Questions and Answers of Introduction To Management Science 13th

What is the communication objective of descriptive analytics?
Why is it often important to test a model before it is used?
Reconsider Problem 2.9. Now using the data on the Clean Data worksheet tab, explore the data visually as follows. a. Generate a scatter plot with College GPA on the vertical axis and High School
What is the ultimate goal of descriptive analytics?
What are the advantages and disadvantages of retrospective testing?
Reconsider Problem 2.9. Using all the data (unpartitioned) on the Clean Data worksheet tab rescaled with standardization, apply the KNN algorithm to this problem with k = 7 and the data standardized.
Why is it often important to develop a well-documented system for applying the models?
Reconsider Problem 2.9. Using the data on the Clean Data worksheet tab, partition the historical records into a training partition (60 percent of the records) and a validation partition (the
Friendly Bank is very active with making loans to deserving people in the local community. However, the bank does need to carefully evaluate each loan to make sure that the recipient of the loan will
Reconsider Problem 2.16. Now using the data on the Clean Data worksheet tab, explore the data quantitatively by evaluating the summary statistics as follows. a. For each column of numerical data
Why is this system usually computer based?
Why is it important for the study team to participate in launching the implementation phase?
Reconsider Problem 2.16. Now using the data on the Clean Data worksheet tab, apply sorting and filtering on the dataset to explore the data as follows. a. Sort the data by Annual Income. Comment
What are the several steps of the implementation phase?
Reconsider Problem 2.16. Now using the data on the Clean Data worksheet tab, apply sorting and filtering on the dataset as follows. a. For each column of numerical data (Annual Income, Credit
Reconsider Problem 2.16. Now using the data on the Clean Data worksheet tab, explore the data visually as follows. a. Generate a scatter plot with late payments on the vertical axis and annual
Reconsider Problem 2.16. Using the data on the Clean Data worksheet tab, partition the historical records into a training partition (60 percent of the records) and a validation partition (the
Reconsider Problem 2.16. Using all the data (unpartitioned) on the Clean Data worksheet tab rescaled with standardization, apply the KNN algorithm with k = 10 to this problem to classify each of the
Now that Jennifer has had 100 customers, what does this allow her to do to allow for refining and/or testing a model that was not feasible earlier when she had data from just 16 previous customers?
What is the goal of affinity analysis?
Why is it so important for Evergreen Solar to only pursue new sales leads that are likely to have solar installed?
How are the nearest neighbors determined in the KNN algorithm?
What is the rule that the classification tree algorithm uses when selecting the next region to split into new smaller regions?
As discussed throughout Chapter 3, the owner of Evergreen Solar (Jennifer) has been exploring different ways of performing predictive analytics in order to better predict whether any new sales lead
What do the Gini Index and the Overall Gini Index measure?
How can numerical predictor variables be converted to categorical predictor variables?
How can data partitioning be used to refine a classification tree model?
What are some examples of modern online retailers that make heavy use of recommendation systems?
What are the two primary indicators of whether a household will have solar installed?
What is the main assumption behind the naïve Bayes algorithm?
How can data partitioning be used to refine a KNN model?
What is the connection between affinity analysis and recommendation systems?
How is a peak sun hour defined?
Reconsider Figure 3.5, which shows the application of the KNN algorithm with k = 3 to the new sales lead (the predictor record) for the case study while using standardized values of the predictor
When is it important to rescale the data?
Figure 3.19 shows the final classification tree for the case study. For each of the following new sales leads (predictor records), read down this classification tree to obtain the prediction
What does it mean to normalize the data?
What is the danger of continuing the splitting step portion of the classification tree algorithm too long?
What do the Regression Index and the Overall Regression Index measure?
What is the tradeoff in choosing different values of k in KNN algorithm?
What is the main difference between multiple linear regression and logistic regression?
What is the new kind of outcome that requires switching from the classification tree algorithm to the regression tree algorithm?
What does it mean to standardize the data?
After using data partitioning to test the various models of interest, what was Jennifer’s conclusion about which model she would continue using?
