Questions and Answers of Forecasting Predictive Analytics

(e) Quarterly production of bricks (in millions of units) at Portland, Australia (March 1956{September 1994).
(d) Monthly total of accidental deaths in the United States(January 1973{December 1978).
(c) Number of lynx trapped annually in the McKenzie River district of northwest Canada (1821{1934).
(b) Daily morning temperature of a cow for 75 days.
(a) Monthly total of people on unemployed bene¯ts in Aus-tralia (January 1956{July 1992).
2.3 For each of the following series on the web page, make a graph of the data (using a computer package), describe the main features and, if transforming seems appropriate, do so and describe the
(d) Weekly electricity consumption for your local area over the past 10 years.\
(c) Daily sales at a fast-food store for six months.
(b) Hourly pulse rate of a person for one week.
(a) Monthly retail sales of computer disks for the past 10 years at your local store.
For each of the following series, what sort of time patterns would you expect to see?
(b) Make a time plot of the data. Is there any time pattern in the temperature readings?
(a) What is your best estimate of the average temperature in June 1995?
2.1 Table 2-20 gives average monthly temperatures in Paris.
Monthly milk production per cow over 14 years (Source: Cryer, 1986). The second graph shows the data adjusted for the length of month. This yields a simpler pattern enabling better forecasts and
Transformations of the electricity production data. Of these, either a square root, cube root, or a log transformation could be used to stabilize the variation to form a series which has variation
The electricity production data shown in Figure 1-2a (p. 7)exhibit a strong trend in addition to the monthly seasonality.The beer data in Figure 2-1 show no trend.Many data series include
4. A trend (T) pattern exists when there is a long-term increase or trend decrease in the data. The sales of many companies, the gross national product (GNP), and many other business or economic
3. A cyclical (C) pattern exists when the data exhibit rises and cyclical falls that are not of a ¯xed period. For economic series, these are usually due to economic °uctuations such as those
2. A seasonal (S) pattern exists when a series is in°uenced by seasonal seasonal factors (e.g., the quarter of the year, the month, or day of the week). Sales of products such as soft drinks, ice
1. A horizontal (H) pattern exists when the data values °uctuate horizontal around a constant mean. (Such a series is called \stationary"in its mean.) A product whose sales do not increase or
In the chapter example, we used a very small sample taken from two Usenet newsgroups (autos and electronics). Use the much larger data set that includes many postings from each of the two groups and
Trivago is a German technology company that is essentially a hotel price comparison website. It claims to be the world’s largest online hotel search site. As part of the information on their
A twenty-foot equivalent unit (TEU) is a standard measurement of volume in container shipping. The majority of containers are either 20 or 40 feet in length. A 20-foot container is 1 TEU; a 40-foot
Wine (in this case red wine) has been graded for many years by experts who actually taste a sample of the wine, examine its color and aroma, and assign a grade (in our case, high quality or lower
The Bank Marketing Data includes actual information for a direct marketing effort by a bank. We will attempt to construct a model with just a few of the available attributes. We are interested in
The Boston Housing data includes information from the 1970 Census for the city of Boston and surrounding area. Note that there are two variables representing value; one is “Medv” which is a
The Credit Card Fraud data is a small version (comprised of 12,240 records) of a much larger data set (containing 248,807 records); it is made up of 2013 European transactions. It is a very
Explain how the predictions made using analytics are somewhat different than those from traditional forecasting models. Are the data used different? Are the types of predictions different?
Suppose that you have been asked to recommend a forecasting technique that would be appropriate to prepare a forecast, given the following situational characteristics:a. You have 10 years of
What are the two main things to consider when selecting a forecast method? Why?
Describe the nine-step forecast process presented in the chapter.
What two groups must communicate well in order for the forecast process to be effective? Explain why.
Explain the process of going from raw data to actions based on a forecast.
Discuss how forecasting has moved from purely judgmental methods to highly complex methods.
Why mine text at all? Isn’t language so complex that little useful insight can be gained through machine learning methods?
In previous chapters, we used data mining diagnostic statistics such as confusion matrices and lift charts to evaluate models. Are these types of statistics useful in text mining?
In the chapter, the two different text mining approaches were both used to mine text: “bag of words” analysis and “natural language processing.” What is the significant difference between the
What is meant by “natural language processing"?
What is meant by “bag of words” analysis?
What is meant by the term dimension reduction?
What method explained in this chapter is a concept in analytics of approximating the sampling distribution of a statistic by repeatedly sampling from a given sample of size n.
“Boosting” may only be used with Naïve Bayes algorithms. Is this a true statement?
“Bagging” is a short form of “bootstrap aggregation.” Explain conceptually how bagging is accomplished in software. Why might bagging increase the predictive capacity of an underlying
What are “weak learners” and are they used by bagging or boosting? Explain the concept of a weak learner.
Explain the fundamental difference between “boosting” and “bagging.”
What is the basic concept underlying an “ensemble” model?
Explain what is meant by Bayes’ theorem as used in the Naive Bayes model. Lift Chart (Validation Dataset) Decile-wise Lift Chart (Validation Dataset) 10- 250 200- 150 100- 2- 50 of 2000 12 3 4 5678
Calculate the classification error rate for the following confusion matrix. Comment on the pattern of misclassifications. How much better did this data mining technique do as compared to a naive
A data mining algorithm has been applied to a transaction dataset and has classified 88 records as fraudulent (30 correctly so) and 952 as nonfraudulent (920 correctly so). Which of the following
The lift chart and the confusion matrix are both standard diagnostic tools used to evaluate a data mining algorithm. Don’t the two measures show-display the same information? Explain any
Data has the characteristic of “non-rivalry.” What is non-rivalry and why is it important to realize that data has this characteristic?
