Lori Cook has developed the following forecasting model: ^y=45.0+4.20x, where ^y=demand for Kool Air conditioners and x=the outside temperature (degrees Fahrenheit) a) When the temperature outside is 70°F, demand forecast=__ air conditioners ( enter your response as an integer).

Lori Cook has developed the following forecasting model: ^y=45.0+4.20x, where ^y=demand for Kool Air conditioners and x=the outside temperature (degrees Fahrenheit) a) When the temperature outside is 70°F, demand forecast=__ air conditioners ( enter your response as an integer).

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section: Chapter Questions

Problem 42P: The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars)...

See similar textbooks

Related questions

Q: Both time series and causal forecasting assume that the past relationship between demand and the…

A: Time series forecasting refers to the prediction of future events of a business. According to the…

Q: A police station had to deploy a police officer for an emergency multiple times in the last four…

A: The technique of Naïve forecasting is when the previous period's sales are utilized to anticipate…

Q: Weekly sales of copy paper at Cubicle Suppliers are in the table below. Compute a three- period…

A: This question is related to the topic -Forecasting and this topic would fall under the business…

Q: Simple exponential smoothing with α= 0.3 is being used to forecast sales of digital cameras at…

A: Given Information: Sales in September: 120 units Forecast in September: 100 units Alpha = 0.3…

Q: A manager of a store that sells and installs spas wants to prepare a forecast for January, February,…

A: The trend equation for monthly demand: Ft = 70 + 5t Where t = 0 in June of last year. In this…

Q: Consider the following time series data. Month 1 2 3 4 5 6…

A: MSE stands for Mean squared error. It calculates the average squared difference between the forecast…

Q: The number of heart surgeries performed at Heartville General Hospital has increased steadily over…

A: i. Forecast Using Exponential Smoothing with alpha value 0.6 and initial forecast 45 Year…

Q: a) b) would have applied the Naïve Model, calculate the Naïve Forecast values for the months of…

A: Answers of all parts is below:-

Q: Data collected on the yearly registrations for a Six Sigma seminar at the Quality College are shown…

A: Forecasting is the process of estimation in which future demand is determined using previous or…

Q: The following is the actual sales for Manama Company for a particular good: t Sales 17 5 31 The…

A: Given data is

Q: Simple exponential smoothing with a 5 0.3 is being used to forecast sales of digital cameras at…

A: Given Smoothing constant a=0.3 Forecast for September = 100 cameras Sales in September = 120 cameras

Q: The manager of a small health clinic would like to use exponential smoothing to forecast demand for…

Q: The demand (in number of units) for Apple iPad over the past 6 months at BestBuy is summarized…

A: 2-month weighted moving average: Formula: Answer:

Q: Obtain the linear trend equation for the following data on new checking accounts at Fair Savings…

A: Linear trend equation is given by: y = a+bx here, a = intercept and b = slope Formulas used: a = ∑y…

Q: The plot of the time series helps to decide about the best model to be used for forecasting O a True…

A: Th answer for the above question is as follows:

Q: A police station had to deploy a police officer for an emergency multiple times in the last four…

A: Naive forecasting is an forecast estimation technique in which the current period forecast is equal…

Q: In the Petroco Service Station problem, for the exponentially smoothed forecast with an a value of…

A: Forecasting is a technique used to predict future projections. It requires evaluating past trends to…

Q: Registration numbers for an accounting seminar over the past I 0 weeks are shown below:…

A: Note: “Since you have posted a question with multiple sub-parts, we will solve the first three…

Q: The Carbondale Hospital is considering the purchase of a new ambulance. The decision will rest…

A: Note: I have answered for sub-parts from (a) to (c) according to guidelines. Kindly post the…

Q: Given that the previous forecast of 65 turnedout to be four units less than the actual demand; the…

A: In simple exponential smoothing method, forecast for period t can be calculated using the following…

Q: Calculate the forecasted registrations for years 2 through 12 using exponential smoothing, with a…

A: Forecasting is a technique used to predict future outcomes on the basis of past data. In business…

Q: A production company was able to hit the following sales order for the past 3 months: 45% of 420pcs…

A: Given data is

Q: Calculate the 2-month Moving Average Forecast for sales from March/2019 to a) January/2019 and enter…

A: Below is the solution:-

Q: Apply 4-period SMA and 4-period WMA (0.1, 0.2, 0.3, 0.4) to the given data. Determine the MAD, MSE,…

A: THE ANSWER IS AS BELOW:

Q: Consider the total production (and sales) of ice cream in Canada (in millions of liters) for the…

A: Since we only answer up to 3 sub-parts, we will answer the first 3. Please resubmit the question and…

Q: All forecasting methods using exponential smoothing, adaptive smoothing, and exponential smoothing…

A: Forecasting is the process of making assumptions of the future on the basis of past and present…

Q: а. Write down the definition of Associative model forecast with appropriate example. b. Compute MAD,…

A: Forecasting involves victimization of past knowledge to come up with a variety, set of numbers, or…

Q: In the Petroco Service Station problem, for the exponentially smoothed forecast with an a value of…

A: Forecasting is a technique used to predict future outcomes on the basis of past data. In businesses…

Q: The manager of a popular tourist resort wants to use the manual trend projection forecasting…

A: The manager of a popular tourist resort wants to use the manual trend projection forecasting…

Q: A police station had to deploy a police officer for an emergency multiple times in the last four…

A: Find the Given details below: Given Details: Weekday Number of calls each day Monday 6…

Q: A police station had to deploy a police officer for an emergency multiple times in the last four…

A: Forecast for Friday using naive approach = Actual demand of previous period(Thursday) = 12.

