What is the difference between a causal model and a time-series model?
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A: Forecasting refers to the process of making predictions for the future using past and present data.…
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A: In this case, we want to know the initial demand for the product. Prediction of demand can be done…
Q: Distinguish between the following types of forecasts:c. Causal versus naive
A: Forecasting is the process of identifying the demand accurately for future production planning and…
Q: What are the two most important factors of forecasting techniques.?
A: Forecasting is a tool for finding future predictions and trends by analyzing past and present data.
Q: What is the primary difference between a time-series model and an associative model?
A: Associative forecasting models typically consider multiple variables that are related to the…
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Q: Identify and briefly explain the two primary approaches to forecasting.
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Q: 1. A forecaster must decide on the value of this factor before he can use the simple moving average…
A: Since you have asked multiple questions, we will solve the first question for you. If you want any…
Q: Whai is Time-series forecasts?
A: Forecasting is the process of identifying the demand accurately for future production planning and…
Q: What is the first-order smoothing and trend adjusted smoothing?
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Q: What is the difference between a dependent and an independent variable?
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Q: Explain the term forecasting with least squares
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Q: Describe the various types of time-series and associative forecasting models. Which types of…
A: Time series models take a gander at past examples of information and endeavor to foresee the future…
Q: Define time-series forecasting model and give examples.
A: Forecasting is the process of making assumptions of the future on the basis of past and present data…
Q: Causal relationships are potentially useful for which component of a time series?
A: Causal inference over random variables, representing different events. The most common example are…
Q: Discuss the differences between a causal model and a time-series model. Be sure to provide examples…
A: Causal model of forecasting is an approach that would predict or forecast the futuristic events…
Q: What is seasonality?How do we forecast using data that has seasonality?
A: Seasonality in time series data is the occurrence of repetitive up and down cycles in series values…
Q: What kind of prediction model will be more suitable for an enterprise that introduced a new product?
A: Prediction models is a technique that uses statistics to make predictions about a collection of data…
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Q: How does the linear trend line forecasting model differ from a lincar regression model for…
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Q: Compare and contrast the four approaches to judgmental forecasting.
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Q: What are the basic types of forecasts? What are their strengths and weaknesses?
A: Forecasting is the process of making assumptions of the future on the basis of past and present data…
Q: In opposition to causal technology, what are the fundamental assumptions when using time series…
A: The following are the basic assumptions in time series forecasting:
Q: Discuss why are forecasts generally wrong
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What is the difference between a causal model and a time-series model?
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- 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 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 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?
- Stock market analysts are continually looking for reliable predictors of stock prices. Consider the problem of modeling the price per share of electric utility stocks (Y). Two variables thought to influence this stock price are return on average equity (X1) and annual dividend rate (X2). The stock price, returns on equity, and dividend rates on a randomly selected day for 16 electric utility stocks are provided in the file P13_15.xlsx. Estimate a multiple regression equation using the given data. Interpret each of the estimated regression coefficients. Also, interpret the standard error of estimate and the R-square value for these data.Management of a home appliance store wants to understand the growth pattern of the monthly sales of a new technology device over the past two years. The managers have recorded the relevant data in the file P13_05.xlsx. Have the sales of this device been growing linearly over the past 24 months? By examining the results of a linear trend line, explain why or why not.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.
- Discuss the differences between a causal model and a time-series model. Be sure to provide examples to illustrate your understanding of these concepts.how is a time series module and a causal model used in a business?What are the basic assumptions in contrast to causal techniques when using predictive time series techniques?
- In comparison to causal techniques, what are the fundamental assumptions when utilizing predictive time series techniques?In opposition to causal technology, what are the fundamental assumptions when using time series predictions?What is the distinction between a dependent variable and an independent variable?