Question: Are the following data time series? If

> The United Nations Development Programme (UNDP) uses the Human Development Index (HDI) in an attempt to summarize in one number the progress in health, education, and economics of a country. The mean years of schooling is positively associated with HDI.

> The United Nations Development Programme (UNDP) collects data in the developing world to help countries solve global and national development challenges. In the UNDP annual Human Development Report, you can find data on over 100 variables for each of 197

> Even with campaigns to reduce smoking, Americans still consume more than four packs of cigarettes per month per adult (libraries.ucsd.edu/ssds/pub/ CTS/tobacco/sales). The Centers for Disease Control and Prevention track cigarette smoking in the United S

> Weddings are one of the fastest growing businesses; about $40 billion is spent on weddings in the United States each year. But demographics may be changing, and this could affect wedding retailers’ marketing plans. Is there evidence tha

> For the Apple prices of Exercise 5, find the lag1 version of the prices.

> Orange growers know that the larger an orange the higher the price it will bring. But as the number of oranges on a tree increases, the fruit tends to be smaller. Here’s a table of that relationship. Create a model for this relationship

> The Organization for Economic Cooperation and Development (OECD) is an organization comprised of thirty countries. To belong, a country must support the principles of representative democracy and a free market economy. How have these countries grown in t

> For the regression model in Exercise 8, the leverage values look like this: The movie with the highest leverage of 0.219 is Walt Disney’s John Carter, which grossed $66M but had a budget of $300M. If the budget for John Carter had bee

> Here is the scatterplot of the variables in Exercise 7 with regression lines added for each kind of movie: The regression model is: a) Write out the regression model. b) In this regression, the variable Budget*R Rating is an interaction term. How wou

> Are R rated movies as profitable as those rated PG-13? Here’s scatterplot of USGross ($M) vs. Budget ($M) for PG-13 (green) and R (purple) rated movies a) How would you code the indicator variable? (Use PG-13 as the base level.) b) H

> A marketing manager has developed a regression model to predict quarterly sales of his company’s mid-weight microfiber jackets based on price and amount spent on advertising. An intern suggests that he include indicator (dummy) variables for each quarter

> For each of the following, show how you would code dummy (indicator) variables to include in a regression model. a) Type of residence (Apartment, Condominium, Townhouse, Single family home) b) Employment status (Full-time, Part-time, Unemployed)

> Here is the regression for Exercise 3 with an indicator variable: a) Write out the regression model. b) In this regression, the variable R Rating is an indicator variable that is 1 for movies that have an R rating. How would you interpret the coeffici

> Do movies of different types have different rates of return on their budgets? Here’s a scatterplot of Gross Revenue in US ($M) vs. Budget ($M) for recent movies whose MPAA Rating is either PG (blue) or R (red): a) Why might a research

> A marketing manager has developed a regression model to predict quarterly sales of his company’s down jackets based on price and amount spent on advertising. An intern suggests that he include an indicator (dummy) variable for the Fall quarter. a) How wo

> For the Gas prices of Exercise 6, the actual value for January 2007 was 2.321. Find the absolute percentage error of your forecast.

> If the VIF for Networth in the regression of Exercise 11 was 20.83, what would the R2 be from the regression of Networth on Age, Income, and Past Spending?

> The analyst from Exercise 11, worried about collinearity, regresses Age against Past Spending, Income, and Networth. The output shows: What is the VIF for Age? Response Variable: Age R? = 98.75% Adjusted R? = 98.74% s = 2.112 with 908 – 4 = 904 deg

> The analyst in Exercise 11 fits the model with the four predictor variables. The regression output shows: // a) How many observations were used in the regression? b) What might you do next? c) Is it clear that Income is more important to predicting Spe

> An analyst wants to build a regression model to predict spending from the following four predictor variables: a) How many observations were used in the regression? b) What might you do next? c) Is it clear that Income is more important to predicting Sp

> For the same regression as in Exercise 9, the Cook’s Distances look like this: The outlier, once again, is John Carter, whose budget was more than $200M more than its gross revenue in the U.S. Setting this movie aside and rerunning th

> For each of the following, show how you would code dummy (or indicator) variables to include in a regression model. a) Company unionization status (Unionized, No Union) b) Gender (Female, Male) c) Account Status (Paid on time, Past Due) d) Political part

> In the regression model of Exercise 3, a) What is the R2 for this regression? What does it mean? b) Why is the “Adjusted R Square” in the table different from the “R Square”?

