A cyclical pattern will depict a systematic increase or decrease in the mean of time series data and over time.
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A cyclical pattern will depict a systematic increase or decrease in the mean of time series data and over time.
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- 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 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?Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.
- 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_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?Choose the correct option that best represents the pattern observed in the time-series graph. Ocean Waves 45 35- 30- 20- 154 10- V Time Series Plot of Ocean Waves 50 Index 60 70 The trend is increasing over time. The trend is decreasing over time. O The trend is relatively stable with no significant changes. The trend exhibits periodic fluctuations. None of the above are correct. 80 90 99 20.2
- Which time-series component is said to fluctuate around the long-term and is fairly irregular in appearance? A ) Trend B) Cyclical C) Seasonal D) Irregular E ) None of TheseWhat distinguishes seasonality from cycles in time series analysis?Room registrations in the Toronto Towers Plaza Hotel have been recorded for the past 9 years. To project future occu-pancy, management would like to determine the mathemati-cal trend of guest registration. This estimate will help the hotel determine whether future expansion will be needed. Given the following time-series data, develop a regression equation relat-ing registrations to time (e.g., a trend equation). Then forecast year 11 registrations. Room registrations are in the thousands:Year 1: 17 Year 2: 16 Year 3: 16 Year 4: 21 Year 5: 20Year 6: 20 Year 7: 23 Year 8: 25 Year 9: 24
- The San Diego Freeway 405 in Southern California showed the number of accidents during the past 6 months. Month Accidents Jan Feb Mar Apr May Jun 18 36 [Select] 28 48 39 55 Using the least squares regression method, the trend equation for forecast is Y = slope X + intercept What is the slope [Select] and the intercept [Select] What would be the forecasted July number of accidents [ Select] of this correlation, and coefficient of determination [ Select] and coefficient of correlationThe following table gives the number of pints of type A blood used at Damascus Hospital in the past 6 weeks: Week Of August 31 September 7 September 14 September 21 September 28 October 5 Pints Used 360 389 412 378 366 371 a) The forecasted demand for the week of October 12 using a 3-week moving average: to two decimal places). = pints (round your responseThe following table gives the number of pints of type A blood used at Damascus Hospital in the past 6 weeks: a) The forecasted demand for the week of October 12 using a 3-week moving average = Week Of August 31 September 7 September 14 September 21 September 28 October 5 pints (round your response to two decimal places). Pints Used 345 389 408 381 371 374