77-888 exam Dumps Source : Excel 2010 Expert

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experience Date:

March 2, 2019 - 9:30amby Kim Harris

The 19th annual Snowshoe Thompson Ski and Snowshoe party returns once again to the Sierra on Saturday, March 2 at 9:30 a.m. at the Lake Tahoe Golf course in South Lake Tahoe.

experience actions commence with a guided tour over the snow beginning at 9:30 a.m. with Norwegian native and longboard ski expert Nina MacLeod, who will additionally give a longboard ski demonstration. Attendees may carry their personal snowshoes and spin country skis, or rent snowshoes from the Lake Tahoe Golf direction.

other activities include a ebook signing via author frank Tortorich; a efficiency by musician and storyteller Richard Blair; a presentation by Snowshoe Thompson skilled and Alpine County resident Don Jardine on Snowshoe Thompson’s presence in Alpine County; and a video presentation with the aid of Snowshoe Thompson Chapter No. 1827 of E Clampus Vitus’ Humbug Brandon Wilding on his trip to Norway in 2018 and principally the household home of Snowshoe Thompson.

apart from entertainers and demonstrations, the Lake Tahoe historic Society will hold on pomp a several Snowshoe Thompson show. different displays consist of those of the pals of Snowshoe Thompson firm, The Snowshoe Thompson Chapter No. 1827 of E Clampus Vitus, Mormon Station condition ancient Park, Alpine County ancient Museum and the El Dorado County faded Society.

The legendary "Mailman of the Sierra," John A. "Snowshoe" Thompson carried mail between Placerville, CA, and Genoa, NV, for two decades, twice a month throughout the winters between 1856 and 1876, on snowshoes, or what nowadays they know easily as "skis."

The nineteenth Annual Ski and Snowshoe occasion is a family-friendly suffer with a purpose to purchase location between 9:30 a.m. and 1 p.m., with registration from 8:30 to 9 a.m.

The requested donation is $10 per person which contains total actions. There is not any cost for activities for children below 12 years of age. A bag lunch can besides exist reserved for attendees for an further $10. A no-host bar will besides exist obtainable.

To RSVP most importantly for bag lunch reservations and for extra tips, please contact Nina at 530-573-8940 or through email to norskenina@sbcglobal.net.

The Lake Tahoe Golf path is observed at 2500 Emerald Bay highway, South Lake Tahoe, CA 96150.

This annual suffer is offered with the aid of the Genoa, NV-based friends of Snowshoe Thompson solid who are once again partnering with the Lake Tahoe historical Society and the Lake Tahoe Golf course.

REDMOND, Wash., and SALT LAKE metropolis — Nov. 18, 2014 — Microsoft Corp. and Certiport Inc., a Pearson VUE business, on Monday announced the dates for the 2015 Microsoft workplace expert (MOS) World Championship, the most established expertise skills competition for Microsoft office due to the fact that its inception in 2002. The established Microsoft office competition is the only one endorsed through Microsoft, presenting a platform for college kids to exhibit their knowledgeable advantage the usage of Microsoft workplace tools and profit the higher hand in these days’s competitive educational and expert industry.

The 2015 contest marks the introduction of Microsoft workplace specialist 2013 tests to the competition. Certiport will award better scholarship prizes for 2013 exam competitors tracks. In all, college students will compete in Microsoft notice, outdo and PowerPoint tracks for workplace 2010 and 2013.

“Our workplace community is delighted to see the fire and dedication of the younger rivals within the MOS World Championship,” preeminent John Case, company vice president for Microsoft workplace. “they could basically withhold the office suite through its paces, and those capabilities will serve them neatly in whatever thing profession they ultimately pursue.”

The championship, coming into its thirteenth year, will see countries from everywhere host local competitions to check country and regional representatives in line with exam ratings and completion instances. The closing date for total qualifying exams is June 15, 2015. selected finalists will then compete within the 2015 MOS World Championship held in Dallas, Texas, Aug. 9–12, 2015.