> a) What is the null hypothesis tested for the coefficient of Run Time in the regression of Exercise 3? b) What is the t-statistic corresponding to this test? c) Why is this t-statistic negative? d) What is the P-value corresponding to this t-statistic? e

> In the regression output for the movies of Exercise 3, a) What is the null hypothesis tested for the coefficient of Stars in this table? b) What is the t-statistic corresponding to this test? c) What is the P-value corresponding to this t-statistic? d) C

> For the movies regression, here is a histogram of the residuals. What does it tell us about these assumptions and conditions? a) Linearity Condition b) Nearly Normal Condition c) Equal Spread Condition 50 40 30 20 10 -150 -25 100 225 Residuals (U)

> For the Apple prices smoothed in Exercise 5, the actual value for January 2007 was 1.034. Find the absolute percentage error of your forecast.

> For the movies examined in Exercise 4, here is a scatterplot of USGross vs. Budget: What (if anything) does this scatterplot tell us about the following assumptions and conditions for the regression? a) Linearity Condition b) Equal Spread Condition c)

> A middle manager at an entertainment company, upon seeing the analysis of Exercise 3, concludes that the longer you make a movie, the less money it will make. He argues that his company’s films should all be cut by 30 minutes to improve their gross. Expl

> What can predict how much a motion picture will make? We have data on a number of recent releases that includes the USGross (in $M), the Budget ($M), the Run Time (minutes), and the average number of Stars awarded by reviewers. The first several entries

> A candy maker surveyed chocolate bars available in a local supermarket and found the following least squares regression model: a) The hand-crafted chocolate she makes has 15 g of fat and 20 g of sugar. How many calories does the model predict for a ser

> A study of homes looking at the relationship between Age of a home and Price produced the following scatterplot. A regression was fit to the data as shown below. On the basis of this plot, would you advise using this regression? Explain. 350,000- 3

> A scatterplot of Salary against Years Experience for some employees, and the scatterplot of residuals against predicted Salary from the regression line are shown in the figures. On the basis of these plots, would you recommend a re-expression of either S

> The regression of Total Revenue on Total Expenses for the concerts of Exercise 13 gives the following model: a) The Durbin-Watson statistic for this analysis is 0.73. Consult Table D in Appendix B and complete the test at α = 0.05. b) Wha

> The manager of the concert production company considered in earlier exercises considers the regression of Total Revenue on Ticket Sales (see Exercise 4) and computes the Durbin-Watson statistic, obtaining a value of 0.51. a) Consult Table D in Appendix B

> A company fits a regression to predict monthly Orders over a period of 48 months. The Durbin-Watson statistic on the residuals is 0.875. a) At a = 0.01, using k = 1 and n = 50, what are the values of dL and dU? b) Is there evidence of positive autocorrel

> A beverage company specializing in sales of champagne reports four years of quarterly sales as follows (in millions of $): The regression equation is Predicted Sales = 14.15 + 4.87 Quarter. a) Find the residuals. b) Plot the residuals against Quarter.

> Here are data on the monthly price of Delicious apples and gas, which are both components of the Consumer Price Index. The timeplot shows the years 2006–2009 for apples; the data table shows just 2006, for both. For the Gas prices: (D

> Here is another part of the regression output for the movies in Exercise 3: a) Using the values from the table, show how the value of R2 could be computed. Don’t try to do the calculation, just show what is computed. b) What is the F-

> A house in the upstate New York area from which the chapter data was drawn has 2 bedrooms and 1000 square feet of living area. Using the multiple regression model found in the chapter, a) Find the price that this model estimates. b) The house just sold

> The bookstore in Exercise 5 decides to have a gala event in an attempt to drum up business. They hire 100 employees for the day and bring in a total of $42,000. a) Find the regression line predicting Sales from Number of people working with the new point

