Polynomial Trendline function flaws in Microsoft Excel

Hargreaves, Bruce R.; McWilliams, Thomas P.

doi:10.1016/j.csda.2009.10.020

“…Recent contributions to the subject make up a whole section of Computational Statistics and Data Analysis published by McCullough (2008a), in particular Yalta (2008). See also Almiron et al (2010) and Hargreaves and McWilliams (2010), as well as a very complete web site held by Heiser (2009) (where the history of Microsoft "fixes" is nicely described). In those papers and that web site, the latest versions covered are Excel 2007 and Calc 3.0.…”

Section: Generalitiesmentioning

confidence: 99%

“…It should be noted that the quality of the adjustments made on the basis of plots has also been criticized, see Hargreaves and McWilliams (2010). If we keep only problems that were not corrected in subsequent service packs, they have shown that equations of polynomial fits added to a plot can be badly formatted; that the computation of R 2 when the forced-intercept trend line is not consistent; that nonsense trend lines can easily be added for categorical X variables.…”

Section: Generalitiesmentioning

confidence: 99%

See 1 more Smart Citation

On the accuracy of statistical procedures in Microsoft Excel 2010

Mélard

¹

2014

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All previous versions of Microsoft Excel until Excel 2007 have been criticized by statisticians for several reasons, including the accuracy of statistical functions, the properties of the random number generator, the quality of statistical add-ins, the weakness of the Solver for nonlinear regression, and the data graphical representation. Until recently Microsoft did not make an attempt to fix all the errors in Excel and was still marketing a product that contained known errors. We provide an update of these studies given the recent release of Excel 2010 and we have added OpenOffice.org Calc 3.3 and Gnumeric 1.10.16 to the analysis, for the purpose of comparison. The conclusion is that the stream of papers, mainly in Computational Statistics and Data Analysis, has started to pay off: Microsoft has partially improved the statistical aspects of Excel, essentially the statistical functions and the random number generator.

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“…Though the trends in many time series can be represented by the linear , quadratic trend or exponential trend models, there are cases where the cubic time trend model is inevitably applicable [3,1]. In particular, the cubic trend model has found applications in Agronomy [19], Computational Statistics [11], Epidermology [20], Fishery [8], Meteorology [9] and Psychology [25,18].…”

Section: Introductionmentioning

confidence: 99%

Buys-Ballot Technique for the Analysis of Time Series with a Cubic-Trend Component

Okereke

¹

,

Omekara

²

,

Ekezie

³

2018

J Indian Soc Probab Stat

0

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Time series, especially those with the cubic trend component, are encountered in many data analysis situations. The decomposition of such series into various components requires a method that can adequately estimate the cubic trend as well as other components of the series. In this study, the chain base, fixed base and classical methods of decomposition of time series with the cubic trend component are discussed with emphasis on the additive model. Chain base and fixed base estimators of the additive model parameters are derived. Basic properties of these two classes of estimators are equally determined. The derived chain base variables have the autocorrelation structure of an invertible third-order moving average model. The chain base estimators are found to be pairwise-negatively correlated estimators. Though the classical method and chain base method are both used for time series decomposition, the chain base method is recommended when a case of multicollinearity has been established.

show abstract

“…In particular, the cubic trend model has found applications in Agronomy [19], Computational Statistics [11], Epidermology [20], Fishery [8], Meteorology [9] and Psychology [25,18].…”

Section: Introductionmentioning

confidence: 99%

Buys-Ballot Technique for the Analysis of Time Series with a Cubic-Trend Component

Okereke

¹

2018

Stat., optim. inf. comput.

1

0

View full text Add to dashboard Cite

Time series, especially those with the cubic trend component, are encountered in many data analysis situations. The decomposition of such series into various components requires a method that can adequately estimate the cubic trend as well as other components of the series. In this study, the chain base, fixed base and classical methods of decomposition of time series with the cubic trend component are discussed with emphasis on the additive model. Chain base and fixed base estimators of the additive model parameters are derived. Basic properties of these two classes of estimators are equally determined. The derived chain base variables have the autocorrelation structure of an invertible third-order moving average model. The chain base estimators are found to be pairwise-negatively correlated estimators. Though the classical method and chain base method are both used for time series decomposition, the chain base method is recommended when a case of multicollinearity has been established.

show abstract

Polynomial Trendline function flaws in Microsoft Excel

Cited by 18 publications

References 12 publications

On the accuracy of statistical procedures in Microsoft Excel 2010

On the accuracy of statistical procedures in Microsoft Excel 2010

Buys-Ballot Technique for the Analysis of Time Series with a Cubic-Trend Component

Buys-Ballot Technique for the Analysis of Time Series with a Cubic-Trend Component

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