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Q:
Unusual sources of process variation that can be attributed to specific reasons are called ____________ causes of variation.
A. common
B. assignable
C. usual
D. expected
Q:
A(n) ____________ process has the ability to produce products or services that meet customer requirements.
A. in-control
B. assignable
C. capable
D. statistical
Q:
The distance between natural tolerance limits and customer specifications is called ____________.
A. process leeway
B. sigma level
C. control limits
D. a zone
Q:
A systematic method for analyzing process data in which we monitor process variation is called ______________.
A. control charting
B. sigma level capability
C. statistical process control
D. rational subgrouping
Q:
A sequence of steadily increasing plot points on a control chart is referred to as ___________.
A. funneling
B. a run up
C. a cycle
D. out of control
Q:
___________ causes of variation may be remedied by local supervision.
A. Common
B. Assignable
C. Usual
D. Expected
Q:
Auxiliary lines drawn on a control chart for accomplishing pattern analysis are called ___________ boundaries.
A. sigma
B. range
C. zone
D. mean
Q:
Sources of process variations that are inherent to the process design are called ___________ causes of variation.
A. common
B. assignable
C. unusual
D. expected
Q:
A control chart on which the proportions of nonconforming units in subgroups of size n are plotted versus time is a(n) _____ chart.
A. B. R
C. p
D. C
Q:
A control chart on which subgroup ranges are plotted versus time is a(n) _____ chart.
A. B. R
C. p
D. C
Q:
A control chart on which subgroup means are plotted versus time is a(n) _________ chart.
A. B. R
C. p
D. C
Q:
A ___________ is a set of process observations that are examined for the purpose of constructing control charts.
A. subgroup
B. sample
C. zone
D. list of common causes
Q:
If a control chart is used correctly and the necessary corrective actions are taken, then as the control limits get close to each other, the potential quality of the product _____________.
A. decreases
B. increases
C. stays the same
D. fluctuates
Q:
A powder metal manufacturing company is producing sleeves for a locking mechanism. The target (nominal) value for the inside diameter is 1 inch. The inside diameter specifications are 1 .005 inches. Assume that the process is in statistical control with = 1.0002 inches, = .003 inches, and subgroup size of 5. Calculate the estimated proportion of out-of-specification sleeve inside diameters.
A. .0471
B. .0217
C. .0001
D. .4783
E. .0139
Q:
A powder metal manufacturing company is producing sleeves for a locking mechanism. The target (nominal) value for the inside diameter is 1 inch. The inside diameter specifications are 1 .005 inches. Assume that the process is in statistical control with = 1.0002 inches, = .003 inches, and subgroup size of 5. Determine the estimated number of standard deviations of process leeway.
A. 3
B. 1.667
C. 3.72
D. 1.443
E. .72
Q:
A powder metal manufacturing company is producing sleeves for a locking mechanism. The target (nominal) value for the inside diameter is 1 inch. The inside diameter specifications are 1 .005 inches. Assume that the process is in statistical control with = 1.0002 inches, = .003 inches, and subgroup size of 5. What is the sigma level capability for this process?
A. 3.0
B. 3.72
C. 1.6
D. 1.0
E. 2.72
Q:
A powder metal manufacturing company is producing sleeves for a locking mechanism. The target (nominal) value for the inside diameter is 1 inch. The inside diameter specifications are 1 .005 inches. Assume that the process is in statistical control with = 1.0002 inches, = .003 inches, and subgroup size of 5. What are the natural tolerance limits for this process? Is the process capable?
A. .99847 to 1.00193, yes
B. .99847 to 1.00193, no
C. .99633 to 1.004, yes
D. .99633 to 1.004, no
E. .995 to 1.005, yes
Q:
The linear regression trend model was applied to a time series of sales data based on the last 16 months of sales. The following partial computer output was obtained.What is the predicted value of y when t = 17?
Q:
The linear regression trend model was applied to a time series of sales data based on the last 16 months of sales. The following partial computer output was obtained.Test the significance of the time term at = .05. State the critical t value and make your decision using a two-sided alternative.
Q:
The linear regression trend model was applied to a time series of sales data based on the last 16 months of sales. The following partial computer output was obtained. Write the prediction equation.
Q:
Consider the regression equation = 18.321 + 3.762(t) and the data below. Compute the residuals (error terms) for periods 6 and 7.
Q:
Consider the regression equation = 18.321 + 3.762(t) and the data below.Compute the predicted value for sales for periods 6 and 7.
Q:
Given the following data, compute the mean absolute deviation.
Q:
Given the following data, compute the mean squared deviation (error).
Q:
Given the following data, compute the total error (sum of the error terms).
Q:
The price and quantity of several food items are listed below for the years 1990 and 2000. Compute the Paasche index, using 1990 as the base year.
Q:
The price and quantity of several food items are listed below for the years 1990 and 2000. Compute the Laspeyres index, using 1990 as the base year.
