QuickBooks 2014 for Dummies by Stephen L. Nelson

By Stephen L. Nelson

Take keep watch over of the books and retain your funds within the black with QuickBooks and "For Dummies"

With over 4 million of his books in print, CPA and perennial bestselling "For Dummies" writer Stephen L. Nelson is aware tips to make QuickBooks and uncomplicated accounting effortless for the remainder of us. Small enterprise vendors, managers, and staff: with a view to use QuickBooks on your company, the recent version of this annual bestseller is the simplest position to begin. From developing the software program to making invoices, recording and paying debts, monitoring stock, getting studies, and crunching numbers for tax prep, you'll realize the right way to do it, why to do it, and get far more prepared within the method. offers small enterprise proprietors the ability to regulate their very own enterprise accounting and monetary administration initiatives utilizing QuickBooks 2014 is helping you construct the right finances, method payroll, create invoices, deal with stock, music expenses, generate monetary stories, stability debts, and simplify your tax go back prep Walks you thru simple bookkeeping innovations, information administration basics, and need-to-know accounting instructions that can assist you music your funds with ease

Keep what you are promoting funds on the right track, on finances, and up to the mark with QuickBooks 2014 and "QuickBooks 2014 For Dummies."

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Who this booklet is for
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In particular, in a multiple linear regression model with p parameters the denominator should be n − p in order to construct an unbiased estimator of the error variance σ 2 . Detailed discussion can be found in later chapters. The unbiasness of estimator s2 for the simple linear regression can be shown in the following derivations. yi − yˆi = yi − b0 − b1 xi = yi − (¯ y − b1 x ¯) − b1 xi = (yi − y¯) − b1 (xi − x ¯) It follows that n n (yi − yˆi ) = n (yi − y¯) − b1 i=1 i=1 (xi − x ¯) = 0. i=1 Note that (yi − yˆi )xi = [(yi − y¯) − b1 (xi − x ¯)]xi , hence we have n n (yi − yˆi )xi = i=1 n = [(yi − y¯) − b1 (xi − x ¯)]xi i=1 [(yi − y¯) − b1 (xi − x ¯)](xi − x ¯) i=1 n = n (xi − x ¯)2 (yi − y¯)(xi − x ¯) − b1 i=1 i=1 Sxy Sxx = 0 Sxx To show that s2 is an unbiased estimate of the error variance, first we note that = n(Sxy − b1 Sxx ) = n Sxy − (yi − yˆi )2 = [(yi − y¯) − b1 (xi − x ¯)]2 , therefore, n n (yi − yˆi )2 = i=1 n [(yi − y¯) − b1 (xi − x ¯)]2 i=1 n 2 = (yi − y¯) − 2b1 i=1 n n (xi − x ¯)(yi − y¯i ) + i=1 (yi − y¯)2 − 2nb1 Sxy + nb21 Sxx = i=1 n (yi − y¯)2 − 2n = i=1 n (yi − y¯)2 − n = i=1 2 Sxy Sxy Sxy + n 2 Sxx Sxx Sxx 2 Sxy Sxx b21 (xi − x ¯)2 i=1 April 29, 2009 11:50 World Scientific Book - 9in x 6in Regression˙master Simple Linear Regression 17 Since (yi − y¯)2 = [β1 (xi − x ¯) + (εi − ε¯)]2 and (yi − y¯)2 = β12 (xi − x ¯)2 + (εi − ε¯)2 + 2β1 (xi − x ¯)(εi − ε¯), therefore, E(yi − y¯)2 = β12 (xi − x ¯)2 + E(εi − ε¯)2 = β12 (xi − x ¯ )2 + n−1 2 σ , n and n n E(yi − y¯)2 = nβ12 Sxx + i=1 i=1 n−1 2 σ = nβ12 Sxx + (n − 1)σ 2 .

Myers, Classical and Modern Regression Analysis With Applications, P76. April 29, 2009 11:50 World Scientific Book - 9in x 6in This page intentionally left blank Regression˙master April 29, 2009 11:50 World Scientific Book - 9in x 6in Regression˙master Chapter 3 Multiple Linear Regression The general purpose of multiple linear regression is to seek for the linear relationship between a dependent variable and several independent variables. Multiple regression allows researchers to examine the effect of more than one independent variables on response at the same time.

A confidence interval of interest can be used to evaluate the accuracy of a single future value of y at a chosen value of regressor x. Confidence interval estimator for a future value of y provides confidence interval for an estimated value y at x with a desirable confidence level 1 − α. It is of interest to compare the above two different kinds of confidence interval. The second kind has larger confidence interval which reflects the less accuracy resulting from the estimation of a single future value of y rather than the mean value computed for the first kind confidence interval.

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