21 How many observations should a stopwatch time-study analyst plan for in a task that has a standard deviation of 1.50 minutes per piece if the goal is to estimate the mean time per piece to within 0.2 minute with a confidence of 99.0 percent? (Round the intermediate calculations to 2 decimal places. Round the final answer to the next whole number.) Observations

21 How many observations should a stopwatch time-study analyst plan for in a task that has a standard deviation of 1.50 minutes per piece if the goal is to estimate the mean time per piece to within 0.2 minute with a confidence of 99.0 percent? (Round the intermediate calculations to 2 decimal places. Round the final answer to the next whole number.) Observations

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 6P: Consider the following time series data: Construct a time series plot. What type of pattern exists...

See similar textbooks

Similar questions

The life (in months) of a certain computer component has a probability density function defined by f(x)=16e−x/6f(x)=16e-x/6for x in [0,∞)[0,∞). Find the probability that a component randomly selected will last between 14 and 23 months?
Consider the following time series data: Construct a time series plot. What type of pattern exists in the data? Develop a three-week moving average for this time series. Compute MSE and a forecast for week 8. Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 8. Compare the three-week moving average forecast with the exponential smoothing forecast using α = 0.2. Which appears to provide the better forecast based on MSE? Use trial and error to find a value of the exponential smoothing coefficient α that results in a smaller MSE than what you calculated for α = 0.2.
Consider the following time series data: Construct a time series plot. What type of pattern exists in the data? Develop a three-week moving average for this time series. Compute MSE and a forecast for week 7. Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 7. Compare the three-week moving average forecast with the exponential smoothing forecast using α = 0.2. Which appears to provide the better forecast based on MSE? Explain. Use trial and error to find a value of the exponential smoothing coefficient α that results in a smaller MSE than what you calculated for α = 0.2.
ind each Poisson probability, using a mean arrival rate of 10 arrivals per hour. (a) Seven arrivals. (Round your answer to 4 decimal places.) Poisson probability (b) Three arrivals. (Round your answer to 4 decimal places.) Poisson probability (c) Fewer than five arrivals. (Round your answer to 4 decimal places.) Poisson probability (d) At least 11 arrivals. (Round your answer to 4 decimal places.) Poisson probability
• If the actual value of a time series at time t and the forecast value for time t is denoted by At and Ft respectively, then the formula for the mean absolute deviation over a range of forecasted values is Please SELECT A or B or C or D, referring to the formula on the image below: 11 Σ At - Fil A) MAD=1 n B) MAD=1 F2 At - Ft)² C) MAD=1 n 11 At-Ft 2 D) MAD=1
Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? Is there an indication of a seasonal pattern? b. Use a multiple linear 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. c. Compute the quarterly forecasts for next year.
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.
Type equation here.Homework 5: Chapter 17 2020 Oct 13, D. 7.28% 4. The standard deviation for the S&P S00 is A. 9.38% BUS 202 Dr. Fausti Please fill out the table below. Use your estimates from the table to answer the multiple-choice questions below. When answering the questions below remember that rounding may result in your answer being slightly different that the answers provided, so pick the answer closet to your calculation. B. 7.28% C. 0.88% D. 5.51% Date S&P IBM RR-S&P RR-IBM 5. If we use the standard deviation as a measure of financial risk which financial asset would be ranked as having the greatest financial risk? 1/1/2020 2/1/2020 3/1/2020 4/1/2020 5/1/2020 6/1/2020 7/1/2020 3226 120 NA NA 2954 125 A. S&P 500 B. IBM 2585 108 2912 122 6. If we use the coefficient of variation as a measure of financial risk which financial asset would be ranked as having the greatest financial risk? C S&P 500 D. IBM 3044 122 3100 119 3271 121 Expected RR variance RR std. dev RR Cof. VAR Yr. RR…
What value can be derived from 4 hour pivot point strategies? And how helpful is probability knowledge to the pivot point strategy for forex?
. Using 625 trading days of data, you estimated the daily log return follows a normal distribution with a mean of 5 bps and and a stdev of 125 bps. Q1a. based on information above, what is the probability of true daily log return average is 0? can you reject the true mean is 0? can you reject the true mean is 10 bps? Q1b. what is the 90, 95, and 99% confidence interval for your mean return estimate? Q1c. what is the mean log return and stdev of log return over one year period and four year period (assuming 252 trading days per year)? Q1d. based on Q1c what is the probably of losing money (negative log return) or doubling your money (total log return = ln(2)) over 1 year and 4 year period?
A regression line has an intercept value of 174.71 and slope value of 25.22. What is the predicted value (the y-value) in the 100th period (the x-value is 100)? Round to two decimal places.
Show your work (use of formula, etc.) in solving the problem. Provide your answer/solution in the answer space provided below. Answer the question: Given the following historical returns, calculate the average return and the standard deviation: Year Return 1 14% 2 10% 3 15% 4 11%