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The phrase "due to rounding differences" is correct and can be used in written English
It is most often used to indicate that discrepancies between two numbers or values are the result of rounding. For example: "There was a discrepancy of 8 cents between the two calculations but it can be attributed to rounding differences."
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All p values are derived via Fisher's exact test *Total number adds up to over 100% due to overlap in pathogens detected Does not add up to 100% due to rounding Unadjusted outcomes differed significantly between the two groups.
Both methods yield essentially the same results, slight differences are presumably due to rounding errors and/or to limitations of the data fitting algorithms used, as well as possible over-fitting in the case of the Adair-Klotz framework.
b Due to rounding figures shown will not always equal the difference between predicted percentages for the public's recommendations of what should be done.
b Due to rounding figures shown will not always equal the difference between predicted percentages for the public's definition of depressive symptoms.
This proportion decreases to 13.3 % for primary and secondary referral hospitals, and to 10%% for third-tier referral hospitals (Table 3).> -wrap-foot> Dinference in total sums are due to rounding We then used the distribution factors obtained through this calculation to parcel out non-targeted public expenditures to different providers for different services.
Differences to 100%% per row are due to rounding Table 2 shows the multinomial logistic regression coefficients as odds ratios (OR) between disapprovers of both WLPT and EUT (= reference category), and those who accept WLPT alone on the one hand, and those who accept EUT (and WLPT) on the other hand.
I redid the "omnibus" ANOVA that Ben carried out and got practically the same result, F (5, 45518) = 2.90, p =.013, any slight difference from Ben's numbers are probably due to rounding error.
In practice there were small (<0.8 %) differences in the patient distributions that were considered to be due to rounding errors and to the assumption that the post-hoc analysis represents the true prevalence rate for ROM.
This will be true in general, and more formally, the linear regression model The difference in the last digit of Total SS between the ANOVA and regression analysis is due to rounding error.
Total 333 99.9aPercentagetage does not add up to 100% due to rounding bPseudonym.
These operations introduce some information loss during compression due to rounding errors.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com