Q:
Using the price of the following food items, compute the aggregate index numbers for the four types of cheese. Let 1990 be the base year for this market basket of goods.1990 = 100.0, 1995 = 108.5, 2000 = 112.0
Q:
Listed below are the prices of a pair of men's boots over a 50-year time period.Find the simple index numbers for the data with 1950 as the base year.
Q:
Two forecasting models were used to predict the future values of a time series. The forecasts are shown below with the actual values. Which model is the most accurate? Why?
Q:
Two forecasting models were used to predict the future values of a time series. The forecasts are shown below with the actual values. Calculate the mean squared deviation (MSD) for Model 2
Q:
Two forecasting models were used to predict the future values of a time series. The forecasts are shown below with the actual values. Calculate the mean absolute deviation (MAD) for Model 2.
Q:
Two forecasting models were used to predict the future values of a time series. The forecasts are shown below with the actual values. Calculate the mean squared deviation (MSD) for Model 1.
Q:
Two forecasting models were used to predict the future values of a time series. The forecasts are shown below with the actual values. Calculate the mean absolute deviation (MAD) for Model 1.
Q:
Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. The normalized (adjusted) seasonal factors are winter = .9982, spring = .9263, summer = 1.139, and fall = .9365. Based on the following deseasonalized observations (dt), a trend line was estimated. The linear regression trend equation is trt = 10.1 + 1.91t. Use the forecasting equation and calculate the forecasted demand for the fall quarter of 1998 and summer quarter of 2000.
Q:
Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. The normalized (adjusted) seasonal factors are winter = .9982, spring = .9263, summer = 1.139, and fall = .9365. Based on the following deseasonalized observations (dt), a trend line was estimated. The linear regression trend equation is trt = 10.1 + 1.91(t). Based on this trend equation, the following trend values are calculated for each period in the time series. Isolate the cyclical and irregular components by calculating the estimate of CLt IRt for the first four quarters in the time series.
Q:
Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. The normalized (adjusted) seasonal factors are winter = .9982, spring = .9263, summer = 1.139, and fall = .9365. Calculate the deseasonalized production value for each observation in the time series.
Q:
Based on the quarterly production data (in thousands of units) for the XYZ manufacturing company, the average seasonal factor () is .986 for winter, .915 for spring, 1.125 for summer, and .925 for fall. Determine the normalized (adjusted) seasonal factors for each quarter.
Q:
Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below, followed by the centered moving average values and their respective periods.
. Calculate the average seasonal factor for each quarter ().
winter = .986, spring = .915, summer = 1.125, fall = .925
Q:
Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below.Calculate the ratio of actual production to the centered moving average values (snt irt) for the entire time series.
Q:
Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below. Calculate the 4-period (quarter) centered moving average for the entire time series.
Q:
Consider the quarterly production data (in thousands of units) for the XYZ manufacturing company below.Calculate the 4-period (quarter) moving average for the entire time series.
Q:
The following data on prices and quantities for the years 1995 and 2000 are given for three products.Calculate the 2000 Paasche index.
Q:
The following data on prices and quantities for the years 1995 and 2000 are given for three products.Calculate the 2000 Laspeyres index.
Q:
The following data on prices and quantities for the years 1995 and 2000 are given for three products. Calculate the 2000 aggregate price index.
Q:
The following data on prices and quantities for the years 1995 and 2000 are given for three products. Calculate the 2000 simple price index for each product separately.
Q:
Use the following price information for three grains.Calculate the Paasche index.
Q:
Use the following price information for three grains.Calculate the Laspeyres index.
Q:
Use the following price information for three grains. Calculate the aggregate price index.
Q:
Use the following price information for three grains.Calculate the simple price index for each grain separately.
Q:
Consider a time series with 15 quarterly sales observations. Using the quadratic trend model, the following partial computer output was obtained.What is the predicted value of y when t = 20?
Q:
Consider a time series with 15 quarterly sales observations. Using the quadratic trend model, the following partial computer output was obtained.Test the significance of the t2 term at =.05. State the critical T value (rejection point) and the p-value. Make your decision using a two-sided null hypothesis.
Q:
Consider a time series with 15 quarterly sales observations. Using the quadratic trend model, the following partial computer output was obtained.State the two-sided null and alternative hypotheses to test the significance of the t2 term.
Q:
Consider a time series with 15 quarterly sales observations. Using the quadratic trend model, the following partial computer output was obtained. Write the prediction equation.
Q:
The linear regression trend model was applied to a time series sales data set based on the last 24 months' sales. The following partial computer output was obtained.What is the predicted value of y when t = 25?
Q:
The linear regression trend model was applied to a time series sales data set based on the last 24 months' sales. The following partial computer output was obtained.Test the significance of the time term at =.05. State the critical t value and make your decision using a two-sided alternative.
