 250 140 120 60 120 173 100 134 114 32 25 66 32 47 18 32 54 30 30 40 60 60 50 21 30 70 80 50 35 25 75 35 120 17 15 30 100 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 2 3 1 2 2 2 2 2 Financial Data for Selected U.S Companies Capital Intangibles Company Sales Employees Expenditures Expenditures Number ($ millions) (1,000s) ($ millions) ($ millions) 10 11 12 13 14 15 16 17 18 19 20 21 22 23 492 3,221.800842.0000 1,690.600120.9050 2,197.276439.0000 2,357.820623.3000 8,129.000035.0000 11,851.000023.0000 323.86063.9000 660.48568.3780 4,351.160150.9120 985.83575.5000 3,802.558139.6000 2,576.046422.6000 106.016028.0000 5,669.894546.8810 319.65702.8940 511.721710.1000 884.618922.8010 166.37502.3000 59.131018.0000 136.69703.1000 767.87998.1000 61.32801.1390 445.63875.8000 147.9000 93.0000 66.8670 59.5560 297.0000 394.0000 2.5900 10.9840 102.7080 16.6010 206.1020 50.6690 1.3120 103.0000 4.5770 19.5600 58.0940 3.9510 1.1400 2.0090 37.4250 1.3880 18.9780 30.6000 29.1000 55.8600 69.6080 29.0000 20.0000 4.2880 3.3720 217.0920 29.5900 157.3520 47.0790 42.0000 31.1000 2.2090 27.0000 33.0000 5.2890 14.5000 18.4930 18.0560 26.3250 12.6000 Cost of Goods Sold ($ millions) 2,285.2007 1,057.2002 1,387.0679 1,743.7952 7,423.0000 10,942.0000 233.5300 582.2649 4,156.8671 874.1287 2,997.2703 1,885.9053 84.6590 4,424.3007 246.6980 286.2288 467.4436 111.0310 43.7430 105.3300 519.3948 35.2020 213.2880 Labor and Research and Advertising Development Related Expense Expense Expense ($ millions) ($ millions) ($ millions) 599.7998 343.2000 661.3997 25.6320 1,178.0000 2,556.0000 22.8350 25.6250 12.8360 19.5000 518.0000 349.4910 35.5550 785.0000 42.8370 48.9990 36.5000 31.0000 26.3210 15.8880 112.1350 17.3140 12.1000 118.3000 114.9000 95.5680 51.9170 12.8000 11.6530 3.5290 44.9990 66.2640 112.3860 139.7290 48.8170 22.9370 141.3000 87.0000 1.8700 16.0350 4.0230 90.3250 46.3000 21.8470 2.4270 62.8060 28.0000 8.9000 11.1820 8.5000 9.2530 14.6000 30.7320 64.8730 8.7790 18.3650 16.4130 9.5000 8.7330 18.5000 1.1000 23.6520 29.6320 38.5420 56.9820 8.6330 2.7860 88.5230 1.4600 www.downloadslide.net Appendix: Data Sets and Databases Financial Data for Selected U.S Companies (Cont.) Capital Intangibles Company Sales Employees Expenditures Expenditures Number ($ millions) (1,000s) ($ millions) ($ millions) 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 2,259.6316 624.8040 329.9578 308.7327 598.9507 172.7920 910.8406 142.1830 425.0828 4,337.9140 271.0080 209.4520 62.4180 4,300.0000 390.6829 270.0127 97.9660 66.4090 56.5550 3,267.9551 2,745.7439 2,609.0000 1,677.6016 6,887.6210 10,584.1990 2,912.7644 4,309.5820 1,946.4766 9,254.1171 5,018.6914 1,510.7798 1,560.0750 2,794.0000 921.3689 1,253.5430 1,328.1138 1,314.6299 7,869.6914 73.0550 108.5090 1,422.4507 87.4350 7.8240 868.7107 137.3950 753.8848 1,445.0166 3,062.6316 2,450.4285 141.2580 16.0270 8.7000 4.0000 2.1070 5.0000 1.5760 7.0000 1.6000 6.8330 36.1000 2.0780 2.9980 3.8000 95.5000 5.1000 6.3000 2.0000 12.5261 3.9000 31.9790 43.9680 33.2000 11.6440 53.5000 132.1400 45.8540 66.8000 24.4530 151.2000 62.8510 15.3000 22.7000 37.4000 13.9700 13.0580 13.1160 27.3460 113.3710 7.8240 87.4350 16.5000 7.6550 9.5280 15.3400 2.8750 6.5480 27.0030 49.6190 32.6000 1.3040 228.7270 86.4030 14.9460 14.8080 39.7150 1.6590 14.4610 5.5880 72.5190 306.0220 27.1230 14.4690 3.7390 412.2886 30.8480 40.0340 6.9940 3.7570 1.6240 502.0398 251.0340 248.0001 284.6089 1,075.1719 714.2002 195.2680 275.3079 121.3300 1,431.0906 479.8997 207.9320 162.5190 256.0999 61.9380 66.4310 201.1960 36.9330 687.7998 26.5680 5.6630 100.4700 8.5150 26.6950 42.4040 14.1080 24.2870 84.1490 67.6310 81.9220 4.5050 27.3350 2.8080 8.3710 43.5920 27.8920 23.5420 5.5880 72.5190 31.8030 101.4290 6.5030 14.6060 7.6680 157.6030 10.8550 22.4540 5.2500 1.0090 6.9940 45.6140 16.1110 10.0000 87.4830 84.0390 22.6000 45.6430 67.3120 6.2920 121.3300 1.6240 63.5190 61.9380 7.3000 18.4340 13.9700 31.2730 43.0750 90.2000 20.6650 37.3860 69.8820 15.3750 7.7640 1.2120 9.7470 4.2120 99.9080 83.1580 88.8250 6.7300 Cost of Goods Sold ($ millions) 1,696.3772 408.4707 225.0410 239.1300 481.9436 118.7090 677.2527 126.9660 256.2837 2,344.1631 134.3790 176.4890 34.4700 2,108.5503 225.1080 189.8000 64.5920 57.2310 44.0550 2,517.7566 1,638.7969 1,874.0000 1,185.9717 4,721.9570 7,353.5000 2,189.5293 2,913.9036 1,403.4976 6,187.7851 3,478.0989 1,157.2117 1,188.9126 1,928.4988 597.7000 806.6758 851.8938 569.7327 5,580.5976 38.9980 77.1740 1,060.5420 51.3970 6.7860 686.0518 112.2350 596.5076 786.8777 1,446.5227 906.9639 95.1540 Labor and Research and Advertising Development Related Expense Expense Expense ($ millions) ($ millions) ($ millions) 421.8057 168.0200 20.9850 36.5000 45.0000 48.2000 7.0000 1.6000 6.8330 36.1000 35.7730 2.0780 2.9980 5.1000 6.3000 2.0000 31.9700 33.2000 53.5000 754.8977 45.0000 564.0000 24.4530 1,375.7996 3,204.2688 879.6548 993.3997 546.0508 2,125.2012 1,318.0999 13.9700 18.4340 780.7996 45.1640 236.5000 1.1550 6.4690 1,931.5005 22.8990 36.9990 305.7000 11.3940 20.5720 200.4850 30.7620 13.4000 1.9360 668.9910 6.7120 3.7000 116.5990 33.4700 12.9790 18.1220 39.8230 7.9090 58.2130 2.7310 12.1440 270.2576 20.2540 1.8970 44.0500 257.6807 18.3780 4.9080 2.5900 59.1300 19.5600 3.9510 161.2000 18.0000 6.4840 44.0700 93.4000 14.9460 1.6590 35.2020 95.9510 9.2530 27.6660 19.3190 18.3650 19.2020 32.0000 31.2730 174.4610 76.5000 43.0750 90.2000 6.3970 69.8820 4.2100 10.4000 83.1580 88.8250 39.8650 243.0450 423.2698 9.9040 9.6000 9.4440 32.0000 1.8510 7500 26.3330 1.8000 57.2710 44.1550 16.1100 87.4830 714.9990 121.3300 11.6440 33.4770 43.7430 18.9700 14.9460 1.6590 57.7210 108.1480 83.0000 36.1310 231.4690 377.1001 66.0560 40.5470 40.0810 334.8057 144.3000 39.7150 24.7010 70.1000 22.6500 48.6510 33.5620 42.1160 155.9000 99.8430 1.6500 25.4520 2.7200 52.1780 22.7240 1.9000 6.4200 76.1870 74.5240 90.5730 9.7580 493 www.downloadslide.net Appendix: Data Sets and Databases Financial Data for Selected U.S Companies (Cont.) Capital Intangibles Company Sales Employees Expenditures Expenditures Number ($ millions) (1,000s) ($ millions) ($ millions) 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 494 6.8030 1,852.0896 365.7217 1,981.4397 2,362.1326 357.0696 220.3790 1,082.4927 848.3799 1,112.0386 1,515.8816 1,328.5508 2,878.4956 4,312.0507 54.3250 122.9470 2,014.7056 969.8328 45.3670 255.1320 1,710.4700 365.8809 33.2650 53.7460 52.8760 9.6630 1,451.6687 321.3638 156.4580 52.1870 447.2100 86.8170 1,132.3499 217.4120 7.7640 1,581.8760 201.4650 198.9010 1,497.0076 153.2290 367.9246 494.4136 52.4550 37.3860 57.7120 586.4766 476.2078 15.3570 393.6016 4,701.1210 5.1000 25.4000 4.9030 28.7000 40.7000 5.5500 3.7000 17.9000 17.1000 16.5890 37.0000 19.9200 58.0000 56.6000 37.3860 57.1720 31.0000 18.5170 8.3500 3.3000 31.7000 3.4800 2.0870 5250 1.1420 2.4190 29.0000 4.9110 2.3500 8650 7.7670 