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essential statistics
Statistics Data Analysis And Decision Modeling 5th International Edition James R. Evans - Solutions
4. If 40 samples of 200 items are tested for nonconformity, and 240 of the 8000 items are defective, find the upper and lower control limits for a p -chart.
3. The sample grand mean (based on 45 samples of seven observations each) for the weight of a can of beans is 250 grams with an average range of 8 grams.Find the upper and lower control limits for the x - and R -charts.
2. Suppose that the sample grand mean for the weight of a package of candies based on 35 samples of 12 candies each is 18 ounces with an average range of 0.8 ounces. Find the upper and lower control limits for the x - and R-charts.
1. Find the upper and lower control limits for x - and R -charts for the thickness of a special kind of disc when the sample grand mean (based on 40 samples of five observations each) is 1.5 millimeters and the average range is 0.03 millimeters.
9. What are the differences between process capability and process control?
8. Describe the common types of out-of-control conditions that one might find in a control chart.
7. Explain the differences between an x chart and an R chart.
6. What are nominal specifications and tolerances?
5. Explain the purpose and use of a control chart.
4. Describe the steps involved in applying statistical process control.
3. What is the aim of statistical process control? When do we consider a process to be in control and when is it out of control ?
2. What is the difference between variables and attributes?
1. What kinds of variation in the production process are beyond our control and what kinds of variation require our intervention? Discuss some of the sources of each type of variation.
22. Use CB Predictor to find the best forecasting model for the data in the following economic time series:a. New Car Salesb. Housing Startsc. Coal Consumptiond. DJIA December Closee. Federal Funds Ratesf. Mortgage Rates g. Prime Rate h. Treasury Yield Rates
21. The Excel file Olympic Track and Field Data provides the gold medal–winning distances for the high jump, discus, and long jump for the modern Olympic Games. Develop forecasting models for each of the events. What do your models predict for the next Olympics?
20. Construct a line chart for each of the variables in the data file Death Cause Statistics, and suggest the best forecasting technique. Then apply CB Predictor to find the best forecasting models for these variables.
19. Construct a line chart for the data in the Excel file Arizona Population.a. Suggest the best-fitting functional form for forecasting these data.b. Use CB Predictor to find the best forecasting model.
18. Find the best moving average and exponential smoothing models for forecasting gasoline prices in the Excel file Gasoline Prices using CB Predictor. Compare your results to your answers to Problem 4.
17. Find the best moving average and exponential smoothing models for the male and female populations in the Excel file Ohio Prison Population using CB Predictor. Compare your results to your answers to Problem 3.
16. Find the best moving average and exponential smoothing models for each of the stocks in the Excel file Baseball Attendance using CB Predictor. Compare your results to your answers to Problem 2.
15. Find the best moving average and exponential smoothing models for each of the stocks in the Excel file Closing Stock Prices using CB Predictor. Compare your results to your answers to Problem 1.
14. Data in the Excel File Microprocessor Data shows the demand for one type of chip used in industrial equipment from a small manufacturer.a. Construct a chart of the data. What appears to happen when a new chip is introduced?b. Develop a causal regression model to forecast demand that includes
13. Develop a multiple regression model with categorical variables that incorporate seasonality for forecasting housing starts beginning in June 2006 using the data in the Excel file Housing Starts.
12. Develop a multiple regression model with categorical variables that incorporate seasonality for forecasting sales using the last three years of data in the Excel file New Car Sales.
11. Develop a multiple regression model with categorical variables that incorporate seasonality for forecasting the temperature in Washington, D.C., using the data for years 1999 and 2000 in the Excel file Washington DC Weather .Use the model to generate forecasts for the next nine months and
10. Find the best autoregressive model for each of the variables in the Excel file Retail Electricity Prices . (Hint: use CB Predictor to identify the strongest lags.)
9. Find the best autoregressive model for the closing price of the S&P 500 using the Excel file S&P 500 . (Hint: use CB Predictor to identify the strongest lags.)
8. Consider the data in the Excel file Nuclear Power .a. Use simple linear regression to forecast the data.What would be the forecasts for the next three years?b. Are the data autocorrelated? Construct first- and second-order autoregressive models and compare the results to part (a).
7. In the Excel file Surgery Infections , develop spreadsheet models for forecasting the surgery infection rates.Use an exponential smoothing model with smoothing constant a = 0.5, and weighted moving average with k = 3 and weights 10%, 20% and 70%. Compare them on the basis of the usual error
6. Consider the data in the Excel file Consumer Price Index .a. Use simple linear regression to forecast the data.What would be the forecasts for the next two months?b. Are the data autocorrelated? Construct first- and second-order autoregressive models and compare the results to part (a).
5. In the Excel file Treasury Yield Rates , develop spreadsheet models for forecasting the one month treasury yield. Use simple moving average models with k = 2 and k = 3, and compare them on the basis of the usual error metrics.
