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Time Series Forecasting and Index Numbers
1.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-1 Chapter 15 Time-Series Forecasting and Index Numbers Statistics for Managers Using Microsoft® Excel 4th Edition
2.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-2 Chapter Goals After completing this chapter, you should be able to: Develop and implement basic forecasting models Identify the components present in a time series Use smoothing-based forecasting models, including moving average and exponential smoothing Apply trend-based forecasting models, including linear trend and nonlinear trend complete time-series forecasting of seasonal data compute and interpret basic index numbers
3.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-3 The Importance of Forecasting Governments forecast unemployment, interest rates, and expected revenues from income taxes for policy purposes Marketing executives forecast demand, sales, and consumer preferences for strategic planning College administrators forecast enrollments to plan for facilities and for faculty recruitment Retail stores forecast demand to control inventory levels, hire employees and provide training
4.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-4 Time-Series Data Numerical data obtained at regular time intervals The time intervals can be annually, quarterly, daily, hourly, etc. Example: Year: 1999 2000 2001 2002 2003 Sales: 75.3 74.2 78.5 79.7 80.2
5.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-5 Time-Series Plot the vertical axis measures the variable of interest the horizontal axis corresponds to the time periods U.S. Inflation Rate 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 Year InflationRate(%) A time-series plot is a two-dimensional plot of time series data
6.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-6 Time-Series Components Time Series Cyclical Component Irregular Component Trend Component Seasonal Component
7.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-7 Upward trend Trend Component Long-run increase or decrease over time (overall upward or downward movement) Data taken over a long period of time Sales Time
8.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-8 Downward linear trend Trend Component Trend can be upward or downward Trend can be linear or non-linear Sales Time Upward nonlinear trend Sales Time (continued)
9.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-9 Seasonal Component Short-term regular wave-like patterns Observed within 1 year Often monthly or quarterly Sales Time (Quarterly) Winter Spring Summer Fall Winter Spring Summer Fall
10.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-10 Cyclical Component Long-term wave-like patterns Regularly occur but may vary in length Often measured peak to peak or trough to trough Sales 1 Cycle Year
11.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-11 Irregular Component Unpredictable, random, “residual” fluctuations Due to random variations of Nature Accidents or unusual events “Noise” in the time series
12.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-12 Multiplicative Time-Series Model for Annual Data Used primarily for forecasting Observed value in time series is the product of components where Ti = Trend value at year i Ci = Cyclical value at year i Ii = Irregular (random) value at year i iiii ICTY ××=
13.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-13 Multiplicative Time-Series Model with a Seasonal Component Used primarily for forecasting Allows consideration of seasonal variation where Ti = Trend value at time i Si = Seasonal value at time i Ci = Cyclical value at time i Ii = Irregular (random) value at time i iiiii ICSTY ×××=
14.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-14 Smoothing the Annual Time Series Calculate moving averages to get an overall impression of the pattern of movement over time Moving Average: averages of consecutive time series values for a chosen period of length L
15.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-15 Moving Averages Used for smoothing A series of arithmetic means over time Result dependent upon choice of L (length of period for computing means) Examples: For a 5 year moving average, L = 5 For a 7 year moving average, L = 7 Etc.
16.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-16 Moving Averages Example: Five-year moving average First average: Second average: etc. (continued) 5 YYYYY MA(5) 54321 ++++ = 5 YYYYY MA(5) 65432 ++++ =
17.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-17 Example: Annual Data Year Sales 1 2 3 4 5 6 7 8 9 10 11 etc… 23 40 25 27 32 48 33 37 37 50 40 etc… Annual Sales 0 10 20 30 40 50 60 1 2 3 4 5 6 7 8 9 10 11 Year Sales … …
18.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-18 Calculating Moving Averages Each moving average is for a consecutive block of 5 years Year Sales 1 23 2 40 3 25 4 27 5 32 6 48 7 33 8 37 9 37 10 50 11 40 Average Year 5-Year Moving Average 3 29.4 4 34.4 5 33.0 6 35.4 7 37.4 8 41.0 9 39.4 … … 5 54321 3 ++++ = 5 3227254023 29.4 ++++ = etc…
19.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-19 Annual vs. 5-Year Moving Average 0 10 20 30 40 50 60 1 2 3 4 5 6 7 8 9 10 11 Year Sales Annual 5-Year Moving Average Annual vs. Moving Average The 5-year moving average smoothes the data and shows the underlying trend