1.1000 18.0150 3.2000 86.6820 20.8580 1.1000 9110 7.4000 1.8400 5.1000 8.3500 1.2120 8200 13.1190 3.8030 4.0510 4.5671 5.6000 7.5620 9.5230 89.5500 17.0620 155.8530 110.1000 12.6430 10.7860 51.3360 41.2990 74.5790 108.0460 44.6810 182.2670 169.2950 1.0660 13.7480 74.7910 40.8340 1.6430 10.6420 91.5640 20.0140 1.5120 2.0870 2.4190 12.7460 86.6820 13.1180 4.5670 1.5100 12.7460 1.2810 16.8570 4.4840 1.2810 142.2810 7.9670 9.7470 131.9400 11.0840 20.6490 19.3850 7.2210 1.3370 3.4390 44.3730 34.2780 16.8570 30.2360 353.2830 1.4590 57.7900 16.7160 141.2700 99.8430 52.1780 9.7580 52.1780 11.9490 44.6610 52.3290 6.2850 348.1426 66.9970 2.8130 7.5620 0.0000 54.2710 7.0670 20.2520 54.7540 6.7300 4.4840 42.2810 1.2160 7.9670 97.2690 11.0840 3.8620 20.6490 41.7940 19.3850 1.6970 10.5440 7.2210 5.8820 1.3370 4290 6.0210 3.4390 11.2110 3.1490 7.0620 44.3730 7160 34.2780 30.2360 53.2830 2.8890 48.6920 Cost of Goods Sold ($ millions) 2.3980 672.7947 217.5420 668.7720 1,055.4187 141.2700 67.1220 310.7820 386.0066 378.7710 758.5320 566.2200 1,247.2339 2,672.3262 26.5960 94.6720 700.4778 448.5286 15.7310 131.6750 752.5889 177.5500 19.7100 16.1820 27.1500 5.6960 505.8267 268.0159 114.1930 36.5130 280.3218 57.2600 785.0718 142.6020 6.4800 1,280.1670 169.2630 164.1940 1,098.2969 59.2350 230.1690 342.9849 26.8120 26.6950 36.9240 391.3706 244.7830 9.5280 265.3079 3,707.6846 Labor and Research and Advertising Development Related Expense Expense Expense ($ millions) ($ millions) ($ millions) 12.2490 4.5070 3.4720 634.0596 11.3940 2.1330 20.5720 315.8997 16.0000 7.3000 469.9229 323.7090 1.1500 6.4600 4.7670 17.6580 503.6768 9.4450 2.1230 12.2220 530.2456 25.8740 19.7100 16.1800 27.1500 5.6950 36.1200 57.2600 6.4800 59.3250 26.8120 26.6950 36.9240 57.5790 9.5280 359.0999 57.5700 73.9670 99.4080 9.2800 73.9670 6.4690 4.7670 9.4400 2.1230 25.8740 99.9080 29.0000 9.2800 4.9110 7230 28.4910 6.7300 55.2940 75.7000 36.8860 7.1610 114.9660 40.6150 91.2150 74.5950 36.9560 391.6277 260.3870 0.7520 1.4590 45.0900 91.2690 5.1820 42.5670 239.9010 16.7100 1.1550 7.6770 6.4690 4570 137.7250 1.1110 4.7670 18.0150 9.4400 2.1230 25.8740 3.2520 20.8580 1.1000 7.4000 1.8400 5.1000 8.3500 1.2110 3.8030 4.0510 5.6000 7.5620 2.8100 5.8820 6.0200 11.2110 3.1490 11.9490 148.0770 11.8950 161.3500 113.1280 18.9510 6.2610 65.6910 61.6940 77.3130 61.8300 115.5890 85.3970 37.6540 44.6610 3.8670 21.1460 8.5670 52.3290 6.2850 42.0600 23.7910 2.8890 19.7100 16.1820 27.1500 30.7620 13.4000 5.6960 1.9360 505.8267 1.3420 6.7120 3.7000 268.0159 12.2490 114.1930 36.5130 280.3280 4.5070 11.3940 2.1000 57.2600 785.0718 9510 20.5720 16.0000 142.6020 7.3000 6.4800 www.downloadslide.net Appendix: Data Sets and Databases Financial Data for Selected U.S Companies (Cont.) Capital Intangibles Company Sales Employees Expenditures Expenditures Number ($ millions) (1,000s) ($ millions) ($ millions) 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 1,167.83402.8100 12,298.398050.7000 439.47271.9020 29,127.0030108.7000 1,993.66248.0000 4,660.894518.1000 976.45788.8280 3,834.93246.6610 9,535.738242.7800 657.77761.2640 100.457043.0750 60,334.5110130.0000 2,150.000090.2110 18,069.000058.3000 109.738069.8870 592.77103.2520 4,642.394514.3280 2,072.441211.1480 4,509.382813.3540 34,736.0030207.7000 1,191.03374.2070 312.73004.2120 1,553.10779.1500 6,997.773430.0080 513.18805.1420 28,085.003094.8000 11,062.898034.9740 23,232.406037.5750 14,961.500047.0110 5,197.707024.1450 7,428.234333.7210 28,607.503067.8410 87.61006.7310 1,165.67363.5310 567.36501.5420 5,954.941416.2970 368.09402.3150 751.73276.2550 895.408710.9000 1,063.290816.1790 1,306.086719.3970 140.44401.9190 4,357.281252.1400 263.90483.7000 6,184.894594.5000 257.65093.3640 50.515052.5350 419.64704.3020 1,227.449020.0000 779.34508.8000 48.6920 1,221.8008 65.1100 1,897.0005 43.4190 636.1238 14.8590 316.7156 1,107.3838 56.1460 44.0680 4,186.9296 311.7000 1,680.0000 32.2560 123.7680 353.5999 270.1846 502.2720 1,760.7100 255.6150 76.5000 343.9539 956.1719 41.9800 2,913.0000 1,774.3904 1,049.6729 1,744.0364 762.2510 601.1216 1,344.3777 12.7120 26.6780 97.4910 732.0000 15.0860 51.1060 145.5140 51.1480 78.7700 3.5730 110.4470 7.4050 398.2000 14.4730 29.1000 22.0310 57.4030 22.0670 8.4580 10.4000 39.8650 9.9040 45.7820 28.4900 55.2940 68.2690 75.7000 36.8860 7.1610 40.6150 91.2150 74.5900 36.9560 3.8770 33.5620 42.1160 1.6500 2.7200 1.9000 6.4200 23.6410 11.2330 41.9800 32.5600 43.0250 90.2110 69.8870 4.2120 6.7310 10.4000 39.8650 9.9040 28.9400 55.9240 2.7160 13.5380 9.3840 25.7670 2.7490 55.8600 12.0830 27.2080 69.6080 7.5700 29.0000 20.0000 4.2880 3.3700 Cost of Goods Sold ($ millions) Labor and Research and Advertising Development Related Expense Expense Expense ($ millions) ($ millions) ($ millions) 1,017.60382.3500 9,285.71091,016.5000 263.810851.1480 20,032.000078.7700 1,755.56623.5730 3,675.6895440.7996 879.351691.8000 3,557.47347.4050 7,075.1875971.0000 565.017614.4700 72.783022.0310 45,999.00703,405.0000 1,460.799657.4030 13,442.00001,345.0000 97.01302.5200 420.320667.3300 4,085.0989324.0000 1,640.81181.2400 2,838.0845236.4540 26,053.906020.9400 865.647782.6730 452.413017.0050 988.8760185.6600 4,886.8125720.5000 375.359925.0200 20,632.00002,344.0000 8,259.76561,051.0000 19,964.6050994.0000 10,046.00001,126.7310 3,336.7566431.9976 5,714.30859.7320 24,787.60501,572.7996 74.551031.5580 1,035.71296.6000 480.511023.5230 4,540.4609444.8997 319.493910.6050 606.83183.5230 681.965626.3250 746.282012.6000 1,021.4856435.2998 122.321027.3350 3,540.96121,235.0000 203.34402.8080 5,224.00002,550.0000 190.41908.3710 18.056043.5920 341.5906135.6000 999.752027.8920 678.4258229.1270 7.0620 13.1160 27.3460 16.5000 31.1370 3.4000 15.3440 2.8250 6.5480 27.0030 49.6110 32.6000 1.3040 25.4000 4.9030 28.7000 40.7000 5.5500 2.0370 3.7000 2670 17.9000 12.5840 17.1000 11.3330 89.0000 16.5890 37.0000 19.9200 7.7130 58.0000 56.6000 31.0000 18.5170 3.300 31.7000 3.4800 6.8000 39.0000 16.6980 23.3000 35.0000 3.9000 8.3780 50.9120 5.5000 39.6000 22.6000 28.0000 46.8810 59.2350 64.6000 31.2730 86.0000 43.0750 11.6000 90.2000 69.8820 29.7730 4.2120 83.1580 290.0000 25.1000 88.8250 6.7300 1.4590 25.0000 4.9810 12.8000 16.7160 99.8430 52.1780 9.7580 58.2460 11.9490 231.0000 114.0000 89.7370 80.3250 15.0520 21.0000 52.0000 44.6610 2.4490 52.3290 18.5000 6.2850 9.9000 30.6000 14.6320 13.2830 29.1000 55.8600 3.2500 37.1000 69.6080 29.0000 20.0000 9.0000 4.2880 495 www.downloadslide.net Appendix: Data Sets and Databases Financial Data for Selected U.S Companies (Cont.) Capital Intangibles Company Sales Employees Expenditures Expenditures Number ($ millions) (1,000s) ($ millions) ($ millions) 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 496 72.1760 3,248.0076 921.1270 711.9827 72.4110 297.5686 677.8489 582.6238 3,750.4109 88.8070 306.9397 331.7366 546.9500 7.5910 3,479.4573 485.6138 