i need the answer quickly For several years many utilities have employed regression analysis to forecast monthly utility usage byresidential customers using weather forecasts, the number of holidays, the number of days in the month,and other factors. For example, the Connecticut Department of Public Utility Control (CDPUC) hasdetermined that regression, properly used, can accurately predict natural gas usage. Most public gasutilities serving Connecticut have reported levels of accuracy from 4% to 10% using regression. Onecompany, Dominion Natural Gas Company of Ohio, uses this approach not to forecast but to explain tocustomers why their natural gas bills have gone up or down compared to the prior month, and alsocompared to the same month of the prior year. The bill shows total MCF (thousand cubic feet of natural gas)used by the customer for that month and why the total MCF usage has changed, based on three factors:1. Change in temperature: each degree increase in temperature causes an increase in the number ofMCFs consumed. The relationship between the change in temperature and the usage of MCF is notlinear, but the monthly bill shows the average change in temperature for the month and the increase ordecrease in MCF related to that change.2. Number of billing days in the period.3. The residual, the change in usage by the customer that is not attributable to temperature or the numberof days in the billing period.A customer of Dominion has used 13.7 MCF in December and is charged $12.50 per MCF for a total billthat month of $171.25. The following data are available to compare the current month's weather and billingperiod to the prior month and to the same month last year:Usage FactorsWeatherNumber of billing daysCustomer-controlled usageCurrent Month vs. Last Month3 degrees cooler; +2.5 MCF5 more days; +0.5 MCF+.9 MCFCurrent Month to Last December8 degrees warmer; -3.5 MCF1 less day; -0.1 MCF-1.8 MCFRequired:1. Determine the amount of difference in the customer's bill from the prior month and from the currentmonth last year. (Negative amounts should be indicated with minus sign. Round your answers to 2decimal places.)Usage FactorsCurrent Month vs. LastMonth$ AmountChangeCurrent Month vs. LastDecember$ AmountChangeWeatherNumber of billing daysCustomer-controlled usageTotal change3 degrees cooler; +2.5 MCF5 more days; +0.5 MCF+0.9 MCF8 degrees warmer; -3.5 MCF1 less day; -0.1 MCF-1.8 MCF

Regression And Utility Rates, SustainabilityUsage factorsCurrent Month Vs. Last MonthChangeCurrent…

Required: 1. Determine the amount of difference in the customer's bill from the prior month and from the current month last year. (Negative amounts should be indicated with minus sign. Round your answers to 2 decimal places.)

Required: 1. Determine the amount of difference in the customer's bill from the prior month and from the current month last year. (Negative amounts should be indicated with minus sign. Round your answers to 2 decimal places.)

Essentials of Business Analytics (MindTap Course List)

2nd Edition

ISBN:9781305627734

Author:Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson

Publisher:Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson

Chapter8: Time Series Analysis And_forecasting

Section: Chapter Questions

Problem 3P: Problems 1 and 2 used different forecasting methods. Which method appears to provide the more...