20.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-20 Exponential Smoothing A weighted moving average Weights decline exponentially Most recent observation weighted most Used for smoothing and short term forecasting (often one period into the future)
21.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-21 Exponential Smoothing The weight (smoothing coefficient) is W Subjectively chosen Range from 0 to 1 Smaller W gives more smoothing, larger W gives less smoothing The weight is: Close to 0 for smoothing out unwanted cyclical and irregular components Close to 1 for forecasting (continued)
22.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-22 Exponential Smoothing Model Exponential smoothing model 11 YE = 1iii E)W1(WYE −−+= where: Ei = exponentially smoothed value for period i Ei-1 = exponentially smoothed value already computed for period i - 1 Yi = observed value in period i W = weight (smoothing coefficient), 0 < W < 1 For i = 2, 3, 4, …
23.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-23 Exponential Smoothing Example Suppose we use weight W = .2 Time Period (i) Sales (Yi) Forecast from prior period (Ei-1) Exponentially Smoothed Value for this period (Ei) 1 2 3 4 5 6 7 8 9 10 etc. 23 40 25 27 32 48 33 37 37 50 etc. -- 23 26.4 26.12 26.296 27.437 31.549 31.840 32.872 33.697 etc. 23 (.2)(40)+(.8)(23)=26.4 (.2)(25)+(.8)(26.4)=26.12 (.2)(27)+(.8)(26.12)=26.296 (.2)(32)+(.8)(26.296)=27.437 (.2)(48)+(.8)(27.437)=31.549 (.2)(48)+(.8)(31.549)=31.840 (.2)(33)+(.8)(31.840)=32.872 (.2)(37)+(.8)(32.872)=33.697 (.2)(50)+(.8)(33.697)=36.958 etc. 1ii i E)W1(WY E −−+ = E1 = Y1 since no prior information exists
24.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-24 Sales vs. Smoothed Sales Fluctuations have been smoothed NOTE: the smoothed value in this case is generally a little low, since the trend is upward sloping and the weighting factor is only .2 0 10 20 30 40 50 60 1 2 3 4 5 6 7 8 9 10 Time Period Sales Sales Smoothed
25.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-25 Forecasting Time Period i + 1 The smoothed value in the current period (i) is used as the forecast value for next period (i + 1) : i1i EYˆ =+
26.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-26 Exponential Smoothing in Excel Use tools / data analysis / exponential smoothing The “damping factor” is (1 - W)
27.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-27 Trend-Based Forecasting Estimate a trend line using regression analysis Year Time Period (X) Sales (Y) 1999 2000 2001 2002 2003 2004 0 1 2 3 4 5 20 40 30 50 70 65 XbbYˆ 10 += Use time (X) as the independent variable:
28.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-28 Trend-Based Forecasting The linear trend forecasting equation is: Sales trend 0 10 20 30 40 50 60 70 80 0 1 2 3 4 5 6 Year sales Year Time Period (X) Sales (Y) 1999 2000 2001 2002 2003 2004 0 1 2 3 4 5 20 40 30 50 70 65 ii X9.571421.905Yˆ += (continued)
29.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-29 Trend-Based Forecasting Forecast for time period 6: Year Time Period (X) Sales (y) 1999 2000 2001 2002 2003 2004 2005 0 1 2 3 4 5 6 20 40 30 50 70 65 ?? (continued) Sales trend 0 10 20 30 40 50 60 70 80 0 1 2 3 4 5 6 Year sales 79.33 (6)9.571421.905Yˆ = +=
30.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-30 Nonlinear Trend Forecasting A nonlinear regression model can be used when the time series exhibits a nonlinear trend Quadratic form is one type of a nonlinear model: Compare adj. r2 and standard error to that of linear model to see if this is an improvement Can try other functional forms to get best fit i 2 i2i10i XXY ε+β+β+β=
31.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-31 Exponential Trend Model Another nonlinear trend model: Transform to linear form: i X 10i εββY i = )εlog()log(βX)βlog()log(Y i1i0i ++=
32.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-32 Exponential Trend Model Exponential trend forecasting equation: i10i Xbb)Yˆlog( += where b0 = estimate of log(β0) b1 = estimate of log(β1) (continued) Interpretation: %100)1βˆ( 1 ×− is the estimated annual compound growth rate (in %)
33.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-33 Model Selection Using Differences Use a linear trend model if the first differences are approximately constant Use a quadratic trend model if the second differences are approximately constant )YY()YY()Y(Y 1-nn2312 −==−=− )]YY()Y[(Y )]YY()Y[(Y)]YY()Y[(Y 2-n1-n1-nn 23341223 −−−== −−−=−−−
34.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-34 Use an exponential trend model if the percentage differences are approximately constant (continued) Model Selection Using Differences %100 Y )Y(Y %100 Y )Y(Y %100 Y )Y(Y 1-n 1-nn 2 23 1 12 × − ==× − =× −
35.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-35 ip-ip2-i21-i10i δYAYAYAAY ++++++= Autoregressive Modeling Used for forecasting Takes advantage of autocorrelation 1st order - correlation between consecutive values 2nd order - correlation between values 2 periods apart pth order Autoregressive models: Random Error
36.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-36 Autoregressive Model: Example Year Units 97 4 98 3 99 2 00 3 01 2 02 2 03 4 04 6 The Office Concept Corp. has acquired a number of office units (in thousands of square feet) over the last eight years. Develop the second order Autoregressive model.