123.2280 488.2327 100.7820 165.7970 274.8440 11,049.5000 1,154.8477 578.7107 124.5440 3,711.2029 124.8600 2,466.0000 2,829.2991 814.8196 4,051.7996 67.0390 240.5670 45.2140 69.9520 54.5490 317.4480 847.9927 467.9546 126.6750 85.7290 680.7666 211.3230 254.3030 1,396.8108 3,981.0000 3,943.0990 1.30002.5210 36.0620263.6167 12.659067.3340 12.5120133.3850 1.02501.2400 4.152020.9420 6.070017.0050 1.400025.0290 38.1700120.8280 2.33309.7320 2.800031.5880 5.20006.6000 8.900023.5230 30.60007.8900 41.3940170.3720 6.658058.6750 2.045010.6050 4.650020.4800 1.70302.4430 4.76603.2790 3.550021.7900 166.8480667.7998 14.419032.2360 11.492026.3000 1.80004.6280 63.4000303.3838 2.00005.2240 26.8650161.7000 36.2000156.8000 14.800048.5520 46.0000349.5999 28.00003.5010 4.00005.5670 2.00001.4110 81.000033.3333 1.12701.7720 5.784012.6650 24.000085.0240 4.845013.1650 14.00077.7490 49.00002.1610 8.220019.2340 1.56704.8350 3.10002.7620 29.416079.9820 52.9000188.3000 56.5320259.5000 29.5900 19.4460 10.5250 45.0790 42.0000 8990 31.1000 2.2090 27.0000 7.2110 33.0000 11.0250 5.2890 14.5000 18.4930 3.5250 5.2550 1.1111 1.6800 88.0003 2.9530 55.5000 4.0800 8.0141 1.9850 4.5720 2.3200 2.0202 27.1000 16.0111 2.6000 2.5170 1.3220 18.1010 8.0033 17.7200 11.0330 19.7930 2.3810 14.1441 49.4949 77.7878 15.6180 2.3570 28.2626 70.3000 49.9000 Cost of Goods Sold ($ millions) 50.9650 2,710.3455 771.0059 653.8069 60.0820 248.7160 613.3047 474.3450 3,240.7886 66.6540 220.4980 295.3848 439.0479 5.0480 3,100.5391 335.3318 96.6630 402.8457 88.7960 120.1080 213.1860 9,955.3984 1,037.4727 433.8230 101.5300 2,729.9280 79.7770 2,028.7996 2,261.0000 622.9507 3,036.5999 54.9070 184.1350 38.0970 65.4570 42.5990 254.1990 664.9578 400.5806 109.6830 72.8400 578.8528 171.4130 205.8410 1,000.2886 3,120.5999 3,352.3008 Labor and Research and Advertising Development Related Expense Expense Expense ($ millions) ($ millions) ($ millions) 24.8290 974.3379 23.5420 351.4700 93.0000 123.1000 169.2570 66.8670 1,132.6216 59.5500 2.5900 10.9840 16.0010 50.6690 1,177.5999 42.0000 20.9000 402.8400 4.0000 2.0000 3.0000 4,485.1953 424.4556 1.0111 23.6630 22.0222 51.0000 18.4021 930.2000 204.9000 1,215.2996 66.5620 61.6900 62.3201 52.3302 42.4444 80.1010 34.1021 4.0999 50.6410 9.9901 9.8175 65.0000 42.4381 3.8107 1,085.7996 1,275.7002 2.8940 10.1000 22.8010 2.3000 18.0000 3.1000 8.1000 1.1390 5.8000 16.0270 8.7000 4.0000 2.1000 5.0000 1.5760 93.0000 66.8670 77.0101 21.0000 4.0008 3.7521 22.0007 21.1234 12.3456 78.9101 91.0111 21.3141 3.2000 51.1617 18.1920 21.2223 24.2526 2.5860 27.2829 30.3132 33.3435 36.3738 39.4041 42.4344 45.4647 48.4950 51.5253 54.5556 57.5859 16.1580 75.8000 60.6162 3.3700 29.5900 157.3520 45.0790 42.0000 31.1000 2.2090 27.0000 33.0000 5.2890 14.5000 18.4930 18.0560 26.3250 15.0500 1.4320 12.6000 22.2426 28.3032 18.2022 24.2628 52.5000 30.3234 5.3300 36.3840 33.0000 2.6500 14.9000 25.2000 1.4150 56.6000 42.4446 3.0470 48.5052 54.5658 60.6264 66.6870 10.4000 1.0011 1.0022 1.0033 1.3090 1.8201 2.0880 3.4510 37.5000 42.3000 www.downloadslide.net Appendix: Data Sets and Databases Financial Data for Selected U.S Companies (Cont.) Capital Intangibles Company Sales Employees Expenditures Expenditures Number ($ millions) (1,000s) ($ millions) ($ millions) 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 1,260.2349 973.2527 19.9060 66.8260 178.7460 26.7510 20.5750 51.5960 106.1150 8.5160 308.8916 753 8069 41.2960 145.6840 51.3130 21.4070 585.6597 516.7239 316.8147 509.7000 341.3887 33.0660 200.5920 184.5810 217.7520 386.8118 69.1530 81.4650 329.5518 36.3870 344.7937 22.8030 196.3030 31.5660 108.8580 83.6260 390.8726 363.9839 52.2620 228.6110 60.8250 16.6890 39.8290 28.9020 8.7410 61.9446 17.2880 9.8850 18.0002 1.3200 2.1980 1.0560 43.1111 18.5216 2.6000 14.2421 5.7000 16.8750 1.1080 3.4190 1.0000 12.5358 8.2000 10.3000 7.0000 10.0000 7.1270 1.0872 4.0000 4.0500 4.0880 7.4040 12.1212 1.6220 6.0040 133.0000 7.5000 84.1000 5.4660 13.7124 1.7000 1.2320 6.1660 7.0160 4420 5.6500 1.5000 40.5000 62.1000 93.4000 27.0000 7.0000 103.0320 25.4530 5.6666 6.1110 5.5430 8.8888 7.7777 1.6940 4.6850 12.0818 15.8370 37.4620 2.5820 13.3250 1.5700 18.7842 56.0530 17.9320 3.9360 27.0360 7.1570 1.9540 5.3320 7.2780 7.3840 18.4880 1.6190 4.1890 12.2520 12.7246 24.7400 2.1060 5.9730 8.1264 1.2870 4.1220 17.3310 11.2700 5.1030 1.8370 1.4910 57.6000 3.9900 1.1040 55.6000 35.0470 11.4810 5.5580 1.4100 88.1388 138.0000 211.0113 82.1003 1.1620 9.9210 12.1402 13.1402 3.6210 12.1213 1.0087 8.0025 5.5554 80.9960 9.3610 12.1314 15.1617 8.1819 9.2021 20.0290 10.3570 10.1146 47.1213 48.1415 16.4950 8.0540 51.5355 57.5982 83.4952 99.9242 10.1115 92.4445 55.6677 40.5880 11.5610 1.1500 41.5600 45.3100 9.8540 1.5610 36.5000 32.1400 43.2000 Cost of Goods Sold ($ millions) Labor and Research and Advertising Development Related Expense Expense Expense ($ millions) ($ millions) ($ millions) 1,055.943612.0000 848.72274.0877 16.51703.3876 48.94804.5222 138.569043.4350 17.993018.1111 13.972014.2222 38.819088.9922 64.050012.4243 5.95007.8246 144.734042.4444 491.1160210.0050 28.132081.8118 105.163051.7100 35.973043.4400 12.955012.8888 359.835077.9999 376.41701.1007 267.24562.0008 375.3457179.9240 287.69079.0007 24.072012.7210 153.54807.6660 142.71608.7770 179.102078.3910 302.55862.9990 54.431011.3410 70.50804.4555 269.637712.1417 27.769021.8283 205.061092.9395 10.683096.9899 142.152097.9294 22.375095.0092 45.913092.6666 45.095092.5555 296.857758.2130 234.63202.7310 43.511012.1440 161.470020.5400 41.68201.8970 9.845018.3700 32.65804.9080 23.14102.5900 6.370059.3100 432.3777160.6660 63.6465 66.6768 69.7071 72.7374 75.7677 78.7980 81.8283 81.0077 77.0222 22.4443 47.8877 16.4370 12.5456 51.8196 21.4231 37.8286 13.6920 5.6670 86.8686 85.8686 86.8888 83.1111 82.2222 22.6665 44.6621 18.1716 15.1413 12.1110 9.8765 4.3210 8.1234 5.6788 12.4582 14.5220 1.4330 13.5620 18.0000 6.4860 44.0700 14.9460 1.6590 35.2020 9.2530 18.3650 27.6600 19.2020 2.1133 3.3210 4.2242 5.6210 6.2155 7.2102 8.9712 24.2601 23.2810 24.8588 2.7060 4.9340 24.5293 1.8480 59.6085 64.8821 8.9610 5.6000 76.7686 3.6080 86.7795 95.9594 94.9388 1.0790 89.9012 3.8620 13.8125 47.8552 51.9185 54.3321 4.8200 43.8388 2.2710 66.7274 53.5422 22.5673 10.0000 86.0000 16.0000 3.0730 30.7300 63.5300 30.0000 10.0000 56.6660 6.3530 497 www.downloadslide.net 498 Executive (CEO) Compensation Data Total Other Bonus Salary Compens Compens ($1,000s) ($1,000s) ($1,000s) ($1,000s) Age 275 429 325 105 25 289 69 38 129 11 282 423 20 448 12 687 1,452 37 489 78 89 24 102 11 82 24 29 11 52 17 74 92 108 64 55 89 31 40 13 453 1,948 1,735 643 1,461 357 669 2,094 597 889 514 466 2,833 427 1,856 1,652 782 613 1,488 447 1,752 2,497 768 2,342 3,409 64 55 47 65 63 54 61 63 57 56 57 48 50 54 60 60 49 56 58 50 63 64 60 71 64 Tenure (years) Exper (years) Market Pct Valuation ($ millions) Own 2 1 2 2 2 1 2 2 2 5 9 