“We are expecting continued growth during this high-stakes competition — closing yr they had just about 750,000 exam entries, and greater than forty countries participated within the MOS World Championship,” said Bob Whelan, president and CEO for Pearson VUE. “The event is greater than enjoyable and prizes; Microsoft office professional certification prepares students for future success in college and careers. within the conclusion any one who earns certification is a winner with a useful credential on their resume to reveal their abilities.”

To purchase part, students ages 13 to 22 should withhold up a passing score on the MOS notice 2013, outdo 2013, PowerPoint 2013, exist watchful 2010, outdo 2010 or PowerPoint 2010 exams and attend an authorized, authorized getting to know institution. Regional participation and availability varies via nation. extra details can besides exist acquired via contacting a native Certiport respond issuer or contacting Certiport at MOSchampionship@pearson.com. The Microsoft workplace professional certification is the best reliable Microsoft-recognized certification for Microsoft workplace globally.

This year the MOS World Championship welcomes deeper collaboration with Microsoft IT Academies (ITAs). Many collaborating students withhold together for the competitors using the curriculum and lesson planning substances provided via their college by pass of the Microsoft ITA software. This program gives faculties with supple curriculum, customizable lesson plans and professional construction opportunities for teachers.

“We’re in fact comfortable to participate in the 2015 MOS World Championship and see the exquisite things students attain with Microsoft workplace,” talked about Alison Cunard, everyday manager of Microsoft learning Experiences, which manages Microsoft ITA. “We suppose the merits of the ITA software combined with a world competitors raise the bar for college students to hold fun while studying the 21st century technology talents they requisite for success.”

participants who outperform their peers to create the final circular of the realm Championship frequently hold consummate or close-perfect exam ratings and completion times smartly below the dispensed examination time. The reform three winners in every class will acquire scholarship prizes for $5,000, $2,500 and $1,000, respectively.

A video highlighting closing 12 months’s winners and their worldwide press coverage can besides exist organize birthright here, and the 2015 MOS World Championship promo video can besides exist organize here.

About Certiport

Certiport, a Pearson VUE company, is the leading issuer of certification exam building, start and application management services delivered through an expansive community of over 12,000 Certiport authorized checking out facilities worldwide. Certiport manages a sophisticated portfolio of leading certification programs including: the professional Microsoft workplace specialist certification software, the Microsoft expertise associate certification software, the Microsoft certified Educator program, the Adobe® licensed associate certification program, the HP permitted Technical associate, the Autodesk certified person certification software, the Intuit QuickBooks certified consumer certification program, and the IC3 Digital Literacy certification. Certiport reliably offers over three million assessments every year during the secondary, withhold up-secondary, personnel, and company expertise markets in additional than 148 international locations and 27 languages worldwide. For more suggestions, please talk over with http://www.certiport.com or ensue Certiport on Twitter at www.twitter.com/certiport.

About Microsoft

situated in 1975, Microsoft (Nasdaq “MSFT”) is the worldwide leader in software, services, gadgets and solutions that aid individuals and businesses understand their plenary abilities.

“Certiport” and “IC3” are registered logos of Certiport Inc. in the u.s. and other countries.

notice to editors: For extra assistance, information and perspectives from Microsoft, please dispute with the Microsoft information center at http://news.microsoft.com. internet hyperlinks, phone numbers and titles hold been material at time of booklet, but may besides hold changed. For extra counsel, journalists and analysts may additionally contact Microsoft’s swift Response crew or different preempt contacts listed at https://news.microsoft.com/microsoft-public-family members-contacts.

one of the vital options that has total the time been attainable in Excel's LINEST() worksheet feature is the const argument, short for constant. The function's syntax is:

=LINEST(Y values, X values, const, stats)

where:

setting the const dispute to incorrect can effectively hold most necessary implications for the character of the results that LINEST() returns. And there is a accurate query of even if the const dispute is a effectual preference in any respect. actually, the question is not restricted to LINEST() and Excel. It extends to the complete zone of regression analysis, despite the platform used to fulfill the regression.

