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Showing 1 to 15 of 3445 results matching Simple Linear Regression
Name/Notes Owner Created Size Views
Simple linear regression results:
Dependent Variable: Hours Spent Commuting
Independent Variable: Hours Worked per week
Hours Spent Commuting = 20.221381 - 0.10622974 Hours Worked per week
Sample size: 40
R (correlation coefficient) = -0.091322535
R-sq = 0.0083398053
Estimate of error standard deviation: 11.032254

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept20.2213813.5862674 ≠ 0385.6385592<0.0001
Slope-0.106229740.18791342 ≠ 038-0.565312140.5752

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model138.89601838.8960180.319577820.5752
Error384625.004121.71063
Total394663.9
Hours Spent Commuting & Hours Worked Per WeekSimple Linear Regression
sadecasey@yahoo.comNov 22, 20173KB2
Simple linear regression results:
Dependent Variable: Hours Spent Commuting
Independent Variable: Age
Hours Spent Commuting = 8.5462146 + 0.42505517 Age
Sample size: 40
R (correlation coefficient) = 0.30213093
R-sq = 0.091283102
Estimate of error standard deviation: 10.560807

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept8.54621465.3370184 ≠ 0381.60130880.1176
Slope0.425055170.21755677 ≠ 0381.95376660.0581

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model1425.73526425.735263.81720410.0581
Error384238.1647111.53065
Total394663.9
Age & Hours Spent Commuting Simple Linear Regression
sadecasey@yahoo.comNov 22, 20173KB2
Simple linear regression results:
Dependent Variable: Hours Worked per week
Independent Variable: Age
Hours Worked per week = 18.356943 - 0.072186386 Age
Sample size: 40
R (correlation coefficient) = -0.059686119
R-sq = 0.0035624328
Estimate of error standard deviation: 9.5069173

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept18.3569434.8044237 ≠ 0383.82084180.0005
Slope-0.0721863860.19584623 ≠ 038-0.368587070.7145

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model112.27890412.2789040.135856430.7145
Error383434.496190.381476
Total393446.775
Age & Hours Worked Per Week Simple Linear Regression
sadecasey@yahoo.comNov 22, 20173KB4
Simple linear regression results:
Dependent Variable: Weight (lbs)
Independent Variable: Height (in)
Weight (lbs) = -186.47059 + 4.7058824 Height (in)
Sample size: 8
R (correlation coefficient) = 0.79792117
R-sq = 0.6366782
Estimate of error standard deviation: 10.145993

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-186.4705994.582651 ≠ 06-1.97150940.0961
Slope4.70588241.4512799 ≠ 063.24257390.0176

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model11082.35291082.352910.5142860.0176
Error6617.64706102.94118
Total71700
Height (in) & Weight (ibs) Simple Linear Regression
sadecasey@yahoo.comNov 20, 20173KB5
Simple linear regression results:
Dependent Variable: var2
Independent Variable: var1
var2 = 87.109756 - 0.2804878 var1
Sample size: 16
R (correlation coefficient) = -0.88823106
R-sq = 0.78895441
Estimate of error standard deviation: 2.0015238

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept87.1097562.8074192 ≠ 01431.028411<0.0001
Slope-0.28048780.03877146 ≠ 014-7.2343885<0.0001

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model1209.66463209.6646352.336377<0.0001
Error1456.0853664.0060976
Total15265.75
Simple Linear Regression
jb61001Nov 19, 20173KB5
Simple linear regression results:
Dependent Variable: Frequency
Independent Variable: Number of Cats
Frequency = 12.6 - 2.4 Number of Cats
Sample size: 4
R (correlation coefficient) = -0.77459667
R-sq = 0.6
Estimate of error standard deviation: 3.0983867

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept12.62.5922963 ≠ 024.86055550.0398
Slope-2.41.3856406 ≠ 02-1.73205080.2254

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model128.828.830.2254
Error219.29.6
Total348
Simple Linear Regression
26031361_ecollege_bakerNov 18, 20173KB2
Simple Linear Regressionlastbillsfanstanding@aol.comNov 16, 2017174B3
Simple linear regression results:
Dependent Variable: Female Height
Independent Variable: Female Age
Female Height = 59.995455 + 0.16363636 Female Age
Sample size: 40
R (correlation coefficient) = 0.14476405
R-sq = 0.020956631
Estimate of error standard deviation: 2.6911604

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept59.9954553.7437391 ≠ 03816.025544<0.0001
Slope0.163636360.181438 ≠ 0380.901885850.3728