9 40 23 29 23 20 41 27 17 31 33 34 18 14 28 30 46 30 26 23 23 8 13 15 14 14 28 34 30 54.5 7.6 21.7 8.9 3.6 0.5 0.7 5.9 1.4 0.7 4.1 0.2 11.7 71.4 0.4 11.5 1.3 0.1 9.4 0.3 534.2 221.1 0.7 9.6 29.4 3.14 0.55 0.52 0.89 0.05 0.06 0.05 0.04 0.03 0.03 0.17 0.01 0.39 10.09 0.03 0.06 0.07 0.02 0.56 0.03 3.99 3.98 0.02 0.83 0.31 Firm Profits ($ millions) 91 145 -47 44 201 71 -187 1,166 377 224 79 189 -332 55 -507 856 14 -29 126 54 249 91 322 99 -99 Firm Sales ($ millions) 872 1,237 1,712 1,681 5,673 1,117 1,475 10,818 2,686 2,201 661 1,539 11,663 2,366 4,864 14,949 5,061 1,929 2,643 1,084 5,137 844 2,097 835 12,021 Appendix: Data Sets and Databases 173 1,441 1,646 294 1,254 325 658 1,723 504 822 374 447 2,781 128 1,782 1,137 761 505 976 434 1,010 956 700 1,813 3,396 Education* Professional Background www.downloadslide.net 38 862 221 391 101 238 25 104 380 107 1,487 198 15 174 80 440 117 182 183 13 209 16 28 71 34 47 36 143 32 34 56 56 12 97 134 74 98 76 2,244 64 601 59 1,554 61 462 61 587 55 688 54 893 60 600 60 570 60 854 62 1,034 51 1,187 55 4,039 55 517 59 616 51 684 62 1,456 52 1,088 45 444 50 1,622 57 1,126 64 2,027 62 600 52 558 62 561 61 2 2 2 1 1 2 2 1 2 2 *0 = no college degree; = undergraduate degree; = graduate degree 7 8 1 7 41 35 41 25 28 36 42 30 23 32 37 16 36 30 11 25 29 28 25 12 34 24 5 17 11 28 15 3 17 21 10 11 14 12 24 4.0 0.04 0.1 0.12 30.6 2.23 16.8 1.03 1.6 0.17 1,689.0 34.04 2.0 0.04 85.6 17.66 0.2 0.21 2.6 0.17 7.0 0.83 3.1 1.21 35.2 0.29 181.0 6.70 0.3 0.01 0.8 0.01 6.0 0.17 2.6 0.17 0.1 0.01 9.7 0.19 3.4 0.21 3.4 0.04 4.2 0.26 0.2 0.01 15.4 0.95 30 -85 82 27 -76 317 417 43 49 81 82 10 715 136 237 -1,086 98 48 -50 347 63 806 10 265 52 4,451 1,911 1,435 1,314 2,301 3,277 4,444 1,214 804 669 578 1,214 12,651 3,180 2,754 12,794 4,439 415 1,569 9,886 2,545 8,379 21,351 2,359 695 Appendix: Data Sets and Databases 2,108 597 616 237 571 269 721 328 538 741 607 1,044 2,409 287 567 682 1,226 952 432 1,085 1,009 1,711 408 543 278 499 www.downloadslide.net This page intentionally left blank www.downloadslide.net Index Page references followed by "f" indicate illustrated figures or photographs; followed by "t" indicates a table A Absenteeism, 223 age and, 223 Abstract, 43, 119, 204-205, 326 Accounting, 8, 13, 40, 220, 238, 277-278, 311 Accounting department, 8, 220 accuracy, 5, 15, 31, 35-37, 43-44, 54, 59, 89-90, 108, 127, 157, 162, 254, 270-272, 322, 343, 348, 410, 419, 438, 440, 442-443, 451, 463-464, 466 of information, 443 Acquisitions, 422 addresses, 80, 449, 476 adjustments, 15, 64, 119, 143, 321, 437-457, 473 Advances, 1, 67, 401, 447 Advantages, 81, 88, 100, 399-400, 444, 473 Advertising, 8, 102, 148, 155, 159, 189, 198-200, 206, 209, 228, 230, 236, 238-240, 242, 244-246, 265, 291-292, 323, 348-349, 417, 436, 461, 492-497 evaluating, 245 local, 155, 159, 206 product, 155, 206, 265, 323, 417, 461 retail, 8, 102, 155, 265 types of, 265 Affect, 3-4, 16, 89, 103, 143, 147, 220, 264, 295, 338, 400, 419, 438, 445 Against the Gods, 12, 436 Age, 12, 15-16, 144, 153, 204, 206, 209-210, 223, 251, 255-256, 258-260, 262, 265, 269-271, 438, 459 Agencies, 15, 119, 133, 141 agreement, 244 Agreements, 107 AIDS, 321 Aircraft, 422, 446 business, 446 Allocations, anticipate, 138-139, 206 appearance, 121, 195, 360 Application, 40, 89, 141, 143, 204, 263-264, 297, 323, 379, 400, 445-446, 450, 454, 456 Applications, 7, 11-12, 28, 38, 55, 57, 69, 78, 86, 89, 113, 115, 117, 124, 134, 141, 144, 168, 171, 173, 180, 195, 198, 228, 231, 238, 259, 290-291, 295, 301, 319, 323, 351-352, 400-401, 434, 440, 442, 445, 460-462, 478 Art, 13, 117, 457 Assets, 94, 276 attention, 2, 4, 9, 15, 120, 160, 200, 464 attributes, audio equipment, 331 Australia, 440-441 Authority, 9, 15-16, 66, 68-70, 97, 113, 209 availability, 1, 4, 7, 144, 189, 206, 417, 438, 466-468 Available, 1-2, 4-7, 9, 11-12, 31-32, 61-62, 65, 67, 72, 75, 81, 86, 93, 95, 100, 103, 108, 119, 121, 141-144, 161, 168, 183, 186, 189, 192, 212, 285, 288-289, 303, 335, 337, 339, 347, 367, 399-400, 419, 427, 438, 443-444, 447, 449, 453, 456, 465-466, 468-470, 473, 487 Awareness, 7, 139 B Bankruptcy, 10, 40 Behavior, 17, 28, 33, 46-47, 78, 134, 138, 178, 194, 235, 321-322, 334, 360, 362, 369-370, 382, 386, 390, 393, 395, 459, 476 Benefits, 164, 264, 444, 449, 454, 460, 467 service, 164, 467 biases, 466 bibliography, 443, 457 Bid, 211-212, 284 board of directors, 10, 382 boldface type, 483 Bond market, 278, 280-281 Bonds, 93 brainstorming, 438 Brand, 265 Brand loyalty, 265 Breakdown, 256 Broker, 275 Budget, 15, 40, 164, 209, 419 Budgeting, 438 Bureau of the Census, 141 Business cycle, 17, 33, 138-139, 143, 153 Business cycles, 144 political, 144 Business environment, 2, 120, 442, 469 Business operations, 119 Business planning, 100, 156 business plans, Business review, 13, 457 Business Week, 264 Buttons, 11 Buyers, 160 C Call centers, 2, 40 Canada, 325 Capabilities, 7, 31, 141, 173, 460, 466 Capacity, 15, 40, 220, 443 Capital, 10, 147-148, 264, 440, 460, 475, 492-497 growth, 10, 147, 440 requirements, 264 working, 440 carrying costs, 155 Case study, 278, 339, 455 Cash flow, 155-156 Cash flows, 143, 264 technology and, 143 Catalogs, 210-211 Central America, 119 Centralization, 468 Change in demand, 336 Character, 356, 423, 476 Checkout, 209, 268-269 Chief executive officer, 262 Children, 144, 265 Claims, 417 Clearing, 165 clothing, 33 coding, 204, 349 Collections, 119, 375 Columbia, 164 Columns, 12, 170-171, 291, 301, 351, 372, 376, 379, 384, 389 commenting, 430 Commitment, 440 Commodities, 145, 153 Companies, 7, 107, 141, 147, 164-165, 263, 265, 445-446, 454, 487, 492-497 Compensation, 262-263, 276, 487 Competition, 33, 35, 59, 100, 103, 107, 401, 440, 446, 468-469 Competitors, 8, 206, 440 complaints, 419, 446 Compliance, 164 Component parts, 120 compromise, 254 Computer software, 6, 180, 208, 238, 245 Conditions, 4, 17-18, 40, 120, 200, 222, 225, 335, 349, 437, 440, 443, 451, 466, 469 Confidence, 22, 24-26, 30, 43, 139, 245, 261, 315, 367, 402, 464 Consideration, 4, 34, 44, 63, 94, 129, 263, 438, 440, 449 Consignment, 273 Consistency, 15 Constraints, Construction, 70, 251, 278, 320, 325, 329-330, 335-337, 355, 400 Consumer credit, 10, 52, 102, 160, 224, 344, 416, 473 Consumer goods, 32, 470-471 Consumer preferences, 16 Consumer Price Index, 144-145, 147 components of, 145, 147 Consumer products, 401, 449-450 Consumers, 10, 15, 143 Consumption, 32-33, 206, 216-217, 252-253, 295, 325, 332-337 consumer, 32, 332 Contacts, 9, 108-111, 265, 427-430, 453 Continuity, 417 Contract, 139, 143, 295 contractions, 17 Contracts, 206, 448 Control, 4-5, 40, 79-80, 90, 206, 213, 265, 323, 349, 356, 376-383, 436, 446, 449, 459, 461-462, 465 Controlling, 165 Conversation, 227, 282, 345 cookies, 54, 113, 167, 349, 430-431 Cooperation, 164, 466 Coordination, 466-467 systems, 467 Copyright, 1, 15, 61, 119, 175, 235, 295, 