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78 6 Regressionallows you to choose other levels.) So the model isLogsulphur = a + b × Windspfor dry days, andLogsulphur = a + d + b × Windspfor wet days.The model thus consists of twoparallel regression lines (Figure6.12). The estimates show thatrainfall decreases the sulphur onaverage by 25% (antilog(–0.1240) =75%), but this effect is notstatistically significant there is stilla large amount of unexplainedvariation in the sulphurmeasurements. This version of themodel is very convenient if youwant to make comparisons with thereference level (which may, forexample, represent a standard set ofconditions or treatment). However,we show later in this section howyou can obtain the alternativeversion with a parameter in themodel for each intercept.We can see whether the lineareffect of wind speed is different inthe two categories of rainfall bya d d i n g t h e i n t e r a c t i o nWindsp.Rain.Figure 6.1213 ADD [FPROBABILITY=yes; TPROBABILITY=yes] Windsp.RainRegression analysisResponse variate: LogsulphurFitted terms: Constant + Windsp + Rain + Windsp.RainSummary of analysisSource d.f. s.s. m.s. v.r. F pr.Regression 3 1.92 0.6402 4.47 0.005Residual 108 15.47 0.1432Total 111 17.39 0.1567Change -1 -0.03 0.0323 0.23 0.636Percentage variance accounted for 8.6Standard error of observations is estimated to be 0.378.Message: the following units have large standardized residuals.Unit Response Residual98 1.633 2.61Message: the following units have high leverage.Unit Response Leverage30 0.477 0.160
6.5 Regression with groups 7972 0.699 0.11295 1.146 0.111104 1.580 0.093Estimates of parametersParameter estimate s.e. t(108) t pr.Constant 1.153 0.109 10.57
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1.1 Menus and windows 3Figure 1.3Ot
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1.1 Menus and windows 7Figure 1.11
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1.1 Menus and windows 9Lower quarti
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to obtain some information about wh
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the help page about the command PRI
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1.3 Commands 15Figure 1.26In GenSta
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2 Data structuresIn this chapter yo
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20 2 Data structuresThe factor Amou
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22 2 Data structures2.3 Forming fac
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24 2 Data structuresYou can filter
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26 3 Syntaxaltogether; default labe
Page 32 and 33: 28 3 SyntaxString tokens can always
Page 34 and 35: 30 3 Syntax3.3 PracticalUse the VAR
Page 36 and 37: 32 3 SyntaxPRINT [SERIAL=no] branch
Page 38 and 39: 34 3 Syntaxplaces in the GenStat la
Page 40: 36 3 Syntax(5) * (star) defines fac
Page 45 and 46: 4.3 Reading data from spreadsheet f
Page 47 and 48: 4.6 Practical 43GenStat adds by def
Page 49 and 50: 4.8 Custom output and captions 4513
Page 51 and 52: 5 Calculations and manipulationIn t
Page 53 and 54: 5.1 Calculations 49Most practical c
Page 55 and 56: SQRT(x) square root of x, for x 0.
Page 57 and 58: 5.1 Calculations 53pay1 pay2 pay3 p
Page 59 and 60: 5.2 Practical 55GenStat text struct
Page 61 and 62: 5.3 Subsets of data values 5750 " A
Page 63: 5.6 Practical 59Notice that any mis
Page 66 and 67: 62 6 Regressionwhere a can be inter
Page 68 and 69: 64 6 Regression2 2called the adjust
Page 70 and 71: 66 6 RegressionClicking on the Fitt
Page 72 and 73: 68 6 Regression6.2 PracticalAn abso
Page 74 and 75: 70 6 Regression7 ADD [FPROBABILITY=
Page 76 and 77: 72 6 RegressionChange -1 -0.184 0.1
Page 78 and 79: 74 6 Regression2 IMPORT 'C:/Program
Page 80 and 81: 76 6 Regression72 0.699 0.05295 1.1
Page 84 and 85: 80 6 Regression15 RDISPLAY [PRINT=a
Page 86 and 87: 7 Analysis of varianceGenStat has v
Page 88 and 89: 84 7 Analysis of varianceterms for
Page 90 and 91: 86 7 Analysis of varianceGrand mean
Page 92 and 93: 88 7 Analysis of varianceThe Residu
Page 94 and 95: 90 7 Analysis of variance8 TREATMEN
Page 96 and 97: 92 7 Analysis of varianceto 20, and
Page 98 and 99: 94 7 Analysis of variance7.7 Split-
Page 100 and 101: 96 7 Analysis of varianceStandard e
Page 102 and 103: 8 Using commands with menus or spre
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Page 106 and 107: 102 8 Using commands with menus or
Page 108 and 109: 9 Programs and proceduresThe standa
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Page 116 and 117: 112 9 Programs and procedures11 LIS
Page 118 and 119: 114 9 Programs and procedures9.7 Ot
Page 120 and 121: 10 Further informationGenStat has e
Page 122 and 123: 118 IndexGrouped data 73in regressi
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