See similar textbooks

Similar questions

Jensen Tire Auto is deciding whether to purchase a maintenance contract for its new computer wheel alignment and balancing machine. Managers feel that maintenance expense should be related to usage, and they collected the following information on weekly usage (hours) and annual maintenance expense (in hundreds of dollars). a. Develop a scatter chart with weekly usage hours as the independent variable. What does the scatter chart indicate about the relationship between weekly usage and annual maintenance expense? b. Use the data to develop an estimated regression equation that could be used to predict the annual maintenance expense for a given number of hours of weekly usage. What is the estimated regression model? c. Test whether each of the regression parameters 0 and 1 is equal to zero at a 0.05 level of significance. What are the correct interpretations of the estimated regression parameters? Are these interpretations reasonable? d. How much of the variation in the sample values of annual maintenance expense does the model you estimated in part (b) explain? e. If the maintenance contract costs 3,000 per year, would you recommend purchasing it? Why or why not?
Duke Energy reported that the cost of electricity for an efficient home in a particular neighborhood of Cincinnati, Ohio, was $104 per month. A researcher believes that the cost of electricity for a comparable neighborhood in Chicago, Illinois, is higher. A sample of homes in this Chicago neighborhood will be taken and the sample mean monthly cost of electricity will be used to test the following null and alternative hypotheses. Assume the sample data lead to rejection of the null hypothesis. What would be your conclusion about the cost of electricity in the Chicago neighborhood? What is the Type I error in this situation? What are the consequences of making this error? What is the Type II error in this situation? What are the consequences of making this error?
Consider the following time series data: Construct a time series plot. What type of pattern exists in the data? Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2. 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. Compute the quarterly forecasts for next year based on the model you developed in part (b). Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables you developed in part (b) to capture seasonal effects and create a variable t such that t = 1 for quarter 1 in year 1, t = 2 for quarter 2 in year 1, … t = 12 for quarter 4 in year 3. Compute the quarterly forecasts for next year based on the model you developed in part (d). Is the model you developed in part (b) or the model you developed in part (d) more effective? Justify your answer.
The Port Authority sells a wide variety of cables and adapters for electronic equipment online. Last year the mean value of orders placed with the Port Authority was 47.28, and management wants to assess whether the mean value of orders placed to date this year is the same as last year. The values of a sample of 49,896 orders placed this year are collected and recorded in the tile PortAuthority. a. Formulate hypotheses that can be used to test whether the mean value of orders placed this year differs from the mean value of orders placed last year. b. Use the data in the file PortAuthority to conduct your hypothesis test. What is the p value for your hypothesis test? At = 0.01, what is your conclusion?
Problems 1 and 2 used different forecasting methods. Which method appears to provide the more accurate forecasts for the historical data? Explain. Consider the following time series data: Using the naïve method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy: Mean absolute error Mean squared error Mean absolute percentage error What is the forecast for week 7? Refer to the time series data in Problem 1. Using the average of all the historical data as a forecast for the next period, compute the following measures of forecast accuracy: Mean absolute error Mean squared error Mean absolute percentage error What is the forecast for week 7?
For the Hawkins Company, the monthly percentages of all shipments received on time over the past 12 months are 80, 82, 84, 83, 83, 84, 85, 84, 82, 83, 84, and 83. Construct a time series plot. What type of pattern exists in the data? Compare a three-month moving average forecast with an exponential smoothing forecast for α = 0.2. Which provides the better forecasts using MSE as the measure of model accuracy? What is the forecast for next month?
The daily water consumption for an Ohio community is normally distributed with a mean consumption of 842,061 gallons and a standard deviation of 87,901 gallons. The community water system will experience a noticeable drop in water pressure when the daily water consumption exceeds 991,628 gallons. What is the probability of experiencing such a drop in water pressure? (Round your answer to 4 decimal places. Round Z-scores to 2 decimal places.)
Cooper Realty is a small real estate company located in Albany, New York, that specializes primarily in residential listings. The company recently became interested in determining the likelihood of one of its listings being sold within a certain number of days. An analysis of company sales of 800 homes in previous years produced the following data. a. If A is defined as the event that a home is listed for more than 90 days before being sold, estimate the probability of A. b. If B is defined as the event that the initial asking price is under 150,000, estimate the probability of B. c. What is the probability of AB? d. Assuming that a contract was just signed to list a home with an initial asking price of less than 150,000, what is the probability that the home will take Cooper Realty more than 90 days to sell? e. Are events A and B independent?