37.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-37 Autoregressive Model: Example Solution Year Yi Yi-1 Yi-2 97 4 -- -- 98 3 4 -- 99 2 3 4 00 3 2 3 01 2 3 2 02 2 2 3 03 4 2 2 04 6 4 2 Coefficients Intercept 3.5 X Variable 1 0.8125 X Variable 2 -0.9375 Excel Output Develop the 2nd order table Use Excel to estimate a regression model 2i1ii 0.9375Y0.8125Y3.5Yˆ −− −+=
38.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-38 Autoregressive Model Example: Forecasting Use the second-order equation to forecast number of units for 2005: 625.4 )0.9375(4)0.8125(63.5 )0.9375(Y)0.8125(Y3.5Yˆ 0.9375Y0.8125Y3.5Yˆ 200320042005 2i1ii = −+= −+= −+= −−
39.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-39 Autoregressive Modeling Steps 1. Choose p (note that df = n – 2p – 1) 2. Form a series of “lagged predictor” variables Yi-1 , Yi-2 , … ,Yi-p 3. Use Excel to run regression model using all p variables 4. Test significance of Ap If null hypothesis rejected, this model is selected If null hypothesis not rejected, decrease p by 1 and repeat
40.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-40 Selecting A Forecasting Model Perform a residual analysis Look for pattern or direction Measure magnitude of residual error using squared differences Measure residual error using MAD Use simplest model Principle of parsimony
41.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-41 Residual Analysis Random errors Trend not accounted for Cyclical effects not accounted for Seasonal effects not accounted for T T T T e e e e 0 0 0 0
42.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-42 Measuring Errors Choose the model that gives the smallest measuring errors Mean Absolute Deviation (MAD) Not sensitive to extreme observations Sum of squared errors (SSE) Sensitive to outliers ∑= −= n 1i 2 ii )Yˆ(YSSE n YˆY MAD n 1i ii∑= − =
43.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-43 Principal of Parsimony Suppose two or more models provide a good fit for the data Select the simplest model Simplest model types: Least-squares linear Least-squares quadratic 1st order autoregressive More complex types: 2nd and 3rd order autoregressive Least-squares exponential
44.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-44 Recall the classical time series model with seasonal variation: Suppose the seasonality is quarterly Define three new dummy variables for quarters: Q1 = 1 if first quarter, 0 otherwise Q2 = 1 if second quarter, 0 otherwise Q3 = 1 if third quarter, 0 otherwise (Quarter 4 is the default if Q1 = Q2 = Q3 = 0) iiiii ICSTY ×××= Forecasting With Seasonal Data
45.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-45 Exponential Model with Quarterly Data Transform to linear form: i Q 4 Q 3 Q 2 X 10i εβββββY 321i = )εlog()log(βQ)log(βQ )log(βQ)log(βX)βlog()log(Y i4332 211i0i +++ ++= βi provides the multiplier for the ith quarter relative to the 4th quarter (i = 2, 3, 4)
46.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-46 Estimating the Quarterly Model Exponential forecasting equation: 342312i10i QbQbQbXbb)Yˆlog( ++++= where b0 = estimate of log(β0), so b1 = estimate of log(β1), so etc… Interpretation: %100)1βˆ( 1 ×− = estimated quarterly compound growth rate (in %) = estimated multiplier for first quarter relative to fourth quarter = estimated multiplier for second quarter rel. to fourth quarter = estimated multiplier for third quarter relative to fourth quarter 0 b βˆ10 0 = 1 b βˆ10 1 = 2βˆ 3βˆ 4βˆ
47.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-47 Quarterly Model Example Suppose the forecasting equation is: 321ii .022Q.073QQ082..017X3.43)Yˆlog( +−−+= b0 = 3.43, so b1 = .017, so b2 = -.082, so b3 = -.073, so b4 = .022, so 53.2691βˆ10 0 b0 == 040.1βˆ10 1 b1 == 827.0βˆ10 2 b2 == 845.0βˆ10 3 b3 == 052.1βˆ10 4 b4 ==
48.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-48 Quarterly Model Example Interpretation: 53.2691βˆ 0 = 040.1βˆ 1 = 827.0βˆ 2 = 845.0βˆ 3 = 052.1βˆ 4 = Unadjusted trend value for first quarter of first year 4.0% = estimated quarterly compound growth rate Ave. sales in Q2 are 82.7% of average 4th quarter sales, after adjusting for the 4% quarterly growth rate Ave. sales in Q3 are 84.5% of average 4th quarter sales, after adjusting for the 4% quarterly growth rate Ave. sales in Q4 are 105.2% of average 4th quarter sales, after adjusting for the 4% quarterly growth rate Value: (continued)
