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# Using Excel's regression tools to determine the 95% confidence interval

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Only for large N's (120 is large enough, but other work or projects might not be).

The more general way to get this from Excel output you gave is

Coeff +/- (Coeff-Lwr95%)

posted by ROU_Xenophobe at 2:10 PM on January 26, 2014

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# Using Excel's regression tools to determine the 95% confidence interval

January 26, 2014 12:48 PM Subscribe

MS Excel's regression tools provide 95% lower/upper confidence results but how does one properly interpret and then express those as a single ± (plus/minus) figure?

The in-house literature I am now required to read at work includes frequent references to linear trends, normally expressed as a trend per decade with a '±' figure identified as the confidence interval at 95%. The underlying datapoints used to calculate the liner trend are monthly measurements (1 per month).

I now have been requested to write a memo summarizing the "statistics" of the latest dataset of 120 months. At our office, the statistics part is accomplished using MS Excel. I have taught myself how to use the Data Analysis Tools in Excel to calculate the linear trend and other regression statistics.

So, I now have the Excel stats for the newest dataset but I am not sure how to properly express them in a way that avoids my looking like a statistics idiot (which I am). My main issue is I am not sure what to base the ± confidence interval on. The internal memos use a single number after the '±' (e.g. ±0.9) but the Excel regression analysis does not do it that simply;

Instead, Excel provides the following:

--------Coeff.---Std.Er.---Lwr95%-Upr95%-Lwr90%-Upr90%

Intcpt -0.2394, 0.0266, -0.2918, -0.1871, -0.2833, -0.1955

Data.X 0.0013, 0.0002,, 0.0009,, 0.0017 ,, 0.0009 ,, 0.0016

I know that the 0.0013 is the slope of the linear trend of monthly observations and to calculate a 10-year trend I would multiply by 120 (months). But it is not clear what numbers I would use to calculate a single '±' that is interpreted as the 95% confidence interval.

Are Excel's 'Lower' and 'Upper' 95% results the basis for the confidence interval? If so, which are the correct numbers to use, and would I then multiply that correct number(s) by 120 as done with the slope coefficient when expressed as a 10-year trend?

Thanks for clearing up an idiot's confusion on this. (Apologize for extra commas used to force columns of data to line up.)

The in-house literature I am now required to read at work includes frequent references to linear trends, normally expressed as a trend per decade with a '±' figure identified as the confidence interval at 95%. The underlying datapoints used to calculate the liner trend are monthly measurements (1 per month).

I now have been requested to write a memo summarizing the "statistics" of the latest dataset of 120 months. At our office, the statistics part is accomplished using MS Excel. I have taught myself how to use the Data Analysis Tools in Excel to calculate the linear trend and other regression statistics.

So, I now have the Excel stats for the newest dataset but I am not sure how to properly express them in a way that avoids my looking like a statistics idiot (which I am). My main issue is I am not sure what to base the ± confidence interval on. The internal memos use a single number after the '±' (e.g. ±0.9) but the Excel regression analysis does not do it that simply;

Instead, Excel provides the following:

--------Coeff.---Std.Er.---Lwr95%-Upr95%-Lwr90%-Upr90%

Intcpt -0.2394, 0.0266, -0.2918, -0.1871, -0.2833, -0.1955

Data.X 0.0013, 0.0002,, 0.0009,, 0.0017 ,, 0.0009 ,, 0.0016

I know that the 0.0013 is the slope of the linear trend of monthly observations and to calculate a 10-year trend I would multiply by 120 (months). But it is not clear what numbers I would use to calculate a single '±' that is interpreted as the 95% confidence interval.

Are Excel's 'Lower' and 'Upper' 95% results the basis for the confidence interval? If so, which are the correct numbers to use, and would I then multiply that correct number(s) by 120 as done with the slope coefficient when expressed as a 10-year trend?

Thanks for clearing up an idiot's confusion on this. (Apologize for extra commas used to force columns of data to line up.)

For 95%= 1.96*standard error is the +/- that you are looking for.

That will change for 90% 1.645*standard error.

So for the Data X . 0013 +/- .0004. You get upper 95% by [1.96(~2)*.0002] or.004+0013=.0017,Lower 95% .0013-.0004 = .0009

posted by radsqd at 1:48 PM on January 26, 2014 [1 favorite]

That will change for 90% 1.645*standard error.

So for the Data X . 0013 +/- .0004. You get upper 95% by [1.96(~2)*.0002] or.004+0013=.0017,Lower 95% .0013-.0004 = .0009

posted by radsqd at 1:48 PM on January 26, 2014 [1 favorite]

*For 95%= 1.96*standard error is the +/- that you are looking for.*

Only for large N's (120 is large enough, but other work or projects might not be).

The more general way to get this from Excel output you gave is

Coeff +/- (Coeff-Lwr95%)

posted by ROU_Xenophobe at 2:10 PM on January 26, 2014

Thanks, all. I was able to get my hands on an older dataset and then ran it through Excel. I then compared those regression results with previous internal memo confirming that indeed previous authors had expressed the confidence interval '±' using the same techniques suggested.

Your responses completely cleared my confusion and provided a needed comfort level.

One follow up question. If I multiply the slope coefficient by 120 to calculate an estimated 10-year trend, would I do the same for the '±' figure. Using example, ±0.004 would become ±0.48 - sound right?

Thanks again.

posted by kartguy at 9:54 AM on January 27, 2014

Your responses completely cleared my confusion and provided a needed comfort level.

One follow up question. If I multiply the slope coefficient by 120 to calculate an estimated 10-year trend, would I do the same for the '±' figure. Using example, ±0.004 would become ±0.48 - sound right?

Thanks again.

posted by kartguy at 9:54 AM on January 27, 2014

This thread is closed to new comments.

posted by Hollywood Upstairs Medical College at 12:56 PM on January 26, 2014