4. For the data in the Excel file Gasoline Prices do the following:a. Develop spreadsheet models for forecasting prices using single moving average and single exponential smoothing.b. Using MAD, MSE, and MAPE as guidance, find the best number of moving average periods and best smoothing constant
3. For the data in the Excel file Ohio Prison Population do the following:a. Develop spreadsheet models for forecasting both male and female populations using single moving average and single exponential smoothing.b. Using MAD, MSE, and MAPE as guidance, find the best number of moving average
2. Use the data in the Excel file Baseball Attendance to do the following:a. Develop spreadsheet models for forecasting attendance using single moving average and single exponential smoothing.b. Using MAD, MSE, and MAPE as guidance, find the best number of moving average periods and best smoothing
1. The Excel file Closing Stock Prices provides data for four stocks over a one-month period.a. Develop spreadsheet models for forecasting each of the stock prices using single moving average and single exponential smoothing.b. Using MAD, MSE, and MAPE as guidance, find the best number of moving
14. Summarize some of the practical issues in using forecasting tools and approaches.
13. What are the advantages of using CB Predictor for forecasting?
12. What is a causal variable in forecasting? Provide an exam ple from your experience of some applications where causal variables might be used in a forecast.
11. How are dummy variables used in regression forecasting models with seasonality?
10. What kind of forecasting models are most appropriate when significant autocorrelation is present in the data?
9. Which methods work well on a stationary time series?
8. Explain how the exponential smoothing model can be interpreted in two different ways.
7. List and define the three principal ways of measuring forecast accuracy. What are the key differences among them?
6. Explain how a simple moving average is calculated.
5. Summarize statistical methods used in forecasting and the types of time series to which they are most appropriate.
4. Describe the seasonal effect and cyclical effect components of a time series.
3. What does the trend component of a time series represent?
2. Describe the operational format of the Delphi method.
1. What is a time series? Why is the analysis of a time series important? What is meant by forecasting?
29. The Helicopter Division of Aerospatiale is studying assembly costs at its Marseilles plant. 4 Past data indicates the following labor hours per helicopter:Helicopter Number Labor Hours 1 2,000 2 1,400 3 1,238 4 1,142 5 1,075 6 1,029 7 985 8 957 Using these data, apply simple linear regression,
28. Cost functions are often nonlinear with volume because production facilities are often able to produce larger quantities at lower rates than smaller quantities.3 Using the following data, apply simple linear regression, and examine the residual plot. What do you conclude? Construct a scatter
27. A national homebuilder builds single‐family homes and condominium‐style townhouses. The Excel file House Sales provides information on the selling price, lot cost, type of home, and region of the country (M = Midwest, S = South) for closings during one month.a. Develop a multiple regression
26. A mental health agency measured the self‐esteem score for randomly selected individuals with disabilities who were involved in some work activity within the past year. The Excel file Self‐Esteem provides the data, including the individuals’ marital status, length of work, type of support
25. The State of Ohio Department of Education has a mandated ninth‐grade proficiency test that covers writing, reading, mathematics, citizenship (social studies), and science. The Excel file Ohio Education Performance provides data on success rates (defined as the percentage of students passing)
24. Apply best‐subsets regression to find the best model for explaining the relationship between current salary and the other variables in the Excel file Salary Data . Use the regression tool to run your selected model, and explain all statistical results.
23. Apply best‐subsets regression to find the best model for explaining the relationship between calories and the other variables in the Excel file Cereal Data . Use the regression tool to run your selected model, and explain all statistical results.
22. Apply stepwise regression using each selection rule and p ‐value criterion to find the best models for predicting earnings/event and average score in the Golfing Statistics data. How do the stepwise models compare with your answer to problem 19?
21. Apply stepwise regression using the each selection rule and t ‐value criterion to find the best model for predicting the number of wins in the Major League Baseball data.Compare your results. How do the stepwise models compare with your answer to problem 18(c)?
20. Apply stepwise regression using each selection rule and p ‐value criterion to find a good model for predicting the number of points scored per game by football teams using the data in the Excel file National Football League . Compare your results.
19. The Excel file Golfing Statistics provides data for a portion of the 2010 professional season for the top 25 golfers.a. Find the best multiple regression model for predicting earnings/event as a function of the remaining variables.b. Find the best multiple regression model for predicting
18. The Excel file Major League Baseball provides data on the 2010 season.a. Construct and examine the correlation matrix. Is multicollinearity a potential problem? Find the variance inflation factors to check your intuition.b. Suggest an appropriate set of independent variables that predict the
17. Using the data in the Excel file Freshman College Data, identify the best regression model for predicting the first year retention rate. For the model you select, conduct further analysis to check for significance of the independent variables and for multicollinearity.