> The production company of Exercise 7 offers advanced sales to “Frequent Buyers” through its website. Here’s a relevant scatterplot: One performer refused to permit advanced sales. What effect has th

> A regression of Total Revenue on Ticket Sales by the concert production company of Exercises 2 and 4 finds the model a) Management is considering adding a stadium-style venue that would seat 10,000. What does this model predict that revenue would be if

> Here are prices for the external disk drives we saw in Chapter 15, Exercise 10: The least squares line is The assumptions and conditions for regression are met. a) Disk drives keep growing in capacity. Some tech experts now talk about Petabyte (PB

> Here are the data from the small bookstore we saw in Chapter 15, Exercise 9. The regression line is: and we can assume that the assumptions and conditions for regression are met. Calculations with technology find that a) Find the predicted sales on

> The concert production company of Exercise 2 made a second scatterplot, this time relating Total Revenue to Ticket a) Describe the relationship between Ticket Sales and Total Revenue. b) How are the results for the two venues similar? c) How are they d

> The analyst in Exercise 1 tried fitting the regression line to each market segment separately and found the following: What does this say about her concern in Exercise 1? Was she justified in worrying that the overall model might not accurately summ

> Here is a table of values from the U.S. Bureau of Labor Statistics: (Data in BLS output) For the series of Output per hour of labor: a) Make a time series plot. b) Describe the trend component. (Remember: Direction, Form, and Strength.) c) Is there evi

> Are the following data time series? If not, explain why. a) Reports from the Bureau of Labor Statistics on the number of U.S. adults who are employed full-time in each major sector of the economy. b) The quarterly Gross Domestic Product (GDP) of France f

> Fred Barolo heads a travel company that offers, among other services, customized travel packages. These packages provide a relatively high profit margin for his company, but Fred worries that a weakened economic outlook will adversely affect this segment

> Alpine Medical Systems, Inc., is a large provider of medical equipment and supplies to hospitals, doctors, clinics, and other health care professionals. Alpine’s VP of Marketing and Sales, Kenneth Jadik, asked one of the company’s analysts, Nicole Haly,

> Most people older than 40 remember chia seeds as the source of “green hair” for a variety of animal-shaped terra-cotta figurines. While chia pets are still available (there is even one resembling President Trump), chia seeds are now more often recognized

> GoLearn is a new social networking site designed mainly for college students who have an interest in study abroad. Students from both within and outside the United States are beginning to join, although the majority of users are from the United States. U

> A concert production company examined its records. The manager made the following scatterplot. The company places concerts in two venues, a smaller, more intimate theater (plotted with blue circles) and a larger auditorium-style venue. a) Describe the

> Here are data on the monthly price of Delicious apples and gas, which are both components of the Consumer Price Index. The timeplot shows the years 2006–2009 for apples; the data table shows just 2006, for both. For the Apple prices:

> Life insurance rates are based on life expectancy values compiled for large demographic groups. But with improvements in medical care and nutrition, life expectancies have been changing. Here is a table from the National Vital Statistics Report that give

> Many professions use tables to determine key quantities. The value of a log is based on the number of board feet of lumber the log may contain. (A board foot is the equivalent of a piece of wood 1 inch thick, 12 inches wide, and 1 foot long. For example,

> Consider again the post-1960 trend in U.S. GDP we examined in Exercise 57. Here are a regression and residual plot when we use the square root of GDP in the model. Is this a better model for GDP? Explain. Response variable is: VGDP R squared = 99.4%

> The scatterplot shows the gross domestic product (GDP) of the United States in trillions of (2010) dollars plotted against years since 1960. (Data in GDP and DJIA 2017) A linear model fit to the relationship looks like this: (We’ve i

> Of course, what matters most to the individual entrepreneur—the licensed commercial lobster fisher—is the price of lobster. Here’s an analysis relating that price ($/lb) to the number of traps (millio

> Does the Dow Jones Industrial Average (DJIA) reflect the economy as measured by the Gross Domestic Product (GDP)? Here’s a plot and a regression. (Both are converted to 2010 dollars to remove the effects of inflation. GDP is in $B to ma