Q: A police station had to deploy a police officer for an emergency multiple times in the last four…

A: Moving Average Method: Moving average is an uncomplicated, technical examination method. Moving…

Q: discuss

A: The answer to this question is false.

Q: A police station had to deploy a police officer for an emergency multiple times in the last four…

A: Exponential smoothing helps in finding the forecasted demand using the previous data. It required…

Q: A police station had to deploy a police officer for an emergency multiple times in the last four…

A: A moving average is a method for determining the overall trends in a data collection by taking the…

Q: My problem asks me to Forecast using a "simple 4-month moving average" but when I calcualte that, I…

A: A) Month Units Sold May 1500 June 1400 July 1800 August 1500…

Q: What effect does the number of cycles in a moving average have on the forecast's responsiveness?

A: In order to estimate potential demand, the Moving Average (MA) projection method uses the MA formula…

Q: Forecasting is a prediction rather than a reality

A: The term forecasting is a technique that uses the historical data as inputs to make the informed…

Q: The following set of data represents the quarterly changes in demand for an item over the next 3…

A: When determining the direction of future trends, forecasting is a technique that makes educated…

Q: Demand for oil changes at Garcia's Garage has been as follows: IT Month Number of Oil Changes…

A: Linear Regression Month Month ( X ) No. of oil changes ( Y ) XY X2 Jan 1 39 39 1 Feb…

Q: A restaurant wants to forecast its weekly sales. Historical data (in dollars) for 15 weeks are…

A: Forecasting refers to the statistical technique used for predicting the future demand and sales of…

Q: The number of heart surgeries performed at Heartville General Hospital has increased steadily over…

A: Since we only answer up to 3 sub-parts, we’ll answer the first 3. Please resubmit the question and…

Q: 2. Suppose that the University of the West had the following data of its growth of enrollment from…

A: Since you have posted a question with multiple sub-parts, we will solve first three sub-parts for…

Q: Using the latest observation in a sequence of data to forecast the next period is a. a naive…

A: Find the answer below: The Correct answer is a) a naïve forecast

Q: 0. During the past five months the emergency new County Hospital has observed the number of patients…

A: Seasonal adjustment is a strategy for information smoothing that is utilized to foresee monetary…

Q: Consider the following actual and forecast demand levels for Big Mac hamburgers at a local…

A: Let, Ft+1 = Forecast for friday Yt = 48.00 Ft = 77.60 α = 0.40 Thus expression for the forecast for…

Q: FORECASTING - Linear Regression General instruction: Solve the following problem as directed. Show…

A: The excel output for the above mentioned problem is as follows,

Q: You have been assigned to forecast the number of aircraft engines ordered each month by Commins…

A: Note: - In the given question alpha value is 0.3 instead of typing mistake 'a 5 0.3'. Given data is…

Q: It has been said that forecasting using exponential smoothing is like driving a car by looking in…

A: Exponential smoothing is a time series forecasting technique for univariate data that can be…

Concept explainers

Forecasting in Business

Forecasting is the process of predicting the future based on the past and the present data and circumstances. In other words, forecasting is the act of making a detailed analysis of future events based on the decisions made in the past as well as the present. Forecasting plays a very important role in operational management as it helps in planning and decision-making. It is not only important for the success of a business but also contributes to individual growth and success. Many business organizations or companies have failed due to the shortcomings of their forecasting.

Question

Lori Cook has developed the following forecasting model:

^y=45.0+4.20x,

where ^y=demand for Kool Air conditioners and

x=the outside temperature (degrees Fahrenheit)

a) When the temperature outside is 70°F, demand forecast=__ air conditioners ( enter your response as an integer).

Leveraging special tenchniques in analyzing historical data to predict future trends. Forecasting covers the methods and types of forecasting and their application to case studies.

Expert Solution

Step 1

Using the given Forecasting model the forecast for air conditioner at various level of temperature are as follows :

a) When the temperature is 70 degree Fahrenheit-339 Air conditioner

Trending now

This is a popular solution!

Step by step

Solved in 2 steps

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, operations-management and related others by exploring similar questions and additional content below.

Similar questions

The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?
The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
The file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?
The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?
Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.
The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?
The management of a technology company is trying to determine the variable that best explains the variation of employee salaries using a sample of 52 full-time employees; see the file P13_08.xlsx. Estimate simple linear regression equations to identify which of the following has the strongest linear relationship with annual salary: the employees gender, age, number of years of relevant work experience prior to employment at the company, number of years of employment at the company, or number of years of post secondary education. Provide support for your conclusion.
The file P13_19.xlsx contains the weekly sales of a particular brand of paper towels at a supermarket for a one-year period. a. Using a span of 3, forecast the sales of this product for the next 10 weeks with the moving averages method. How well does this method with span 3 forecast the known observations in this series? b. Repeat part a with a span of 10. c. Which of these two spans appears to be more appropriate? Justify your choice.
The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a branch office of Northern Central Bank. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b? Is it guaranteed to produce better forecasts for the future?