355, 437, 459, 477-478, 484, 487 Corporate strategy, 204 corporation, 10, 30, 36, 95, 134, 139-140, 169, 213, 272, 304, 313, 330, 352, 367, 372, 379, 382-384, 387-388, 393, 409, 422, 434, 439 Corporations, 487 corrections, 32 cost accounting, 220 Cost of goods sold, 492-497 Cost-benefit analysis, 441 Cost-of-living adjustments, 143 Cost-of-living index, 15 Costs, 16-17, 32, 34, 100, 104, 155, 164-165, 215-216, 220-222, 254, 278, 400, 443, 445, 449, 467 distribution, 445 sales and, 155, 449 CPI, 145 credibility, 451 Credit, 10, 40, 52, 102, 160, 164, 206, 224, 273-274, 344, 416, 446, 473 Credit cards, 164 Curves, 33-34, 125, 127-130, 178, 200 Customer service, 9, 109 Customers, 2, 9, 18, 37-38, 100, 148, 155, 209, 268, 318-319, 325, 327, 340, 351, 438, 443, 446 D data, 1-8, 10-12, 15-48, 50-59, 61-86, 88-90, 92, 94-98, 101-105, 109-110, 112-115, 119-121, 123-131, 133-135, 137, 139-142, 144, 146-149, 151, 154, 156, 160-161, 163, 166-171, 175-184, 186-200, 202-204, 206, 208-213, 216, 220-226, 228, 230-231, 236-239, 241-242, 244-249, 251-252, 254-256, 261-264, 266, 268-271, 274-276, 278-279, 282, 284-285, 288-293, 295-341, 343-352, 354, 355-356, 360, 364-368, 370, 372-373, 375-377, 379, 382-383, 387-388, 390, 392, 394-396, 399-403, 405-406, 409-410, 412-419, 422-424, 426-428, 430, 433-435, 437-440, 443-448, 453-456, 459-465, 467-476, 487-499 Data collection, 5, 8, 15, 443, 453, 465 data processing, 15 data storage, 65, 74, 90 data value, 472 Database, 103, 154, 288, 465, 468, 473, 487 purpose of, 103, 468 501 www.downloadslide.net databases, 90, 144, 276, 467-468, 487-499 dates, 130 Deadlines, 443 Debt, 10-11, 206, 278 Decentralization, 468 Decision makers, 2, 5, 447-448, 459 Decision making, 144, 292, 447-449, 474 Decision-making, 4, 6, 9, 15, 40, 89, 144, 261, 400, 447-449, 460, 462-463, 465-466, 468 group, 40, 466 in organizations, 460 Decision-making process, 4, 6, 9, 40, 89, 400, 447, 460, 462-463, 465-466, 468 Default values, 101, 259 Deflation, 145-146, 332 Demand, 2, 13, 32, 40, 54, 72, 89-90, 93-94, 100, 102, 112-113, 128, 141, 143, 151, 155-156, 161, 173, 206, 320-321, 325, 332, 335-336, 354, 400, 403, 405, 412, 436, 449, 454, 457, 466, 469 aggregate, 155 change in, 32, 336, 469 derived, 454 excess, 466 increases in, 32 inelastic, 143 price elasticity of, 320 prices and, 143 Demand deposits, 335-336 Demand for products, 89, 400 Department of Commerce, 124, 335, 471 Dependent variables, 206, 320, 445 Deposits, 2, 164, 335-336, 487 design, 1, 3, 227, 282, 345, 451 diagrams, 203 disabilities, 66 Discounts, 155 Discrimination, 144 Disposable income, 265, 304-305, 307-312, 315, 330, 332-333 Distance, 179, 188, 220, 263 Distribution, 3, 22-26, 28, 54, 59, 80, 180, 182-183, 191-194, 242-243, 245, 247, 260, 298, 436, 438, 441, 445, 448, 468, 477-480, 482-483 Distribution centers, 54 Dividends, 147, 327-328 Documentation, 156 Dollar, 32, 40, 51, 101, 119, 144-145, 147, 153, 176, 178, 315, 464, 474, 487 exchange rates, 119 Dollars, 8, 48, 119, 123, 125, 144-146, 148-149, 151, 153-154, 156, 176, 179, 188, 208, 213, 218, 276, 278, 307, 315, 330-332, 337-338, 418-419, 422, 449, 470-471, 487 Dominance, 442 Draft, 284 Drugs, 440 Dynamics, 439, 441 E Earnings, 44, 46, 119, 147, 208, 265, 279, 327-328, 491 test, 44 E-business, 469 Econometric models, 13, 33-34, 320-321, 354, 436 Economic development, 225 Economic forces, 320 Economic growth, rates, Economic policy, 3-4, 139 Economic variables, 32, 321, 417, 462 Economics, 173, 322, 335, 445 Economy, 3-4, 127, 138-140, 143, 145-146, 159, 173, 206, 226, 264, 282, 321, 335, 345, 440, 468-469 team, 469 Education, 1, 7, 10, 15, 61, 119, 175, 206, 235, 263, 295, 355, 437, 459, 477, 487, 490 Efficiency, 468 Elasticities, 333 Elasticity of demand, 320 income, 320 price, 320 email, 9, 109 Embargo, emphasis, 73, 441, 462, 469 Employees, 4, 10, 12, 15, 51, 101-102, 206, 217-219, 223, 332, 492-497 Employment, 17, 89, 119, 143, 206, 335-336, 400-401, 502 470 service employment, 335-336 Enterprise resource planning, Enterprise resource planning (ERP) systems, Entities, 264, 422 Entrepreneurs, Environment, 2, 7, 32, 65, 120, 264, 332, 438, 440-442, 446, 451, 469-470 Equilibrium, 320 Equity, 278 Equity financing, 278 Error correction, 322, 324, 354 Europe, 440-441 Evaluation, 5, 13, 35, 103, 145, 213, 303, 343, 459, 465-466 evidence, 25, 28, 59, 75, 112, 168, 178, 183, 190, 192, 235, 268, 304, 396, 419, 438, 445, 449 Exchange, 99, 119 Exchange rate, 119 fixed, 119 Exchange rates, 119 forecasting, 119 Expansion, 17, 139, 440, 443, 475 expect, 6, 23, 25, 37, 178, 180, 188, 241, 273, 284, 330, 465 Expectations, 139, 265, 443, 466 Expenditures, 8-9, 40, 148, 198-200, 206, 209, 228, 230, 238, 240, 244, 264-265, 268-269, 419, 440, 492-497 Expenses, 100, 215-216, 236, 278, 291 Experience, 2, 34, 74-75, 90, 104, 128, 167, 202, 222, 255-256, 259, 262-263, 344, 360, 382, 396, 440, 447, 450, 454 expertise, 4, 129, 453, 466 Explanations, 76, 246 Exports, 470-473 Extraordinary items, 47 F Failure, 288, 290, 450-451 Family, 152, 155, 268-269, 417 feasibility study, 325 Feature, 433 Federal government, 3, 40 Federal Reserve, 2, 119, 141, 335 Federal Reserve Board, 2, 141, 335 feedback, 6, 439-440, 464-466 Fields, 143, 235, 447 Filtering, 34 Finance, 9, 89, 108-109, 278, 322, 400, 422-423, 427 Financial resources, 453 Financial services, 164, 422 Firms, 104, 143, 211-212, 217, 262, 264, 321, 440, 447, 466-467 Flexibility, 81 Focus groups, 453 Food, 8, 16, 33, 46, 53, 145, 166-167, 225, 268-269, 344, 348, 440, 453-454, 475 production, 8, 16, 167, 453, 475 Forecasting, 1-13, 15-26, 28-48, 50-59, 61-65, 68-70, 78-80, 82, 88-89, 92-95, 97-98, 101-106, 111, 113, 117, 119-120, 123, 126, 128-129, 133, 139-141, 143-144, 161, 167, 173, 175, 181, 183, 192-193, 197, 200, 209, 213, 219, 223-225, 235, 254-255, 263-264, 268, 270-271, 278, 287-288, 292, 295-296, 301, 303-304, 307, 309-310, 313, 318, 320, 323-324, 327-328, 332, 334, 339, 341, 343, 349, 354, 355-356, 360-363, 366-367, 371, 375, 377, 382, 388, 393, 397-400, 402-403, 413, 415-419, 423, 426-427, 430, 432, 436, 437-457, 459-476, 477, 487 sales, 2-3, 5, 8-10, 16, 18-19, 30-33, 39, 46-48, 50-51, 53-55, 62-65, 70, 82, 88, 95, 97-98, 101-102, 113, 117, 119, 123, 126, 128, 133, 139-140, 144, 161, 167, 175, 197, 200, 209, 224, 235, 254-255, 263-264, 270-271, 295, 301, 303-304, 307, 309-310, 313, 323, 332, 339, 341, 343, 349, 355, 388, 393, 397-399, 413, 415-419, 430, 440-442, 449-455, 459-460, 462, 464, 469-470, 474-475 Forecasts, 1-9, 11, 13, 32-35, 40, 44, 47-48, 54, 61-62, 64-65, 70, 72, 74-76, 79-83, 88-90, 97-98, 101-103, 109-111, 113-115, 119-120, 123, 128-129, 133-134, 139-141, 143, 150-151, 154, 156, 159-161, 163, 166-170, 200, 202, 221, 242, 264, 296, 310, 314, 320-323, 330, 332, 334, 345, 348-350, 354, 355-356, 360-367, 370-371, 374-376, 378-379, 381-386, 392-393, 396-403, 