Q:
The linear regression trend model was applied to a time series sales data set based on the last 24 months' sales. The following partial computer output was obtained. Write the prediction equation.
Q:
Consider the following set of quarterly sales data, given in thousands of dollars.The following dummy variable model that incorporates a linear trend and constant seasonal variation was used: y(t) = B0 + B1t + BQ1(Q1) + BQ2(Q2) + BQ3(Q3) + Et. In this model, there are 3 binary seasonal variables (Q1, Q2, and Q3), where Qi is a binary (0,1) variable defined as:Qi = 1, if the time series data is associated with quarter i;Qi = 0, if the time series data is not associated with quarter i.The results associated with this data and model are given in the following Minitab computer output.The regression equation isSales = 2442 + 6.2 Time - 693 Q1 - 1499 Q2 + 153 Q3At α = .05, test the significance of the model.
Q:
Consider the following set of quarterly sales data, given in thousands of dollars.The following dummy variable model that incorporates a linear trend and constant seasonal variation was used: y(t) = B0 + B1t + BQ1(Q1) + BQ2(Q2) + BQ3(Q3) + Et. In this model, there are 3 binary seasonal variables (Q1, Q2, and Q3), where Qi is a binary (0,1) variable defined as:Qi = 1, if the time series data is associated with quarter i;Qi = 0, if the time series data is not associated with quarter i.The results associated with this data and model are given in the following Minitab computer output.The regression equation isSales = 2442 + 6.2 Time - 693 Q1 - 1499 Q2 + 153 Q3Provide a managerial interpretation of the regression coefficient for the variable "time."
Q:
Consider the following set of quarterly sales data, given in thousands of dollars.The following dummy variable model that incorporates a linear trend and constant seasonal variation was used: y(t) = B0 + B1t + BQ1(Q1) + BQ2(Q2) + BQ3(Q3) + Et. In this model, there are 3 binary seasonal variables (Q1, Q2, and Q3), where Qi is a binary (0,1) variable defined as:Qi = 1, if the time series data is associated with quarter i;Qi = 0, if the time series data is not associated with quarter i.The results associated with this data and model are given in the following Minitab computer output.The regression equation isSales = 2442 + 6.2Time - 693Q1 - 1499Q2 + 153Q3Provide a managerial interpretation of the regression coefficients for the variables Q1 (quarter 1), Q2 (quarter 2), and Q3 (quarter 3).
Q:
Consider the following set of quarterly sales data given in thousands of dollars. Write an appropriate dummy variable model that incorporates a linear trend and constant seasonal variation.
Q:
Consider the regression equation = 6.04 + 0.10 (t)and the data below. Compute the residual (error term) for period 8.
Q:
Consider the regression equation = 6.04 + 0.10(t)and the data below.Compute the predicted value of sales for period 8.
Q:
The linear trend equation for the following data is = 1.4286 + 2.5(t) .Find the residual value (error) for period 7.
Q:
The linear trend equation for the following data is = 1.4286 + 2.5 (t).What is the predicted value of the fund in period 7?
Q:
The linear trend equation for the following data is = 1.4286 + 2.5(t). What is the predicted value of the fund in the period t = 1?
Q:
Consider the following data and calculations. Calculate the estimated value of b1 and b0, and state the linear trend regression prediction equation.
Q:
Consider the following data and calculate S2 using simple exponential smoothing and α = 0.3.
Q:
Consider the following data and calculate S1 using simple exponential smoothing and α = 0.3.
Q:
Based on the following data, a forecaster used simple exponential smoothing and determined the following: S0 = 19, S1 = 18.6, S2 = 19.08, S3 = 19.064, S4 = 19.851, and S5 = 19.481. Calculate the Mean Absolute Deviation (MAD).
Q:
Based on the following data, a forecaster used simple exponential smoothing and determined the following: S0 = 19, S1 = 18.6, S2 = 19.08, S3 = 19.064, S4 = 19.851, and S5 = 19.481. Calculate the Mean Squared Deviation (MSD or MSE).
Q:
Based on the following data, a forecaster used simple exponential smoothing and determined the following: S0 = 19, S1 = 18.6, S2 = 19.08, S3 = 19.064, S4 = 19.851, and S5 = 19.481. Calculate the average forecast error.
Q:
Consider the following data.Calculate S5 using simple exponential smoothing if S3 = 19.064 and = 0.2.
Q:
Consider the following data.Calculate S3 using simple exponential smoothing if S1 = 18.6 and = 0.2.
Q:
Consider the following data.Using simple exponential smoothing with = 0.2, determine the forecast error for time period 1.
Q:
Consider the following data.Calculate S1 using simple exponential smoothing and = .2.
Q:
Consider the following data.Calculate S0 using simple exponential smoothing and =.2.
Q:
Given the following data, compute the mean absolute deviation.
Q:
Given the following data, compute the mean squared deviation (error).
Q:
Given the following data, compute the total error (sum of the error terms).