> Lobster are caught in traps, which are baited and left in the open ocean. Licenses to fish for lobster are limited, there is a small additional fee for each trap in use, and there are limits on the numbers of traps that can be placed in each of seven fis

> According to the Maine Department of Marine Resources, in 2016 more than 130,800,000 pounds of lobster were landed in Maine—a catch worth more than $533.09M. The lobster fishing industry is carefully controlled and licensed, and facts a

> In Exercise 40, we fit a linear regression for the number of monthly international visitors to Hawaii (for the years 2002 through 2006) using Time and dummy variables for the months as predictors. The R2 value was 59.9% and a residual plot against Time

> In Exercise 39, we fit a linear regression for the number of monthly domestic visitors to Hawaii (for the years 2002 through 2006) using Time and dummy variables for the months as predictors. The R2 value was 96.6% and a residual plot against Time would

> The data for hard drives in Exercise 6 originally included a 200 GB (0.2 TB) drive that sold for $299.00 (see Chapter 4, Exercise 2). a) Find the regression line predicting Price from Capacity with this hard drive added. b) What has changed from the orig

> In Exercise 39, we fit a linear regression for the number of monthly domestic visitors to Hawaii (for the years 2002 through 2006) using Time and dummy variables for the months as predictors. The R2 value was 96.6% and a residual plot against Time would

> The following time series plot shows the data for the monthly U.S. Unemployment rate (%) from January 2003 to June 2013. These data have been seasonally adjusted (meaning that the seasonal component has already been removed). a) What time series compon

> Return to the oil price data of Exercise 47. a) Find a linear model for this series. b) Find an exponential (multiplicative) model for this series. c) For the model of Exercise 47 and the models of parts a and b, compute the MAPE. Which model did best? G

> A time series plot of monthly crude oil price ($/barrel) from January 2001 to March 2007 is shown here. Using these data, a) Fit a first-order autoregressive model. b) Obtain a forecast for March 2007. 70+ A 60+ 50+ 40+ 30 20+ 10 Month Jan Jan Jan

> Using the data from Exercise 42, develop and compare the following models. a) Fit an appropriate autoregressive model by testing for the significance of each autoregressive term. b) Obtain a forecast for the week of May 28, 2007. c) Compare your forecast

> a) Fit a linear trend model with dummy variables for the seasonal effect to the e-commerce data in Exercise 44. b) Fit an exponential trend (multiplicative) model with dummy variables to these data. c) Which model fits better? Exercise 44: Quarterly e-c

> Quarterly e-commerce retail sales (in millions of dollars) in the United States are provided. (Source: U.S. Census Bureau; www.census.gov.) Use this time series to answer the following questions. a) Fit a linear trend model to this series but do not use

> The plot of residuals in Exercise 41 shows large fluctuations starting in 2006 and an earlier outlier that wasn’t as evident in the data. The outlier is September 2001. This wasn’t a typical month for air travel becaus

> We have seen that gas prices can fluctuate. But during some periods they have moved consistently. Here are the data extracted for one week of each month from January 2002 to May 2007. The bend in the plot and the increasing variation suggest a multipl

> The Port of Oakland airport reports the number of passengers passing through each month. The time series plot shows fluctuations that may relate to the economy. By recognizing the series as a time series, we may learn more. Here is an additive model th

> An analysis of spending by a sample of credit card bank cardholders shows that spending by cardholders in January ( Jan) is related to their spending in December (Dec): The assumptions and conditions of the linear regression seemed to be satisfied and

> In Exercise 39 we examined domestic tourists who visit Hawaii. Now, let’s consider international tourism. Here’s a time series plot of international visitors for the same time period. Here’s the lin

> Much of the public and private industry in Hawaii depends on tourism. The following time series plot shows the number of domestic visitors to Hawaii by air from the rest of the United States per month from January 2002 through December 2006 before the fi

> The movie Harry Potter and the Sorcerer’s Stone opened as a great success. But every movie sees declining revenue over time. Here are the daily revenues for the movie during its first 17 days. a) Without plotting the data, what componen