405, 410, 415-416, 419, 421-422, 424, 427, 430, 433, 435-436, 437-438, 440-445, 447-448, 451-454, 456-457, 459-460, 462-464, 466-469, 472-476 Foundations, 457 Freedom, 23-25, 28, 39-40, 69, 185, 191-192, 197, 207, 241, 243, 245-247, 257, 260, 264, 266, 366, 483-484 Frequency, 265 Fund, 92, 127-128, 171-173, 419 G GDP, 335 Gender, 248, 250-251, 265, 270-271 General Fund, 419 Germany, 467 Global economy, GNP, 17, 208, 338 Goals, 4, Goods, 2, 32, 123, 144, 153, 220, 470-471, 492-497 Government, 3-4, 15, 40, 119, 133, 141, 143-144, 264, 322-323, 325, 335, 440, 470 Government agencies, 15, 119, 133, 141 Government spending, 144 Graphs, 170, 228, 230, 403, 435-436 Gross domestic product, 3, 335 components of, 335 real, 335 Gross national product, 17, 208 GNP, 17, 208 Gross sales, 273 Group, 23, 25, 28, 39-40, 78, 112, 288, 366, 387, 396, 439-442, 466-467, 491 group dynamics, 439, 441 groups, 10, 104, 270, 278, 370, 392, 418, 440-442, 453 development of, 278 Growth rate, 127, 201-203, 217 Guidelines, 117, 460 H Health care, 103-104 hiring process, 470 HTML, 141 hypothesis, 23, 25, 39, 190, 192, 197, 213, 219, 224, 242-243, 246, 260, 265, 270, 300-301, 303, 336, 365 I illustration, 126, 354 Implementation, 5, 32, 460, 466 Impression, 166, 389 Inc., 95, 103, 222, 331, 478 Incentives, 263 Income, 32, 47-48, 99-100, 144, 147, 206, 264-265, 268-269, 271-272, 278, 301-305, 307-312, 315, 317, 320-321, 330-333, 335-337, 417, 422, 471 differences in, 303, 307, 330 disposable, 265, 301, 304-305, 307-312, 315, 330, 332-333 increase in, 268, 307, 309, 337 market, 144, 206, 265, 278, 422 national, 48, 147, 330, 335, 417, 471 per capita, 32, 320, 332-333 personal, 144, 271-272, 301, 304-305, 307, 310-311, 315, 331, 335-337, 471 Income elasticity, 307 Independent variables, 228, 235-237, 239-241, 244, 247, 251-261, 263-266, 268-272, 276, 279, 284, 286, 301, 320, 325, 335-337, 345, 347, 350-351, 355, 360, 425, 486 Indexes, 33, 130-132, 134, 137-138, 145, 148-149, 151-153, 156-157, 159-160, 167-168, 471, 474 Industry, 28, 40, 103-104, 223, 265, 321, 330-331, 401, 440, 442, 488 infer, 301, 334 Inflation, 4, 16, 32, 120, 143-144, 164, 251, 265, 267, 269, 348 expectations of, 265 Inflation rate, 143 Information, 7, 9, 19, 32, 34, 62, 64, 73, 80, 88, 132, 134, 138, 140, 144, 161, 168, 178, 186, 189, 193, 210, 227, 235, 241, 251-252, 254, 265, 282, 325, 335, 345, 355, 387, 402, 405, 417, www.downloadslide.net 430-431, 433, 438, 441, 443, 446-447, 451, 455, 467-469, 491 Information processing, 144 Information systems, 467 Innovation, Installations, 222 Insurance, 104, 107, 164, 200 types of, 164 Intangibles, 492-497 Integration, 354 intelligence, 445, 447 Interdependence, 336-337 Interest, 1-4, 6-7, 10, 15, 21, 33, 48, 61, 82, 86, 89, 100, 103, 108, 115, 119, 133, 138-139, 164, 200, 206, 211-213, 244, 254, 262, 264-265, 278-281, 284, 310, 320, 322, 400, 437, 441, 444, 451 credit, 10, 164, 206 Interest rate, 3, 138, 211-213, 278-280 current, 138 Interest rates, 2, 206, 212, 264-265, 279-281 real, 265 Internet, 7, 9, 40, 202-203, 442, 487 Inventories, 65, 117, 161, 321 Inventory, 65, 72, 88, 90, 93, 119, 133, 143, 155, 168, 321-323, 400, 461-462, 466-467 management of, 119 Inventory control, 90, 323 Inventory management, 65, 93 Investment, 46, 89, 94-96, 99-100, 102, 148, 150, 156, 211, 303, 330, 332, 367, 400, 451 Investment decisions, 156 Investments, 211 Investors, 10, 94, 156, 303, 475 J Japan, 440-441 job applicants, 460 Job performance, 4-5, 247-251, 459-460 Jobs, 206 journals, K Knowledge, 7, 155, 175, 186-187, 189, 192, 198, 206, 212, 223, 235, 250, 255, 284, 295, 305, 331, 335, 438, 467 Korea, 151 L Labor, 119, 143, 153, 213, 295, 320-321, 492-497 labor force, 153 Lags, 18, 21-26, 28, 30, 39-44, 46-47, 51, 57, 69-70, 78, 84, 88, 97, 113, 194, 298, 309, 356, 360, 366, 370, 375, 377, 379, 382, 384, 388-390, 392, 394-396, 404, 415, 418-419, 433 Language, 445 Latin America, 54 Lead time, 9, 100, 128, 367, 444 Leadership, 138, 442, 446 Learning, 8, 113, 323, 445, 456, 462 Legislation, 164 Leverage, 261-263, 267, 276 Life insurance, 200 term, 200 Lifestyle, 32, 440 Limited partnership, 103, 107-108 Loans, 40, 44 Local data, 335 Long-term debt, 10, 278 issuance, 278 Loss, 134, 141, 144 forecasting, 141, 144 income, 144 known, 141 reserve, 141 Lying, 179 M M&A, 233 Management, 3-4, 6, 9-11, 13, 34-35, 40, 65, 89, 93-94, 108, 117, 119, 129, 141, 143, 164, 173, 204, 206, 220-221, 264-265, 323, 349, 354, 382, 400, 416, 422, 436, 437-438, 442-443, 446, 451, 457, 460, 464, 466-469, 475-476 Managers, 1-2, 4-5, 8, 34, 89-90, 119, 164, 200, 262, 265, 400, 443, 447, 450, 453, 460, 464, 466-470 Manufacturers, 123, 143, 155, 449 Manufacturing, 138, 143, 206, 213, 223, 335-336, 376, 451 Manufacturing employment, 335-336 Manufacturing operations, 451 Margin, 100, 447 Market research, 457 Market segment, 422 Market segmentation, 265 Market share, 103, 107-108, 120, 265, 440 Market value, 213, 215 Marketing, 2, 89, 103-104, 108, 129, 206, 265, 417, 419, 441, 443, 457, 461, 466 defined, 466 global, 108 needs and, 89 people, 206, 466 place, 2, 89, 104 Marketing strategies, 461 Marketplace, 107, 446, 469 Markets, 446, 449 Markups, 145 meaning, 270 Measurement, 37, 101, 226, 254, 282, 334-335, 345, 436 measurements, 37, 88, 101, 186, 334, 474 Media, 265, 278 median, 131-132, 148-149 medium, 94, 453, 460-462 selecting, 460 meetings, 54 memorization, 455 Memory, 123 Merchandise exports, 471-473 message, 265 marketing, 265 sales, 265 Mexico, 54, 119 Middle East, 143 Minimum wage, 487 MIS, 467-468 Missing values, 5, 133, 142-143, 343 Monetary risk, 119 Money, 2, 10, 119, 254, 278, 332, 440, 446, 467-468, 487 commodity, 332 demand for, 2, 332 Money market, 487 Music, 19-20, 33, 57 Myths, 457 N National income, 147, 471 Natural disasters, 33 Negative relationship, 176, 189, 236, 279 nervousness, 417 Networking, 445 Neural network, 445-446, 455-457 neural networks, 417, 444-446, 454, 456-457, 462 New products, 470 New-product development, 461 Newspapers, 252 North America, 164 O Objectives, 2, 103, 438, 460, 470 Obligation, 92-93, 469 Obsolescence, 155 Occupancy, 489 Occurrence, 50, 451, 460 Offer, 103, 164 Offset, 57, 100, 213 Oil, 4-5, 95, 143, 151, 401, 445 Operating expenses, 215-216, 278 Operations, 2, 9, 11-12, 53, 117, 119, 164, 217, 419, 445, 451, 460-462, 473 Opportunities, 89, 102, 440, 460, 469-470 Order processing, 155 Organization, 2-4, 6, 9, 32, 119, 334, 437, 439-442, 465-467, 474 Organizations, 2-3, 7, 34, 119, 335, 459-460, 466-468 Outlays, 104 outlines, 61 Output, 3, 11, 24, 40, 58, 75, 115, 123, 136, 159-160, 172-173, 195-196, 201, 218, 221-223, 231-232, 240, 246, 249-250, 252-253, 259-260, 270, 279, 286-287, 292, 304-305, 307-309, 311-313, 319, 337, 341-343, 348-350, 352-353, 371, 374-375, 378, 380-386, 392-393, 396-397, 455-456, 461 potential, 160, 259-260, 279, 343, 