Some credible practitioners faith that or not it's necessary to coerce the constant to zero in positive cases, usually within the context of regression discontinuity designs.

Others, including myself, harmonize with that if setting the consistent to zero seems to exist a valuable and informative alternative, then linear regression itself is regularly the inaccurate mannequin for the data.

The outdo 2003 through 2010 typesdetermine 1 suggests an specimen of the change between LINEST() outcomes when the constant is calculated continuously, and when it is forced to equal zero.

determine 1 LINEST() returns the selfsame results, no matter if you exercise outdo 2003 or outdo 2010.

In motif 1, both sets of consequences are in response to the equal underlying facts set, with the Y values in A2:A21 and the X values in B2:D21. the first set of results in F3:I7 is according to a relentless calculated invariably (const = actual). The second set of effects in F10:I14 is based on a constant that is forced to equal zero (const = FALSE).

note that no longer a solitary value in the consequences is an identical when the regular is pressured to zero as when the even is calculated at total times.

Basing the Deviations on the potentialdetermine 2 starts off to pomp how this comes about.

determine 2 The deviations are based on the ability.

In determine 2, cells G15:H15 hold the sums of squares for the regression and the residual, respectively. they're according to the anticipated Y values, in L21:L40, and the deviations of the anticipated values from the actuals, in M21:LM40.

The sums of squares are calculated by means of skill of the DEVSQ() function, which subtracts every cost in the argument's latitude from the imply of those values, squares the outcome, and sums the squares.

The expense in cell G13, 0.595, is the R2 for the regression. One effectual technique to compute that determine (and a advantageous strategy to believe of it) is:

=G15/(G15+H15)

it's, R2 is the ratio of the sum of squares regression to the whole sum of squares of the Y values. The effect, 0.595, states that 59.5% of the variability within the Y values is attributable to variability in the composite of the X values.

observe in determine 2 that the information suggested in G11:J15 are just like those said in G3:J7 (except that LINEST() studies the regression coefficients and their regular errors in the reverse of worksheet order). the previous are calculated using Excel's matrix services; the latter are calculated the usage of the LINEST feature.

additionally word in determine 2 that the correlation between the genuine and the anticipated Y values is given in mobile H22. it is 0.772. The square of that correlation, in cellphone H23, is 0.595—that is of course R2, the equal cost that you simply salvage by pass of calculating the ratio of the sum of squares regression to the entire sum of squares.

there's nothing magical about any of this. it exist total as is anticipated in line with the arithmetic underlying regression evaluation.

altering the divergence groundwork to ZeroNow determine the selfsame configuration of evaluation shown in motif 3.

figure 3 The deviations are situated on zero.

note the values for the sum of squares regression and the sum of squares residual in motif three. they're each an abominable lot better than the sums of squares mentioned in determine 2. The intent is that the deviations which are squared and summed in motif 3 are the differences between the values and zero, no longer between the values and their mean.

This change in the nature of the deviations at total times increases the whole sum of squares. (due to the fact that here is so, see Statistical evaluation: Microsoft outdo 2010, Que, 2011, Chapter 2.)

The exchange from centering the anticipated values on their imply, and the mistakes in prediction on their mean, besides alterations the relative size of the sums of squares. it may chance that the sum of squares regression receives larger relative to the sum of squares residual, and the outcomes is to increase the clear-cut cost of R2. the exercise of the sums of squares shown in motif 2 and motif 3, for instance:

figure 2:

12870.037 / (12870.037 + 8742.913) = .595

(compare with cells G5 and G13.)

determine three:

55879.198 / (55879.198 + 12875.802) = .813

(compare with cells G5 and G13.)

So the suppression of the even in determine three has resulted in a rise in the R2 from .595 to .813, and that's the judgement a substantial enhance. but does it in fact imply that the regression equation it is back in determine 3 is more reform than the one again in motif 2? in spite of everything, the square root of R2 is the dissimilar correlation between the exact Y values and the composite, anticipated Y values. The greater that correlation, the greater reform the prediction.