16. The Excel file Credit Approval Decisions provides information on credit history for a sample of banking customers.Use regression analysis to identify the best model for predicting the credit score as a function of the other numerical variables. For the model you select, conduct further analysis
15. The Excel file Salary Data provides information on current salary, beginning salary, previous experience (in months) when hired, and total years of education for a sample of 100 employees in a firm.a. Develop a multiple regression model for predicting current salary as a function of the other
14. The Excel file Cereal Data provides a variety of nutritional information about 67 cereals and their shelf location in a supermarket. Use regression analysis to find the best model that explains the relationship between calories and the other variables. Investigate the model assumptions and
13. For the data in the Excel file Olympic Track and Field Results, fit a multiple linear regression model using Discus Throw as the dependent variable and High Jump and Long Jump as the independent variables. How much does the fit improve over the simple linear regression which uses High Jump as
12. Consider the data in the Excel file Restaurant Sales .Develop a multiple linear regression model for the dependent variable Delivery Sales on a given day, using Lunch Sales and Dinner Sales as the independent variables. Compare the results with the simple linear regression which uses Lunch
11. Consider the data in the Excel file Restaurant Sales . Can one reasonably predict the Delivery Sales on a given day using the Lunch Sales? Run a regression analysis using Delivery Sales as the dependent variable and Lunch Sales as the independent variable. Interpret the key regression results.
10. A deep‐foundation engineering contractor has bid on a foundation system for a new world headquarters building for a Fortune 500 company. A part of the project consists of installing 311 auger cast piles. The contractor was given bid information for cost‐estimating purposes, which consisted
9. Consider the data in the Excel file Olympic Track and Field Results . The Olympic records in Discus Throw, High Jump and Long Jump all show a clear increasing trend. Can one be predicted from the others(s)? Run a regression analysis using Discus Throw as the dependent variable and High Jump as
8. The Excel file National Football League provides various data on professional football for the 2007 season.a. Construct a scatter diagram for Points/Game and Yards/Game in the Excel file. Does there appear to be a linear relationship?b. Develop a regression model for predicting Points/Game as a
7. Using the data in the Excel file China Trade Data , run a regression analysis using US exports as the dependent variable and US imports as the independent variable.Interpret the key regression results.
6. Using the data in the Excel file Banking Data , run a regression analysis using Average Bank Balance as the dependent variable and Median Years of Education as the independent variable. Interpret the key regression results.
5. Choose a stock of interest and historical daily data on its closing values for 2009 through October 1, 2010. Use the Excel file S&P 500 to find the beta risk of the stock using simple linear regression. How do you interpret your result?
4. Use the 2010 data in the Excel files S&P 500 and Google Stock Prices to find the beta risk of Google stock using simple linear regression. How do you interpret your result?
3. The managing director of a consulting group has the following monthly data on total overhead costs and professional labor hours to bill to clients: 2 Total Billable$340,000 3,000$400,000 4,000$435,000 5,000$477,000 6,000$529,000 7,000$587,000 8,000 Develop a regression model to identify the
2. For the data in China Trade Data , set up a scatter plot and add a trendline to determine if a linear relationship exists between US imports ( Y ) and US exports ( X ). Also, find the estimated imports for a year when the exports were equal to 40 (billion $).
1. Using the data in the Excel file Banking Data , check if a linear relationship exists between the variables Average Bank Balance and Median Years of Education.Construct a scatter chart and fit a trendline.
20. What is a curvilinear regression model? How do you know when to use one?
19. What is interaction, and why is it important to test for it?
18. Explain how to include categorical variables in regression models.
17. How should you balance technical criteria and practical judgment in building regression models?
16. Describe the differences and advantages/disadvantages of using stepwise and best‐subsets approaches in building regression models.
15. Describe the systematic process for building a good regression model.
14. What is multicollinearity, and how is it measured? What problems can it cause in regression results?
13. What is the difference in the hypotheses tested in single and multiple regression using the Significance F ‐statistic?
12. What are the advantages and disadvantages of using the R2 and adjusted R2 as measures of predictive fit?
11. What are partial regression coefficients, and what information do they provide?
10. Explain the assumptions of linear regression. How can you determine if each of these assumptions holds?
9. How are standard residuals calculated, and how can they help identify outliers?
8. Explain why the prediction interval is necessarily wider than the corresponding confidence interval.
7. How should you interpret the value of the dependent variable generated by the fitted regression model for a specific value of the explanatory variable?
6. What is the standard error of the estimate? What information does it provide?
5. How is the efficacy of a regression model measured?
4. How can regression analysis be applied to understand risk associated with stocks and mutual funds?
3. Does the regression line change when one switches the role of the independent and dependent variables? Why or why not?
2. What is simple linear regression? How does one find the best fitting regression line?
1. Interpret the intercept and slope parameters in the linear regression model.
31. For the data in the Excel file New Account Processing, test for the independence of gender and prior industry background.
30. For the data in the Excel file Graduate School Survey perform a chi-square test for independence to determine whether plans to attend graduate school are independent of marital status.
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