349 overhead, 100, 155, 220 Ownership, 103, 108, 262, 265 P Par value, 211-212 Parameter, 38, 84, 86, 88, 256, 300, 311, 363, 380, 382, 384, 386-387, 391, 394, 396, 399, 433 parentheses, 76, 272, 337, 365, 418 Partnering, 441 Partnership, 103, 107-108 payback period, 100 Payment arrangements, 10 Percentage changes, 303, 338 percentages, 37, 130, 132, 146, 149, 263, 440 Performance, 4-5, 35, 64, 138, 153, 247-251, 272-273, 286, 335, 444-446, 455-456, 459-460 Permits, 138, 212-213, 258, 266, 329-330, 335-336, 344 Personal income, 271-272, 301, 304-305, 307, 311, 315, 331, 335-337 Personal lines, 164 Personal services, 145 Pharmaceutical industry, 440 Place, 2-3, 35, 89, 95, 104, 107, 130, 160, 169, 303, 396, 400, 435, 459, 465, 468-469 Plans, 1-2, 7, 40, 95, 100, 119, 129, 155, 159, 164, 204, 238, 335, 443-444, 469 approaches to, business, 1-2, 7, 40, 100, 119, 155, 159, 335, 443-444, 469 reasons for, 204 Policies, 139, 335 Politics, 9, 453-454 Pooling, 443 Population, 15-16, 22-24, 32-33, 103, 107-108, 120, 122-123, 143-144, 153, 178-182, 184, 190-191, 196, 200, 205-206, 213, 220, 224, 237-238, 241, 263, 266, 325-326, 438, 441, 445-446, 470, 487 Portfolio, 44, 367 Model, 44, 367 Portfolio investment, 367 posture, 439, 442 Power, 9, 15, 18, 32, 144-145, 147, 192, 318-319, 325, 327-328, 333, 340, 351, 454, 487 Present value, 144 Price, 7, 16, 44, 46, 92, 100, 122, 143-147, 153, 155, 164, 175-176, 178-179, 181, 183, 186, 188-189, 195-196, 206, 213, 220, 228, 231, 235-236, 238-240, 242, 244-246, 251, 265, 275, 291-292, 295, 320-321, 323, 325-326, 332-334, 339, 382, 401, 409-410, 413, 417, 453, 457, 462, 471, 490-491 defined, 122, 144 price changes, 145 price elasticity, 320 Price changes, 145 Price elasticity, 320 Price elasticity of demand, 320 Price wars, 143 Prices, 4, 32, 103, 119, 123, 127, 138, 143-145, 206, 265, 275, 291, 295, 332, 382-385, 405, 410, 487 demand and, 143 inflation and, 144 minimum, 138, 487 retail, 123, 143, 145, 265, 332 Pricing, 2, 451, 461-462 new product, 451 objectives, value, 451 Principles, 59, 446, 476 Probability, 3, 38, 80, 194, 196, 212, 246, 260, 366, 448-451, 477, 479 Product development, 461 Product life cycle, 33 Product line, 90, 400 Product mix, 155 Product or service, 32, 143 Product quality, 401 Production, 2, 8, 16-17, 32, 40, 89-90, 102, 117, 119, 123, 133, 138, 144, 161, 167, 213, 222, 295, 329, 335, 400, 443, 453, 460-462, 466, 475, 488-489 centralized, 466 national, 17, 102, 138, 335 503 www.downloadslide.net Production costs, 32 Production planning, 461, 466 Productivity, 16, 32, 120, 122-123, 222, 247 Products, 4, 15, 54, 89-90, 93, 95, 143, 164, 189, 222, 323, 331-332, 400-401, 440-442, 449-450, 461, 469-470, 491 consumer products, 401, 449-450 product life cycles, 469 Professionals, 262 Profit, 100, 220, 468 Profits, 155, 206, 217-219, 262, 442, 449 projection, 34, 103, 106, 108, 126, 140, 204, 304, 461 Promotion, 159 Property, 213, 215 Protection, 164 Psychology, 445 Public offering, 103, 108 Purchasing, 2, 32, 144-145, 147, 155, 487 Purchasing power, 32, 144-145, 147, 487 purpose, 9-10, 53, 103, 129, 145, 153, 186, 334, 382, 460, 467-468 general, 145, 382, 468 specific, statement of, 103 Q Quality, 5, 7, 40, 94, 155, 189, 213, 251, 276, 278, 376-383, 401, 440, 445-446, 466 quality control, 5, 40, 213, 376-383 Quantitative approach, 437 Quantity demanded, 320 Quantity discounts, 155 Quantity supplied, 320 Questionnaires, 439 Quota, 466 Quotas, 102 quotations, 92, 409 R Race, 265, 446 Radiation, 107 Rate of return, 422 Rates, 2, 81, 103-104, 119, 159, 206, 212, 220, 264-265, 275, 278-281 gross, 220 reasonable, 104, 220 Rating, 4, 164, 248, 250, 279, 459 Ratios, 38, 131-132, 141-142, 186, 192, 244, 264, 387 Raw materials, 32, 323, 400 Reach, 198, 201, 204, 298, 448 Real estate, 213, 265, 275, 335-336 recommendations, 40, 108 Records, 8, 44, 50, 94, 101, 107-108, 149, 152, 161, 198, 220, 222, 224, 334, 339 Recruiting, 40, 164 Referrals, 103, 107-108 Regression analysis, 1, 6-7, 10, 19, 166, 178, 180-181, 189, 195-196, 201, 204, 208, 218-219, 221-225, 228, 231, 233, 235-276, 278-284, 286-292, 295, 298-300, 304-305, 308-309, 314, 319-321, 333-336, 339, 341-343, 348-351, 366, 445, 459-461, 463, 470-471, 474-475 Relationships, 4, 6, 8-9, 15, 53, 133, 184, 189-190, 197, 200, 204, 236, 247, 255, 264-266, 288, 295, 322-323, 445-446, 462 Replication, 451 reports, 107, 264, 422, 440 Representations, 451 research, 35, 40, 99, 108, 117, 138, 141, 164, 226, 284, 345, 417, 436, 438, 440, 442-444, 453-454, 457, 492-497 planning, 117, 442 primary, 164, 438, 443 Research and development, 40, 492-497 cost of, 492-497 Resources, 7, 9, 400, 453 enterprise resource planning (ERP), Responsibility, 388, 422, 466-467 Restricted, 399, 472 Restrictions, 446 Retail prices, 332 Retail stores, 48-50, 102, 151, 265 Retailers, 143, 332 Retailing, 143, 332 technology and, 143 Revenue, 9, 17, 26, 28-29, 40, 55-56, 95, 103, 107-109, 221, 278, 325, 327, 419, 421, 440, 504 466, 487 Revenues, 26, 32, 95-96, 160, 222, 264, 417, 419-422, 475 revision, 79, 464 Risk, 1, 12, 119, 436 business, 1, 12, 119, 436 personal, Risks, 155 Role, 1-2, 35, 129, 144, 261, 360, 366, 466 managerial, 1, 466 S Salaries, 12, 32, 262, 265 Salary, 12, 155, 262-263, 265, 295, 498 Sales, 2-3, 5, 8-10, 16, 18-19, 30-33, 39, 46-51, 53-55, 62-65, 70, 72, 76, 82-84, 87-88, 95-102, 113, 117, 119, 123, 125-126, 128, 130-140, 144-151, 153-163, 166-170, 175, 186, 195-201, 206, 209-210, 224, 228, 230-231, 235-236, 238-240, 244, 246-247, 252, 254-256, 259-260, 262-265, 269-274, 290-291, 295, 301-305, 307-315, 317, 321-323, 329-332, 339-343, 348-350, 352-353, 355, 388-399, 401, 405, 410, 413-422, 430-431, 434, 440-442, 449-455, 459-460, 462, 464, 469-470, 474-475, 492-497 Sales data, 5, 10, 46-47, 50-51, 53, 55, 95-96, 98, 101-102, 134, 146, 161, 163, 166, 168, 224, 302, 304, 313, 315, 339-341, 348-349, 352, 388, 390, 413, 415, 417-419, 440, 453, 455, 459-460, 475 Sales force, 417 Sales records, 50, 161 Sales tax, 419-422 Salespeople, 127-128, 171-173, 272 Samples, 25-26, 119, 180-181, 192, 241, 300 Sampling, 22, 79, 260, 356, 360, 362, 379, 451, 474 Sampling distribution, 22 Save As dialog box, 58 Saving, 295 increase in, 295 Scanning, 107 scope, 128, 254, 263, 320 SD, 285 SEA, 285 Securities, 99 Securities and Exchange Commission, 99 Security, 144, 264, 442 Segmentation, 265 age, 265 gender, 265 Selection, 5, 7, 31, 33-35, 59, 117, 129, 220, 228, 254, 256, 258-259, 261, 265, 283-284, 336, 356, 363-364, 387, 466 Selection process, 5, 254 Sellers, 332, 469 sentences, 160 SEP, 44, 49-50, 52-54, 109, 153, 161, 163, 225, 344, 350, 409, 421, 423, 428, 454 Service employment, 335-336 Services, 11, 32, 72, 89, 103, 144-145, 164, 367, 400, 422, 440-441, 461 defined, 32, 144 Shareholders, 327-328 shipping, 155 Shopping center, 206 Shopping centers, 206 Shortage, 143 