conversations.from: human
conversations.value: Do you know a description for a book titled Business Statistics about Title, Business, Statistics with the genre: Business & Money, Management & Leadership?
conversations.from: assistant
conversations.value: Title: Business Statistics
  
  Publisher: Cengage learning
  
  Publication Date: 2010
  
  Binding: Paperback
conversations.from: human
conversations.value: Do you know the first four paragraphs for this book?
conversations.from: assistant
conversations.value: Business Statistics
  In addition to considering the effect of cyclical fluctuations and forecasting such fluctuations, specific causative variables that have influenced the time series values historically should also be studied. Regression and correlation analysis (see Chapters 14 and ) are particularly applicable for such studies as the relationship between pricing strategy and sales volume. Beyond the historical analyses, the possible implications of new products and of changes in the marketing environment are also areas of required attention.
  The moving average can be used to forecast the data value for the next (forthcoming) period in the time series, but not for periods that are farther in the future. It is an appropriate method of forecasting when there is no trend, cyclical or seasonal influence on the data, which of course is an unlikely situation. The procedure serves simply to average out the irregular component in the recent time series data.8.
  The following algebraic model serves to represent how the exponentially decreasing weights are determined. Specifically, where α is a smoothing constant discussed below, the most recent value of the time series is weighted by α, the next most recent value is weighted by α (1 – α), the next value by α (1 – α)2, and so forth, and all the weighted values are then summed to determine the forecast:
  Although the above formula serves to present the rationale of exponential smoothing, its use is quite cumbersome. A simplified procedure that requires an initial "seed" forecast but does not require the determination of weights is generally used instead. The formula for determining the forecast by the simplified method of exponential smoothing is
conversations.from: human
conversations.value: Add another sentence about Problem.
conversations.from: assistant
conversations.value: Business Statistics
  In addition to considering the effect of cyclical fluctuations and forecasting such fluctuations, specific causative variables that have influenced the time series values historically should also be studied. Regression and correlation analysis (see Chapters 14 and ) are particularly applicable for such studies as the relationship between pricing strategy and sales volume. Beyond the historical analyses, the possible implications of new products and of changes in the marketing environment are also areas of required attention. 
  The moving average can be used to forecast the data value for the next (forthcoming) period in the time series, but not for periods that are farther in the future. It is an appropriate method of forecasting when there is no trend, cyclical or seasonal influence on the data, which of course is an unlikely situation. The procedure serves simply to average out the irregular component in the recent time series data. See Problem 16. 8. 
  The following algebraic model serves to represent how the exponentially decreasing weights are determined. Specifically, where α is a smoothing constant discussed below, the most recent value of the time series is weighted by α, the next most recent value is weighted by α (1 – α), the next value by α (1 – α)2, and so forth, and all the weighted values are then summed to determine the forecast:
  Although the above formula serves to present the rationale of exponential smoothing, its use is quite cumbersome. A simplified procedure that requires an initial "seed" forecast but does not require the determination of weights is generally used instead. The formula for determining the forecast by the simplified method of exponential smoothing is
conversations.from: human
conversations.value: Do you know four more paragraphs?
conversations.from: assistant
conversations.value: Problem 17.6 illustrates the use of the Wilcoxon test for testing the difference between two medians data that have been collected as paired observations.
  Problem 17.7 illustrates use of the Kruskal–Wallis test for testing the null hypothesis that several populations have the same median.
  Whereas Excel has no direct method for nonparametric procedures, Minitab includes a variety of such techniques. Solved Problems 17.8 through 17.10 illustrate the use of Minitab for the one-sample Wilcoxon test, the two-sample Mann–Whitney test for independence, and the Kruskal–Wallis test for several independent samples.
  Therefore, using the 5 percent level of significance, we cannot reject the null hypothesis that the sequence of women and men occurred randomly.
conversations.from: human
conversations.value: Delete the last sentence.
conversations.from: assistant
conversations.value: Problem 17.6 illustrates the use of the Wilcoxon test for testing the difference between two medians data that have been collected as paired observations.
  Problem 17.7 illustrates use of the Kruskal–Wallis test for testing the null hypothesis that several populations have the same median.
  Whereas Excel has no direct method for nonparametric procedures, Minitab includes a variety of such techniques. Solved Problems 17.8 through 17.10 illustrate the use of Minitab for the one-sample Wilcoxon test, the two-sample Mann–Whitney test for independence, and the Kruskal–Wallis test for several independent samples.
conversations.from: human
conversations.value: Do you know the last four paragraphs?
conversations.from: assistant
conversations.value: A stable process can be improved only by changing the design of the process. Attempts to make adjustments to a stable process, which is called tampering, results in more variation in the quality of the output.
  Control charts are used to detect the occurrence of assignable causes affecting the quality of process output.
  Figure 19-3 presents the required chart. The centerline and control limits correspond to those calculated in Problem 19.12. As indicated in the output message given just below the chart, there were two sample means (for samples #8 and #9) that were beyond 3 sigmas from the centerline. From Table 19.3 we can observe that the two values were 14.84 oz and 14.80 oz, respectively. Thus, there is strong evidence of assignable-cause variation associated with these sample data.
  Figure 19-4 presents the required chart. The centerline and control limits correspond to those calculated in Problem 19.14. As indicated in the output message given just below the chart, there were two sample means (for samples #8 and #9) that were beyond 3 sigmas from the centerline. From Table 19.3 we can observe that the two values were 14.84 oz and 14.80 oz, respectively. Thus, there is strong evidence of assignable-cause variation associated with these sample data.