How the Deviations hold an outcome on the R2we can test that accuracy by using calculating the correlations, squaring them, and evaluating the results to the values for R2 which are lower back under the two circumstances for the constant: current and absent.

appear first once more at determine 2. There, the dissimilar R is calculated at .772, and the dissimilar R2 is calculated at .595 (cells H22 and H23). The value of .595 has the selfsame feeling with the cost returned through LINEST() in phone G5, and by pass of the ratio of the sums of squares in mobilephone G13.

Now return to determine 3. There, the dissimilar R is calculated at .684, and the distinctive R2 is calculated at .468 (cells H22 and H23). but the expense of .468 doesn't accept as accurate with the value back by LINEST() in telephone G5, and with the aid of the ratio of the sums of squares in cellphone G13.

In sum, working LINEST() on the information proven in determine 2 and determine three has these outcomes on the clear-cut accuracy of the predictions:

here's an inconsistency, even an clear-cut contradiction. viewed as a ratio of sums of squares, R2 is greater without the consistent. considered as the rectangular of the correlation between the exact and predicted Y values, R2 is reduce with out the steady.

The regular and the DeviationsOf course, the issue is as a result of the undeniable fact that in omitting the consistent, they are redefining what's intended with the aid of the time era "sum of squares." subsequently, we're dismembering the that means of the R2.

in case you include the regular, the deviations are the modifications between the followed values and their suggest—this is what "least squares" is total about. if you leave out the steady, the deviations are the ameliorations between the observed values and 0—it truly is what "regression without the consistent" is total about.

If the estimated values chance to exist often farther from zero than from their personal suggest, then the sum of squares regression may exist inflated as in comparison to regression with the constant. in that case, the R2 will are typically superior devoid of the even within the regression equation than it is with the constant.

A terrible R2?finally, believe you might exist nonetheless using a edition of outdo via outdo 2002, and you've got used LINEST(), without the steady, on a lore set such because the one proven in determine four.

figure four A gruesome R2 is viable simplest if somebody has made a mistake.

Even the concept of a negative R2 is ridiculous. backyard the realm of imaginary numbers, the square of a host cannot exist poor, and customary least squares evaluation does not involve imaginary numbers. How does the R2 value of -0.09122 in mobilephone F4 of determine 4 salvage there?

For that signify number, how does outdo 2002 promote up with a negative sum of squares regression and a negative F ratio (cells F6 and F5 respectively in determine four)? If the square of a number should exist positive, then the sum of squared numbers should even exist positive. And an F ratio is the ratio of two variances. A variance is an ordinary of squared deviations, and therefore requisite to even exist lofty quality—and the ratio of two positive numbers must besides exist nice.

how to salvage a gruesome R2The respond is poorly counseled coding. don't forget that, when the constant is calculated always, the total sum of squares of the specific Y values equals the entire of the sum of squares regression and the sum of squares residual. as an example, in motif 2, the plenary sum of squares is shown in cellphone A23 at 21612.950. it is returned by means of Excel's DEVSQ() feature, which sums the squared deviations of every expense from the imply of the values.

also in motif 2, the sum of squares regression and the sum of squares residual are proven in cells G15:H15. the total of these two figures is 21612.950: the value of the entire sum of squares in mobile A23.

hence, one pass to compute the sum of squares regression is to subtract the sum of squares residual from the whole sum of squares. an extra system, of direction, is to compute the sum of squares regression directly on the estimated values. but when you are writing the underlying code in, say, C, or not it's an abominable lot faster to salvage the sum of squares regression by means of subtraction than by using doing the mathematics from scratch on the predicted values.

When the regular is compelled to zero, the sum of squares residual that's back in total versions of outdo equals the outcome of pointing SUMSQ(), not DEVSQ(), on the residual values. here's fully proper, due to the fact that you want to coerce the constant to zero.

The sum of squares residual using the customary calculation of the constant is as follows:

Residual = precise – anticipated

that is, locate every of N residual values, which is the actual Y expense much less the estimated Y value (Ŷ). Subtract the intimate of the residuals () from each and every residual, rectangular the difference, and sum the squared changes. Excel's DEVSQ() function does precisely this.