> Walmart grew rapidly in the years leading up to the financial crisis. Here is the monthly revenue ($Billion) for Walmart from November 2003 to January 2007. a) What components of a time series do you see in this timeplot? Here’s a reg

> Use the following model to forecast quarterly sales ($000) for a start-up (where time is rescaled to begin at zero and Q2, Q3, and Q4 are dummy variables for the indicated quarters), and answer the following questions. a) For the first quarter of the t

> Use the following model to forecast quarterly sales ($Million) for a company (where time is rescaled to begin at zero and Q2, Q3, and Q4 are dummy variables for the indicated quarters), and answer the following questions. a) For the first quarter of th

> Average annual interest rates (banks prime lending) in the United States from 1966 through 2009 are shown in the following time series graph. a) What components do you see in this series? Here’s an autoregressive model with a 13-week

> In Exercise 30 we looked at the weekly average retail price (cents per gallon) of regular gas nationwide from 2011 through June 2013. Here’s the time series plot again: a) What components can you see in this plot? Hereâ€&#1

> The most common use of the Consumer Price Index (CPI) is as an economic indicator to forecast inflation and evaluate the effectiveness of government policies. Following is the time series plot for the monthly CPI (not seasonally adjusted) from January 20

> The Gallup organization periodically asks the following question: If your party nominated a generally well-qualified person for president who happened to be a woman, would you vote for that person? Here is a time series plot of the percentage answering &

> For the regression in Exercise 19: A student tries taking the reciprocal of customers and produces the plot shown below: a) Would you recommend this transformation? Why or why not? b) What would you suggest?

> We have data on the weekly average retail price (cents per gallon) of regular gas nationwide. We’ll extract the data from 2011 through June 2013. Here’s a time series plot. Here are a 4-point moving average and a 3-t

> Coffee is the world’s second largest legal export commodity (after oil) and is the second largest source of foreign exchange for developing nations. The United States consumes about one-fifth of the world’s coffee. The

> Suppose an autoregressive model is used to model sales for a company that peaks twice per year (in June and December). a) What lagged variables would you try in a regression to forecast sales? Explain. b) How would you determine which of your lagged vari

> Suppose an autoregressive model is used for data in which quarterly sales in 201 were: 1.9, 1.7, 2.2, and 2.3 ($Billion). a) If a first-order autoregressive model is developed with estimated parameters of b0 = 0.100 and b1 = 1.12, compute the forecast fo

> The following graph shows the quarterly median weekly earnings for U.S. women 25 years of age or older (U.S. Bureau of Labor Statistics; www.bls.gov). Data are provided from the first quarter of 2003 through the first quarter of 2013. Here is single ex

> This graph shows the quarterly median weekly earnings from the first quarter of 2003 through the first quarter of 2013 for men, 25 years of age or older, in the United States (U.S. Bureau of Labor Statistics; www.bls.gov). Here are time series plots sh

> The following time series graph shows daily closing stock prices (adjusted for splits and dividends) for Google, Inc., from January 1, 2008, through June 21, 2013 (Source: Yahoo! Finance). a) Which time series components are evident? Single exponential

> The following time series graph shows daily closing stock prices for Toyota Motor Manufacturing from April 1, 2008, through June 21, 2013 (Source: Yahoo! Finance). a) Which time series components seem to be present? The method of moving averages was ap

> Target Corp. operates “big box” stores that sell everyday essentials and fashionable differentiated merchandise. It also operates an online business at target.com. Target’s reported gross earnings per

> The price of bananas fluctuates on the world market. Here are the prices ($/tonne) for the years 2000–2004 (Source: Holy See Country Review, 2008). a) Find a 3-year moving average prediction for the price in 2005. b) Find a prediction

> A quickly growing company shows the following scatterplot of customers vs. time (in months). a) What re-expression might you suggest for the number of customers? b) What power in the ladder of powers does that correspond to?

> Sara Lee Corp., maker of food, beverage, and household products, is known especially for its baked products, marketed under its corporate name. For the five years ending July 1 of each year from 2002 to 2006, their bakery division reported the following