In the Universal Bank data in this chapter only 10% of the records represented customers that had taken out a personal loan (the target variable). If we were to score a new customer based upon the
Show the computation for the misclassification rate of this confusion matrix.Confusion MatrixActual\Predicted01097020128
The validation data set confusion matrix for the Universal Bank data classification model is shown.How many records were in the validation data set? How many of the records were correctly classified
Some data mining algorithms work so “well” that they have a tendency to overfit the training data. What does the term “overfit” mean and what difficulties does overlooking it cause for the
In the Universal Bank classification model estimated with XLMiner the software produced the validation data set lift chart shown.How is the naïve model displayed in the diagram? What does the other
How do “structured” and “unstructured” data differ? Which is the more prevalent form of data?
The first step in data mining procedures according to SAS and IBM/SPSS is to “sample” the data. Sampling here refers to dividing the data available for analysis into at least two parts: a
A classification model's misclassification rate on the validation set is a better measure of the model's predictive ability on new (unseen) data than its misclassification rate on the training set.
The data below show retail sales at hardware stores in the United States monthly between January 1992 and December 2005. The data are in millions of dollars and are not seasonally
The following table contains quarterly data on Upper Midwest car sales (CS) in thousands for 1996 Q1 through 2016 Q4:
A regional supplier of jet fuel is interested in forecasting its sales. These sales data are shown for the period from 2002Q1 to 2017Q4 (data in billions of gallons):
The Bechtal Tire Company (BTC) is a supplier of automotive tires for U.S. car companies. BTC has hired you to analyze its sales. For this problem, do all the work in Forecast X™ and be sure to
Carl Lipke is the marketing VP for a propane gas distributor. He would like to have a forecast of sales on a quarterly basis, and he has asked you to prepare a time-series decomposition model. The
A tanning parlor located in a major shopping center near a large New England city has the following history of customers over the last four years (data are in hundreds of customers and months are the
How do true cycles and the cycles typically found in business data differ?
How is the long-term trend determined for a time-series decomposition model?
What is the difference between seasonal factors and seasonal indices?
Discuss the trend, the seasonal, and the cyclical components.
Explain the similarity between how time-series decomposition and Winters’ exponential smoothing deal with seasonality.
Your company produces a favorite summertime food product, and you have been placed in charge of forecasting shipments of this product. The historical data below represent your company’s past
Develop an example to show how to set up a data file to apply regression analysis to combine forecasts.
Outline the process for combining forecast models explained in this appendix.
AmeriPlas, Inc., produces 20-ounce plastic drinking cups that are embossed with the names of prominent beers and soft drinks. The sales data
a. Construct a time-series graph of the sales data for HeathCo’s line of skiwear. Does there appear to be a seasonal pattern in the sales data? Explain why you think the results are as you have
In Chapter 4, you worked with data on sales for a line of skiwear that is produced by HeathCo Industries. Barbara Lynch, product manager for the skiwear, has the responsibility of providing forecasts
The following inventory pattern has been observed in the Zahm Corporation over 12
Explain things that should be considered when selecting independent variables for a multiple regression model that will be used to make a forecast.
Describe some ways dummy variables can be useful in regression models.
Explain what is meant by a "dummy variable."
Describe how a regression plane differs from a regression line.
Explain the five-step process for evaluating a multiple regression model.
Explain the difference between bivariate (simple) regression and multiple regression.
Fifteen mid-western and mountain states have united in an effort to promote and forecast tourism. One aspect of their work has been related to the dollar amount spent per year on domestic travel
Carolina Wood Products, Inc., a major manufacturer of household furniture, is interested in predicting expenditures on furniture (FURN) for the entire United States. It has the following data by
Dick Staples, has mentioned to Barbara Lynch that he has found both the unemployment rate and the level of income to be useful predictors.a. Suppose that Ms. Lynch provides you with the following
Barbara Lynch is the product manager for a line of skiwear produced by HeathCo Industries and privately branded for sale under several different names, including Northern Slopes and Jacque Monri. A
Mid-Valley Travel Agency (MVTA) has offices in 12 cities. The company believes that its monthly airline bookings are related to the mean income in those cities and has collected the following
Nelson Industries manufactures a part for a type of aircraft engine that is becoming obsolete. The sales history for the last 10 years is as
Explain what is meant by heteroscedasticity.
Explain the difference between the most common kind of correlation (the Pearson product moment correlation, discussed in Chapter 2) and serial correlation.
Explain the difference between a simple trend model and a causal model.
In this chapter, you learned four steps that should be used to evaluate a regression model. What is the first step and why is it so important? Explain the other three steps, indicating what you
How can seasonal data be forecast with a simple bivariate linear regression model? Explain the deseasonalize-forecast-reseasonalize process. How does the material in this chapter suggest that
Why is it useful to look at data in a graph as well as in a table? What is the main advantage of seeing a graph of the data?For most people looking a long tables of numbers provides little useful
What is an "event model?" Give some examples of when such a model might be useful.
What are some methods that might be useful to forecast "new products" for which there are few historical observations?
What data pattern would suggest the use of a Winters' exponential smoothing model?
When is a Holt's exponential smoothing model most appropriate?

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