A sociologist was hired by a large city hospital to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance (miles) between home and work for the employees. A sample of 10 employees was chosen, and the following data were collected. a. Develop a scatter chart for these data. Does a linear relationship appear reasonable? Explain. b. Use the data to develop an estimated regression equation that could be used to predict the number of days absent given the distance to work. What is the estimated regression model? c. What is the 99% confidence interval for the regression parameter 1? Based on this interval, what conclusion can you make about the hypotheses that the regression parameter 1 is equal to zero? d. What is the 99% confidence interval for the regression parameter 0? Based on this interval, what conclusion can you make about the hypotheses that the regression parameter 0 is equal to zero? e. How much of the variation in the sample values of number of days absent does the model you estimated in part (b) explain?
South Shore Construction builds permanent docks and seawalls along the southern shore of Long Island, New York. Although the firm has been in business only five years, revenue has increased from $308,000 in the first year of operation to $1,084,000 in the most recent year. The following data show the quarterly sales revenue in thousands of dollars: Construct a time series plot. What type of pattern exists in the data? Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter I, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. Based on the model you developed in part (b), compute estimates of quarterly sales for year 6. Let Period = 1 refer to the observation in quarter 1 of year 1; Period = 2 refer to the observation in quarter 2 of year 1; … and Period = 20 refer to the observation in quarter 4 of year 5. Using the dummy variables defined in part (b) and the variable Period, develop an equation to account for seasonal effects and any linear trend in the time series. Based on the seasonal effects in the data and linear trend estimated in part (c), compute estimates of quarterly sales for year 6. Is the model you developed in part (b) or the model you developed in part (d) more effective? Justify your answer.
Johnson Filtration. Inc., provides maintenance service for water filtration systems throughout southern Florida. Customers contact Johnson with requests for maintenance service on their water filtration systems. To estimate the service time and the service cost. Johnson’s managers want to predict the repair time necessary for each maintenance request. Hence, repair time in hours is the dependent variable. Repair time is believed to be related to three factors: the number of months since the last maintenance service, the type of repair problem (mechanical or electrical), and the repairperson who performs the repair (Donna Newton or Bob Jones). Data for a sample of 10 service calls are reported in the following table: Develop the simple linear regression equation to predict repair time given the number of months since the last maintenance service, and use the results to test the hypothesis that no relationship exists between repair time and the number of months since the last maintenance service at the 0.05 level of significance. What is the interpretation of this relationship? What does the coefficient of determination tell you about this model? Using the simple linear regression model developed in part (a), calculate the predicted repair time and residual for each of the 10 repairs in the data. Sort the data in ascending order by value of the residual. Do you see any pattern in the residuals for the two types of repair? Do you see any pattern in the residuals for the two repairpersons? Do these results suggest any potential modifications to your simple linear regression model? Now create a scatter chart with months since last service on the x-axis and repair time in hours on the y-axis for which the points representing electrical and mechanical repairs are shown in different shapes and/or colors. Create a similar scatter chart of months since last service and repair time in hours for which the points representing repairs by Bob Jones and Donna Newton are shown in different shapes and/or colors. Do these charts and the results of your residual analysis suggest the same potential modifications to your simple linear regression model? Create a new dummy variable that is equal to zero if the type of repair is mechanical and one if the type of repair is electrical. Develop the multiple regression equation to predict repair time, given the number of months since the last maintenance service and the type of repair. What are the interpretations of the estimated regression parameters? What does the coefficient of determination tell you about this model? Create a new dummy variable that is equal to zero if the repairperson is Bob Jones and one if the repairperson is Donna Newton. Develop the multiple regression equation to predict repair time, given the number of months since the last maintenance service and the repairperson. What are the interpretations of the estimated regression parameters? What does the coefficient of determination tell you about this model? Develop the multiple regression equation to predict repair time, given the number of months since the last maintenance service, the type of repair, and the repairperson. What are the interpretations of the estimated regression parameters? What does the coefficient of determination tell you about this model? Which of these models would you use? Why?
The regional transit authority for a major metropolitan area wants to determine whether there is a relationship between the age of a bus and the annual maintenance cost. A sample of 10 buses resulted in the following data: Develop a scatter chart for these data. What does the scatter chart indicate about the relationship between age of a bus and the annual maintenance cost? Use the data to develop an estimated regression equation that could be used to predict the annual maintenance cost given the age of the bus. What is the estimated regression model? Test whether each of the regression parameters β0 and β1 is equal to zero at a 0.05 level of significance. What are the correct interpretations of the estimated regression parameters? Are these interpretations reasonable? How much of the variation in the sample values of annual maintenance cost does the model you estimated in part (b) explain? What do you predict the annual maintenance cost to be for a 3.5-year-old bus?