What type of relationship between the predictor variables and the outcome is assumed with multiple linear regression?
What are yes-or-no outcomes? What is an example for the case study?
How is the regression tree algorithm different from the classification tree algorithm?
Which algorithms are local algorithms and which are global algorithms?
What are numerical outcomes? What is an example for the case study?
Compared to the KNN algorithm, what is the main advantage of the classification tree or regression tree algorithms?
How can Analytic Solver be used to solve models based on the KNN algorithm?
Compared to the KNN algorithm, what is a disadvantage of the classification tree or regression tree algorithms?
What are the pros of using the KNN algorithm?
What are the cons of using the KNN algorithm?
Reconsider Problem 3.17. Partition the historical records into a training partition (60 percent of the 800 records) and a validation partition (the remaining 40 percent of the 800 records). a.
Reconsider Problem 3.17. The historical data for annual income and credit score have each now been binned into five categories (Low, Medium Low, Medium, Medium High, and High) on the Binned Data
Reconsider Problem 3.17. a. Apply multiple linear regression to determine the prediction equation for the number of late payments as a function of the annual income and credit score. b. Use
Reconsider Problem 3.17. a. Apply logistic regression so as to classify applicants as to whether they are likely to default (defined as greater than a 10 percent chance of default). Determine
How does the Computer Club Warehouse (CCW) operate?
What does a seasonal factor measure?
What kind of variable is the next value that will occur in a time series?
What is causal forecasting?
Statistical forecasting methods cannot be used under what circumstances?
What is the last-value forecasting method and when might it be a reasonable method to use?
What is the averaging forecasting method and when might it be a reasonable method to use?
What are the consequences of not having enough agents on duty in the CCW call center? Of having too many?
What is the formula for calculating the seasonally adjusted call volume from the actual call volume and the seasonal factor?
What is the goal of time series forecasting methods?
When applying causal forecasting to the CCW problem, what is the dependent variable and what is the independent variable?
Is judgmental forecasting used only when statistical forecasting methods cannot be used?
What is the moving-average forecasting method and when might it be a reasonable method to use?
What is Lydia’s current major frustration?
What is the formula for calculating the forecast of the actual call volume from the seasonal factor and the seasonally adjusted forecast?
Is the probability distribution of CCW’s average daily call volume the same for every quarter?
When doing causal forecasting with a single independent variable, what does linear regression involve?
How does the jury of executive opinion method differ from the manager’s opinion method?
How does the exponential smoothing forecasting method differ from the moving-average forecasting method?
What is CCW’s 25 percent rule?
Why is the last-value forecasting method sometimes called the naïve method?
What is the explanation for why the average forecasting errors were higher for the other time series forecasting methods than for the supposedly less powerful last-value method?
What is the form of the equation for a linear regression line with a single independent variable? With more than one independent variable?
How does the salesforce composite method begin?
How does exponential smoothing with trend differ from the exponential smoothing forecasting method?
What is MAD?
Why did the averaging forecasting method not perform very well on the case study?
What is the distinction between a stable time series and an unstable time series?
What is the name of the method for obtaining the value of the constants in the regression equation for a linear regression line?
When is a market survey particularly helpful?
How does the linear regression forecasting method obtain forecasts?
What is MSE?
What is the rationale for replacing the averaging forecasting method by the moving-average forecasting method?
What is the consultant’s recommendation regarding what should be forecasted instead of call volumes to begin the forecasting process?
How does the MAD value for CCW’s new forecasting procedure compare with that for the old procedure that used the 25 percent rule?
When might the Delphi method be used?
What are the two main measures of the accuracy of a forecasting method?
How does the exponential smoothing forecasting method modify the moving-average forecasting method?
What are the major components of CCW’s total sales?
With exponential smoothing, when is a small value of the smoothing constant appropriate? A larger value?
What is the formula for obtaining the next forecast with exponential smoothing? What is added to this formula when using exponential smoothing with trend?
What does the marketing manager say is the one big factor that drives CCW’s total sales up or down?

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