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Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-49 Index Numbers Index numbers allow relative comparisons over time Index numbers are reported relative to a Base Period Index Base period index = 100 by definition Used for an individual item or measurement
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Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-50 Simple Price Index Simple Price Index: 100 P P I base i i ×= where Ii = index number for year i Pi = price for year i Pbase = price for the base year
51.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-51 Index Numbers: Example Airplane ticket prices from 1995 to 2003: 90)100( 320 288 100 P P I 2000 1996 1996 ==×= Year Price Index (base year = 2000) 1995 272 85.0 1996 288 90.0 1997 295 92.2 1998 311 97.2 1999 322 100.6 2000 320 100.0 2001 348 108.8 2002 366 114.4 2003 384 120.0 100)100( 320 320 100 P P I 2000 2000 2000 ==×= 120)100( 320 384 100 P P I 2000 2003 2003 ==×= Base Year:
52.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-52 Prices in 1996 were 90% of base year prices Prices in 2000 were 100% of base year prices (by definition, since 2000 is the base year) Prices in 2003 were 120% of base year prices Index Numbers: Interpretation 90)100( 320 288 100 P P I 2000 1996 1996 ==×= 100)100( 320 320 100 P P I 2000 2000 2000 ==×= 120)100( 320 384 100 P P I 2000 2003 2003 ==×=
53.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-53 Aggregate Price Indexes An aggregate index is used to measure the rate of change from a base period for a group of items Aggregate Price Indexes Unweighted aggregate price index Weighted aggregate price indexes Paasche Index Laspeyres Index
54.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-54 Unweighted Aggregate Price Index Unweighted aggregate price index formula: 100 P P I n 1i )0( i n 1i )t( i )t( U ×= ∑ ∑ = = = unweighted price index at time t = sum of the prices for the group of items at time t = sum of the prices for the group of items in time period 0∑ ∑ = = n 1i )0( i n 1i )t( i )t( U P P I i = item t = time period n = total number of items
55.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-55 Unweighted total expenses were 18.8% higher in 2004 than in 2001 Automobile Expenses: Monthly Amounts ($): Year Lease payment Fuel Repair Total Index (2001=100) 2001 260 45 40 345 100.0 2002 280 60 40 380 110.1 2003 305 55 45 405 117.4 2004 310 50 50 410 118.8 Unweighted Aggregate Price Index: Example 118.8(100) 345 410 100 P P I 2001 2004 2004 ==×= ∑ ∑
56.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-56 Weighted Aggregate Price Indexes Paasche index 100 QP QP I n 1i )t( i )0( i n 1i )t( i )t( i )t( P ×= ∑ ∑ = = : weights based on : weights based on current period 0 quantities period quantities = price in time period t = price in period 0 100 QP QP I n 1i )0( i )0( i n 1i )0( i )t( i )t( L ×= ∑ ∑ = = Laspeyres index )0( iQ )t( iQ )t( iP )0( iP
57.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-57 Common Price Indexes Consumer Price Index (CPI) Producer Price Index (PPI) Stock Market Indexes Dow Jones Industrial Average S&P 500 Index NASDAQ Index
58.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-58 Pitfalls in Time-Series Analysis Assuming the mechanism that governs the time series behavior in the past will still hold in the future Using mechanical extrapolation of the trend to forecast the future without considering personal judgments, business experiences, changing technologies, and habits, etc.
59.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-59 Chapter Summary Discussed the importance of forecasting Addressed component factors of the time-series model Performed smoothing of data series Moving averages Exponential smoothing Described least square trend fitting and forecasting Linear, quadratic and exponential models
60.
Statistics for Managers
Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 15-60 Chapter Summary Addressed autoregressive models Described procedure for choosing appropriate models Addressed time series forecasting of monthly or quarterly data (use of dummy variables) Discussed pitfalls concerning time-series analysis (continued)
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