Short-term financing, 400 SIMPLE, 4-5, 7, 23, 32-35, 38, 61, 65-67, 73, 78, 80-84, 86, 88-92, 95, 97-98, 101, 104-106, 121, 123, 126, 141, 145, 166, 169, 175-233, 235-238, 240, 243, 254, 258, 261, 271, 296, 298-299, 303, 307, 310-311, 313, 315, 321-324, 330-331, 333, 340-341, 364-366, 395, 398-399, 401, 413, 444, 446, 449, 452, 454, 464, 472-475 Size, 25, 36-37, 43-44, 52, 79, 182-183, 192, 206, 212-213, 215, 220, 223, 240, 251, 261, 263-264, 268-270, 301, 313, 315, 338, 341, 429, 456, 466, 471, 491 Skills, 10, 48, 143, 446 Slope, 72-73, 81-82, 86, 123, 128, 176-178, 190, 196, 200, 202, 207-211, 213, 219, 221-222, 224, 243, 246, 250, 275, 307, 312-314, 317, 320 Social class, 265 market segmentation, 265 Social Security, 144, 264 Society, 437, 446-447, 468 summary, 446 software, 1, 4, 6-7, 22, 34, 82, 115, 122, 128, 144, 168, 180, 208, 221-222, 224, 238, 240, 245, 262-263, 265, 311, 424, 430, 433, 445, 456, 460, 466-468, 475-476 purchasing, 144 South America, 119 South Korea, 151 Speculation, 442 spreadsheets, Standard deviation, 22, 80, 179-180, 191, 196, 206, 237-238, 241, 245, 251, 253, 266, 272, 296, 298, 315, 337, 380, 464 Standardization, 262 Statistical Abstract of the United States, 43, 119, 204-205, 326 statistics, 7, 11, 21, 59, 76, 103, 107-108, 149, 153, 173, 224, 233, 235, 240, 245, 251, 258, 274-275, 279, 284, 288-289, 292, 299, 314, 336-337, 339, 370, 377, 380, 384, 392, 396, 418, 424, 430, 436, 439, 471, 478, 489 analyzing, 107 Status, Steam, 95 Stock, 119, 127, 138-139, 155, 206, 263, 265, 276, 284, 323, 382-385, 401, 405, 409 Store personnel, 206 Strategic plans, 204 Strategies, 3, 119, 143, 265, 335, 442-443, 446, 461 competitive, 265, 443 Strategy, 61, 155, 204, 355-356, 363, 367, 388, 444, 457 Stress, 417, 446 Students, 202-203, 225, 275, 278, 282, 284, 345, 417, 425, 454 Substitution, 74 Success, 288, 379, 398, 445-446, 450-451, 457, 476 summarizing, 45, 92-93, 113, 166, 213, 330, 421, 424, 426 Supply, 54, 93-94 Support, 5, 11, 28, 35, 183, 223, 279, 288, 443, 467 surveys, 28, 104 system, 54, 79-80, 95, 100, 119, 138, 144, 202-203, 310, 320-321, 334, 445-446, 451, 461, 464, 467-468 T Tables, 11, 23, 25, 186-187, 246-247, 264, 301, 304, 403, 477-486 Tax revenues, 419-422 Taxes, 335 consumption, 335 employment, 335 estate, 335 income, 335 teams, 215-216, 278 Technological advances, 447 Technological environment, 442 Technology, 32, 92, 123, 143-144, 200, 441, 447, 457, 460, 469 Telecommunications, 40, 123, 143 telephone, 202-203, 208, 401, 442 Tenure, 498 Terminology, 195 Territory, 164, 272 Timing, 461-462 tone, 139 Top managers, 470 Total revenue, 278 Trade, 45, 138, 160, 335-336, 386, 471 domestic, 335 Trade-offs, 386 Training, 9, 247, 428, 445, 455-456 Transactions, 335, 436, 469 Transportation, 367-371 trend analysis, 129, 166, 169, 460 Trends, 1, 80, 89, 103, 117, 121-122, 129, 139, 144, 204, 295, 400, 446, 468-469 TRIPS, 220 Trucks, 164, 220 Trust, 10 U Unemployed, 206 Unemployment, 3, 119, 138, 226-228, 264-265, 282-284, 304-305, 345-347, 425 categories of, 284 www.downloadslide.net cyclical, 138, 282, 345, 347, 425 seasonal, 345 Unemployment rate, 3, 138, 226-228, 282-284, 304-305, 345-347, 425 Unions, 104, 107 Unit production, 460 United States, 3, 43, 48, 119, 123-124, 143, 151, 153, 168, 204-205, 262, 325-326, 332, 438, 440 Universities, 119 U.S, 16-17, 49-50, 98, 119, 124, 138, 141, 148, 155, 200, 204, 264, 279, 308, 326-327, 332, 335, 338, 401, 438, 471, 487, 490, 492-497 U.S., 16-17, 49-50, 98, 119, 124, 138, 141, 148, 155, 200, 204, 264, 279, 308, 326-327, 332, 335, 338, 401, 438, 471, 487, 490, 492-497 U.S Census Bureau, 141 U.S Department of Commerce, 124, 335, 471 U.S dollar, 119 U.S economy, 138 U.S Postal Service, 155 Utilities, 279 Utility, 6, 11, 52, 278-279, 401, 490 Workers, workforce, 40, 222-223, 451 World, 7, 9-10, 40, 119, 139, 143-144, 178, 277, 288, 295, 325, 440, 443, 446-447, 455, 457, 467-469 World economy, 468 World Health Organization, 440 World Wide Web, 7, 119, 144, 469 WWW, writing process, 442 WWW, 7, 141, 284-285, 288-289, 487 V Validity, 279 Value, 3, 15, 23-26, 28, 32, 36-42, 44, 46, 62, 64, 67-69, 72-76, 78-82, 84-86, 88-90, 92-97, 99, 101, 115, 121, 123, 127-131, 133-135, 137-138, 140, 142, 144, 147-150, 159, 166-169, 178-179, 181-184, 186, 188-191, 193, 195-197, 204, 206, 209, 211-213, 215, 221-222, 224, 239-240, 243-247, 249-252, 258-264, 267, 278-279, 281, 286, 296, 298, 300, 303, 307, 312-313, 320, 330, 332, 336, 348, 360-362, 365-367, 371, 373-374, 378, 381, 383, 385, 392, 396-397, 402, 448, 451-452, 454, 459, 463-464, 471-474, 483 building, 138, 212-213, 336, 360, 366-367 defined, 32, 80, 115, 131, 144, 300, 366 market value, 213, 215 Variability, 17, 79, 107, 119, 121-122, 134, 142, 159, 161, 182, 185-187, 189-190, 193-195, 198, 206, 222-223, 244-245, 247, 270, 296, 298-299, 304, 307, 314-315, 317, 319, 336 Variable costs, 220 Variables, 1, 3, 8, 11, 17-18, 32, 34, 53, 133, 143, 147, 166, 175, 178, 189, 197-198, 204, 206, 209-211, 217, 224-225, 228, 235-242, 244-247, 249-266, 268-272, 275-276, 278-279, 282, 284-286, 288, 290-292, 295-296, 301, 303, 306-307, 310, 314-315, 317-318, 320-325, 327, 329-337, 339, 341, 344-345, 347-351, 355, 360-361, 400, 417, 425, 430, 437, 444-445, 448, 451, 453, 459-460, 462, 470-471, 474-475, 486, 487, 491 nonlinear relationships between, 288 Variance, 26, 41-42, 51, 184-188, 192-193, 196-197, 201, 221-222, 241, 246, 250-253, 260, 262, 267, 269-270, 280-281, 286-287, 296, 305-306, 308-310, 314-315, 317, 319, 322, 334, 337, 341-343, 348, 350, 365, 402, 426, 453 Venture capitalists, 156 videos, 148 Visualize, 15, 61, 449 Volume, 8-9, 123, 144-145, 152-153, 159-160, 165-166, 175-176, 186, 195, 197, 225-227, 235-236, 238, 240, 244, 246, 270, 282, 345, 347, 401, 425-427 Volumes, 3, 8, 153, 156, 224, 242 W Wall Street Journal, 159, 264 War, 331 Water, 18, 100, 318-319, 325, 327-328, 351 Wealth, 446 Weather conditions, 18, 222 Web, 7, 9, 108-109, 119, 141, 143-144, 427-428, 469 websites, Welfare programs, 144 Women, 144, 247, 270, 417, 419 Won, 274 word of mouth, 107 Work, 3-5, 9, 35, 37, 101-102, 107, 112, 117, 120, 125, 134, 161, 208, 212, 221, 251, 254, 284, 349, 355, 365, 372, 399, 416, 438, 446, 454-456, 474 505 ... Freedom 2 2 3 3 3 3 3 4 4 4 4 4 5 5 29 28 28 28 28 28 27 27 27 27 27 27 27 27 27 27 26 26 26 26 26 26 26 26 26 26 25 25 25 25 25 24 R2 0000 4570 6370 0880 3 020 3870 8948 4790 5690 6410 6 420 6570... 52 30 58 59 52 56 49 63 61 39 62 78 10 19 27 31 64 81 42 67 48 64 57 10 48 96 75 12 47 20 73 98 27 59 23 90 34 16 32 94 22 .1 22 .5 23 .1 24 .0 22 .6 21 .7 23 .8 22 .0 22 .4 22 .6 21 .1 22 .5 22 .2 24.8 22 .6... 3. 92 3.69 3.69 905 1, 028 927 997 919 822 988 1,033 1, 028 909 969 921 999 923 936 739 868 859 8 62 971 1,003 1, 022 8 92 1,004 990 876 24 6 24 4 25 3 25 8 26 3 25 5 24 1 24 4 25 3 24 6 25 8 24 4 26 9 26 2 25 6 24 7