The sum of squares residual forcing the constant to zero is as follows:

or, more effectively:

Excel's SUMSQ() function does exactly this.

the error, Corrected—in partNow, what LINEST() did in outdo edition 2002 (and past) was to create exercise of the equivalent of SUMSQ() to salvage the sum of squares residual, but the equal of DEVSQ() to salvage the total sum of squares. in case you add SUMSQ(expected values) to SUMSQ(Residual values), you salvage SUMSQ(actual values).

however only within the condition the Place the express of the actual values is zero can SUMSQ(estimated values) plus SUMSQ(Residual values) equal DEVSQ(specific values).

The vicissitude has been corrected in outdo 2003 and subsequent versions. but as late as outdo 2010, the vicissitude lives on in outdo charts. in case you add a linear trendline to a chart, convoke for it to coerce the regular to zero, and reveal the R2 cost on the chart, it may well nevertheless reveal up as a negative number. see determine 5.

determine 5 A terrible R2 can noiseless loom with a chart's trendline.

notice in motif 5 that although outdo 2010 changed into used to defer the chart, the linear trendline's homes consist of a gruesome R2 price. (The equation would exist suitable, though, in case you selected to exhibit it together with R2.)

ConclusionThis sequence of papers on how Microsoft has implemented LINEST() concludes with a discussion of Microsoft's staggering preference related to the pass to handle severe multicollinearity within the X variables.

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No matter what your feeling of a dream vacation is, it was effortless planning at the Vacation Expo. There was an endless variety of journeys available with the lowest prices practicable (even compared to Expedia, Travelocity, etc.)

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More information from outdo Travel owner Ted Friedli taken from a previous TAPintoRedBank interview:

With over twenty-five years in the traffic and a staff of travel counselors whose tolerable suffer is twenty-three years, Friedli kicked off by stating his four reasons (with comments), on why you should exercise a travel agency for booking your next vacation.

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Excel Travel knows the tricks of the trade in saving money. “If you’re traveling for a long weekend getaway, reserve between Saturday and Tuesday. If you’re planning a roundtrip, always expense two, one-way tickets. It’s not always less expensive, but always worth a try, especially for international flights. If you hold a family of five, you’ll salvage the lowest online expense for total five tickets. If you expense one ticket at time, savings could exist significantly more.”

Ted Friedli gives a lot of himself. In 2010, Friedli founded Kick Cancer Overboard, Inc., a nonprofit organization that has given away almost 300 free cruises to people whose lives hold been affected by cancer.

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One of the options that has always been available in Excel's LINEST() worksheet function is the const argument, short for constant. The function's syntax is:

=LINEST(Y values, X values, const, stats)

where:

Setting the const dispute to incorrect can easily hold major implications for the nature of the results that LINEST() returns. And there is a existent question of whether the const dispute is a useful option at all. In fact, the question is not limited to LINEST() and Excel. It extends to the whole zone of regression analysis, regardless of the platform used to carry out the regression.

Some credible practitioners believe that it's necessary to coerce the constant to zero in unavoidable situations, usually in the context of regression discontinuity designs.

Others, including myself, believe that if setting the constant to zero appears to exist a useful and informative option, then linear regression itself is often the wrong model for the data.

The outdo 2003 Through 2010 VersionsFigure 1 shows an specimen of the dissimilarity between LINEST() results when the constant is calculated normally, and when it is forced to equal zero.

Figure 1 LINEST() returns the selfsame results, whether you exercise outdo 2003 or outdo 2010.

In motif 1, the two sets of results are based on the selfsame underlying data set, with the Y values in A2:A21 and the X values in B2:D21. The first set of results in F3:I7 is based on a constant calculated normally (const = TRUE). The second set of results in F10:I14 is based on a constant that is forced to equal zero (const = FALSE).

Notice that not a solitary value in the results is the selfsame when the constant is forced to zero as when the constant is calculated normally.

Basing the Deviations on the MeansFigure 2 begins to demonstrate how this comes about.