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model15.89090915.89090910.813398080.3728
Error38275.209097.2423445
Total39281.1
Simple Linear Regression
lupitamondragon54@gmail.comNov 14, 20173KB9
Simple linear regression results:
Dependent Variable: Female Weight
Independent Variable: Female Age
Female Weight = 84.695455 + 2.3636364 Female Age
Sample size: 40
R (correlation coefficient) = 0.29055258
R-sq = 0.084420803
Estimate of error standard deviation: 18.729381

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept84.69545526.0549 ≠ 0383.25065360.0024
Slope2.36363641.2627346 ≠ 0381.87183940.0689

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model11229.09091229.09093.50378260.0689
Error3813330.009350.78971
Total3914559.1
Simple Linear Regression
lupitamondragon54@gmail.comNov 14, 20173KB3
Simple linear regression results:
Dependent Variable: Female Weight
Independent Variable: Female Height
Female Weight = -71.909285 + 3.2369264 Female Height
Sample size: 40
R (correlation coefficient) = 0.44977569
R-sq = 0.20229817
Estimate of error standard deviation: 17.482187

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept-71.90928566.113762 ≠ 038-1.08765990.2836
Slope3.23692641.0427143 ≠ 0383.10432710.0036

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model12945.27932945.27939.6368470.0036
Error3811613.821305.62686
Total3914559.1
Simple Linear Regression
lupitamondragon54@gmail.comNov 14, 20173KB5
Simple linear regression results:
Dependent Variable: Weight
Independent Variable: Age
Weight = 105.74872 + 1.8606681 Age
Sample size: 40
R (correlation coefficient) = 0.28594086
R-sq = 0.081762177
Estimate of error standard deviation: 20.615727

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept105.7487221.115901 ≠ 0385.0080136<0.0001
Slope1.86066811.0115293 ≠ 0381.83946040.0737

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model11438.06381438.06383.38361440.0737
Error3816150.311425.00819
Total3917588.375
Age & Weight Simple Linear Regression
mozquedai@students.trinitydc.eduNov 14, 20173KB4
Simple linear regression results:
Dependent Variable: Weight
Independent Variable: Height(in.)
Weight = 8.292463 + 2.1655247 Height(in.)
Sample size: 40
R (correlation coefficient) = 0.23548099
R-sq = 0.055451296
Estimate of error standard deviation: 20.908999

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept8.29246391.002875 ≠ 0380.0911230880.9279
Slope2.16552471.4498654 ≠ 0381.4936040.1435

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model1975.29819975.298192.23085290.1435
Error3816613.077437.18623
Total3917588.375
Height & Weight Simple Linear Regression
mozquedai@students.trinitydc.eduNov 14, 20173KB4
Simple linear regression results:
Dependent Variable: Height(in.)
Independent Variable: Age
Height(in.) = 60.74505 + 0.095997593 Age
Sample size: 40
R (correlation coefficient) = 0.13566723
R-sq = 0.018405597
Estimate of error standard deviation: 2.3178205

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept60.745052.374055 ≠ 03825.587044<0.0001
Slope0.0959975930.11372596 ≠ 0380.844113240.4039

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model13.8279043.8279040.712527170.4039
Error38204.14715.372292
Total39207.975
Age & Height Simple Linear Regression
mozquedai@students.trinitydc.eduNov 14, 20173KB4
Simple linear regression results:
Dependent Variable: Cost
Independent Variable: Total Student Population
Cost = 46225.923 - 0.029189982 Total Student Population
Sample size: 98
R (correlation coefficient) = -0.031224727
R-sq = 0.00097498359
Estimate of error standard deviation: 9809.4697

Parameter estimates:
ParameterEstimateStd. Err.AlternativeDFT-StatP-value
Intercept46225.9231222.731 ≠ 09637.805472<0.0001
Slope-0.0291899820.095364722 ≠ 096-0.306087850.7602

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model19015363.39015363.30.0936897710.7602
Error969.2376667e996225695
Total979.2466821e9
Simple Linear Regression 1
vta15@my.fsu.eduNov 12, 20173KB4
Simple linear regression results:
Dependent Variable: Cost
Independent Variable: Total Student Population
Cost = 46225.923 - 0.029189982 Total Student Population
Sample size: 98
R (correlation coefficient) = -0.031224727
R-sq = 0.00097498359
Estimate of error standard deviation: 9809.4697

Parameter estimates:
ParameterEstimateStd. Err.DF95% L. Limit95% U. Limit
Intercept46225.9231222.7319643798.82148653.025
Slope-0.0291899820.09536472296-0.218487460.16010749

Analysis of variance table for regression model:
SourceDFSSMSF-statP-value
Model19015363.39015363.30.0936897710.7602
Error969.2376667e996225695
Total979.2466821e9
Simple Linear Regression
vta15@my.fsu.eduNov 12, 20173KB4

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