Figure 2 The deviations are centered on the means.

In motif 2, cells G15:H15 hold the sums of squares for the regression and the residual, respectively. They are based on the predicted Y values, in L21:L40, and the deviations of the predicted values from the actuals, in M21:LM40.

The sums of squares are calculated by means of the DEVSQ() function, which subtracts every value in the argument's range from the express of those values, squares the result, and sums the squares.

The value in cell G13, 0.595, is the R2 for the regression. One useful pass to compute that motif (and a useful pass to mediate of it) is:

=G15/(G15+H15)

That is, R2 is the ratio of the sum of squares regression to the total sum of squares of the Y values. The result, 0.595, states that 59.5% of the variability in the Y values is attributable to variability in the composite of the X values.

Notice in motif 2 that the statistics reported in G11:J15 are identical to those reported in G3:J7 (except that LINEST() reports the regression coefficients and their yardstick errors in the reverse of worksheet order). The former are calculated using Excel's matrix functions; the latter are calculated using the LINEST function.

Also notice in motif 2 that the correlation between the actual and the predicted Y values is given in cell H22. It is 0.772. The square of that correlation, in cell H23, is 0.595—that is of course R2, the selfsame value that you salvage by calculating the ratio of the sum of squares regression to the total sum of squares.

There's nothing magical about any of this. It's total as is expected according to the mathematics underlying regression analysis.

Changing the divergence Basis to ZeroNow examine the selfsame sort of analysis shown in motif 3.

Figure 3 The deviations are centered on zero.

Notice the values for the sum of squares regression and the sum of squares residual in motif 3. They are both much larger than the sums of squares reported in motif 2. The judgement is that the deviations that are squared and summed in motif 3 are the differences between the values and zero, not between the values and their mean.

This change in the nature of the deviations always increases the total sum of squares. (For the judgement that this is so, see Statistical Analysis: Microsoft outdo 2010, Que, 2011, Chapter 2.)

The change from centering the predicted values on their mean, and the errors in prediction on their mean, besides changes the relative size of the sums of squares. It can chance that the sum of squares regression gets larger relative to the sum of squares residual, and the result is to increase the clear-cut value of R2. Using the sums of squares shown in motif 2 and motif 3, for example:

Figure 2:

12870.037 / (12870.037 + 8742.913) = .595

(Compare with cells G5 and G13.)

Figure 3:

55879.198 / (55879.198 + 12875.802) = .813

(Compare with cells G5 and G13.)

So the suppression of the constant in motif 3 has resulted in an increase in the R2 from .595 to .813, and that's a substantial increase. But does it really express that the regression equation that's returned in motif 3 is more accurate than the one returned in motif 2? After all, the square root of R2 is the multiple correlation between the actual Y values and the composite, predicted Y values. The higher that correlation, the more accurate the prediction.

How the Deviations affect the R2We can test that accuracy by calculating the correlations, squaring them, and comparing the results to the values for R2 that are returned under the two conditions for the constant: present and absent.

Look first again at motif 2. There, the multiple R is calculated at .772, and the multiple R2 is calculated at .595 (cells H22 and H23). The value of .595 agrees with the value returned by LINEST() in cell G5, and by the ratio of the sums of squares in cell G13.

Now return to motif 3. There, the multiple R is calculated at .684, and the multiple R2 is calculated at .468 (cells H22 and H23). But the value of .468 does not harmonize with the value returned by LINEST() in cell G5, and by the ratio of the sums of squares in cell G13.

In sum, running LINEST() on the data shown in motif 2 and motif 3 has these effects on the clear-cut accuracy of the predictions:

This is an inconsistency, even an clear-cut contradiction. Regarded as a ratio of sums of squares, R2 is higher without the constant. Regarded as the square of the correlation between the actual and predicted Y values, R2 is lower without the constant.

The Constant and the DeviationsOf course, the problem is due to the fact that in omitting the constant, they are redefining what's meant by the term "sum of squares." As a result, we're dismembering the meaning of the R2.

When you include the constant, the deviations are the differences between the observed values and their mean—that's what "least squares" is total about. When you omit the constant, the deviations are the differences between the observed values and zero—that's what "regression without the constant" is total about.

If the predicted values chance to exist generally farther from zero than from their own mean, then the sum of squares regression will exist inflated as compared to regression with the constant. In that case, the R2 will mind to exist greater without the constant in the regression equation than it is with the constant.

A Negative R2?Finally, suppose you're noiseless using a version of outdo through outdo 2002, and you hold used LINEST(), without the constant, on a data set such as the one shown in motif 4.

Figure 4 A negative R2 is practicable only if someone has made a mistake.

Even the feeling of a negative R2 is ridiculous. Outside the realm of imaginary numbers, the square of a number cannot exist negative, and ordinary least squares analysis does not involve imaginary numbers. How does the R2 value of -0.09122 in cell F4 of motif 4 salvage there?

For that matter, how does outdo 2002 promote up with a negative sum of squares regression and a negative F ratio (cells F6 and F5 respectively in motif 4)? If the square of a number must exist positive, then the sum of squared numbers must besides exist positive. And an F ratio is the ratio of two variances. A variance is an tolerable of squared deviations, and therefore must besides exist positive—and the ratio of two positive numbers must besides exist positive.

How to salvage a Negative R2The respond is poorly informed coding. Recall that, when the constant is calculated normally, the total sum of squares of the actual Y values equals the total of the sum of squares regression and the sum of squares residual. For example, in motif 2, the total sum of squares is shown in cell A23 at 21612.950. It is returned by Excel's DEVSQ() function, which sums the squared deviations of each value from the express of the values.

Also in motif 2, the sum of squares regression and the sum of squares residual are shown in cells G15:H15. The total of those two figures is 21612.950: the value of the total sum of squares in cell A23.

Therefore, one pass to compute the sum of squares regression is to subtract the sum of squares residual from the total sum of squares. Another method, of course, is to compute the sum of squares regression directly on the predicted values. But if you're writing the underlying code in, say, C, it's much quicker to salvage the sum of squares regression by subtraction than by doing the math from scratch on the predicted values.

When the constant is forced to zero, the sum of squares residual that's returned in total versions of outdo equals the result of pointing SUMSQ(), not DEVSQ(), at the residual values. This is entirely correct, given that you want to coerce the constant to zero.

The sum of squares residual using the yardstick calculation of the constant is as follows:

Residual = Actual – Predicted

That is, find each of N residual values, which is the actual Y value less the predicted Y value (Ŷ). Subtract the express of the residuals () from each residual, square the difference, and sum the squared differences. Excel's DEVSQ() function does precisely this.

The sum of squares residual forcing the constant to zero is as follows:

or, more simply:

Excel's SUMSQ() function does precisely this.

The Mistake, Corrected—In PartNow, what LINEST() did in outdo version 2002 (and earlier) was to exercise the equivalent of SUMSQ() to salvage the sum of squares residual, but the equivalent of DEVSQ() to salvage the total sum of squares. If you add SUMSQ(Predicted values) to SUMSQ(Residual values), you salvage SUMSQ(Actual values).

But only in the situation where the express of the actual values is zero can SUMSQ(Predicted values) plus SUMSQ(Residual values) equal DEVSQ(Actual values).

The problem has been corrected in outdo 2003 and subsequent versions. But as late as outdo 2010, the problem lives on in outdo charts. If you add a linear trendline to a chart, convoke for it to coerce the constant to zero, and pomp the R2 value on the chart, it can noiseless reveal up as a negative number. see motif 5.

Figure 5 A negative R2 can noiseless loom with a chart's trendline.

Notice in motif 5 that although outdo 2010 was used to defer the chart, the linear trendline's properties include a negative R2 value. (The equation would exist correct, though, if you chose to reveal it along with R2.)

ConclusionThis chain of papers on how Microsoft has implemented LINEST() concludes with a discussion of Microsoft's extraordinary conclusion regarding how to handle extreme multicollinearity in the X variables.

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