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Orlov Chemistry Department, Oregon State University (1996)

In modern science, regression analysis is a necessary part of virtually almost any data reduction process. Popular spreadsheet programs, such as Quattro Pro, Microsoft Excel, and Lotus 1-2-3 provide comprehensive statistical program packages, which include a regression tool among many others. Usually, the regression module is explained clearly enough in on-line help and spreadsheet documentation (i.e. items in the regression input dialog box). However, the description of the output is minimal and is often a mystery for the user who is unfamiliar with certain statistical concepts. The objective of this short handout is to give a more detailed description of the regression tool and to touch upon related statistical topics in a hopefully readable manner. It is designed for science undergraduate and graduate students inexperienced in statistical matters. The regression output in Microsoft Excel is pretty standard and is chosen as a basis for illustrations and examples ( Quattro Pro and Lotus 1-2-3 use an almost identical format).


In a regression analysis we study the relationship, called the regression function, between one variable y, called the dependent variable, and several others xi, called the independent variables. Regression function also involves a set of unknown parameters bi. If a regression function is linear in the parameters (but not necessarily in the independent variables ! ) we term it a linear regression model. Otherwise, the model is called non-linear. Linear regression models with more than one independent variable are referred to as multiple linear models, as opposed to simple linear models with one independent variable.

The following notation is used in this work: y y* p xi (i=1,2, p) bi (i=1,2, p) b0 k=p+1 n i =1,2 p j=1,2, n - dependent variable (predicted by a regression model) - dependent variable (experimental value) - number of independent variables (number of coefficients) - ith independent variable from total set of p variables - ith coefficient corresponding to xi - intercept (or constant) - total number of parameters including intercept (constant) - number of observations ( experimental data points) - independent variables index - data points index

Now let us illustrate the classification of regression models with mathematical expressions: Multiple linear model General formula: y = b0 + b1x1 + b2x2 + bpxp or y = b0 + i bixi i=1,2, p (1) (1a)

Polynomial (model is linear in parameters , but not in independent variables): y = b0 + b1x + b2x2 + b3x3 bpxp, which is just a specific case of (1) with x1 = x, x2 = x2, x3 = x3 ..xp = xp

Simple linear model y = b0 + b1x1 It is obvious that simple linear model is just specific case of multiple one with k=2 (p=1) Non-linear model y = A(1-e-Bx), where A, B are parameters In further discussion we restrict ourselves to multiple linear regression analysis.


Our primary goal is to determine the best set of parameters bi, such that the model predicts experimental values of the dependent variable as accurately as possible (i.e. calculated values yj should be close to experimental values yj* ). We also wish to judge whether our model itself is adequate to fit the observed experimental data (i.e. whether we chose the correct mathematical form of it). We need to check whether all terms in our model are significant (i.e. is the improvement in goodness of fit due to the addition of a certain term to the model bigger than the noise in experimental data).


The standard regression output of spreadsheet programs provides information to reach the objectives raised in the previous section. Now we explain how to do that and touch upon related statistical terms and definitions. The following numerical example will be used throughout the handout to illustrate the discussion:

Table 1. Original experimental data Data point # y* z j 1 20.6947 2.5 2 28.5623 3.1 3 157.0020 8.1 4 334.6340 12.2 5 406.5697 13.5 6 696.0331 17.9 7 945.1385 21.0

We choose y* to be the dependent experimental observable and z to be the independent one. Suppose we have, say, theoretical reasons to believe that relationship between two is: y* = b0 + b1*z + b2*z2 + b3*z3

We can rewrite this expression in form (1): y = b0 + b1x1 + b2x2 + b3x3, where x1 = z, x2 = z2 and x2 = z3 (1b)

In the next step we prepare the spreadsheet input table for regression analysis: Table 2. Regression input Data point # Dependent var. j y* 1 2 3 4 5 6 7 20.6947 28.5623 157.0020 334.6340 406.5697 696.0331 945.1385

Independent variables x1(=z) x2(=z2) x3(=z3) 2.5 3.1 8.1 12.2 13.5 17.9 21.0 6.25 9.61 65.61 148.84 182.25 320.41 441.00 15.63 29.79 531.44 1815.85 2460.38 5735.34 9261.00

In order to perform a regression analysis we choose from the Microsoft Excel menu*: Tools Data analysis Regression

Note that data analysis tool should have been previously added to Microsoft Excel during the program setup (Tools Add-Ins Analysis ToolPak).

The pop-up input dialog box is shown on Fig.1. Elements of this box are described in online help. Most of them become clear in the course of our discussion as well. The Input Y range refers to the spreadsheet cells containing the independent variable y* and the Input X range to those containing independent variables x ( in our example x = x1, x2, x3) (see Table 2). If we do not want to force our model through the origin we leave the Constant is Zero box unchecked. The meaning of Confidence level entry will become clear later. The block Output options allows one to choose the content and locations of the regression output. The minimal output has two parts Regression Statistics and ANOVA (ANalysis Of VAriance). Checking the appropriate boxes in subblocks Residuals and Normal Probability will expand the default output information. We omit from our discussion description of Normal Probability output. Now we are ready to proceed with the discussion of the regression output.

* - In Quattro Pro the sequence is Tools - Numeric Tools - Analysis Tools - Advanced Regression. In the
last step instead of Advanced Regression, one can choose Regression from the menu. In this case the simplified regression output will be obtained.

Fig. 1. Regression input dialog box

Residual output Example In Microsoft Excel the residual output has the following format: Table3. Residual output* Observation Predicted Y Residuals (j) (yj) (r) 1 20.4424 0.2523 2 28.9772 -0.4149 3 156.3982 0.6038 4 335.5517 -0.9178 5 406.3355 0.2342 6 695.6173 0.4159 7 945.3121 -0.1736

Standard Residuals (r) 0.3351 -0.5511 0.8020 -1.2189 0.3111 0.5524 -0.2305

* - Corresponding notation used in this handout is given in parenthesis

Residual (or error, or deviation) is the difference between the observed value y* of the dependent variable for the jth experimental data point (x1j, x2j, , xpj, yj*) and the

corresponding value yj given by the regression function yj = b0 + b1x1j + b2x2j + bpxpj (yj = b0 + b1x1j + b2x2j + b3x3j in our example): rj = yj* - yj (2)

Parameters b (b0, b1, b2, bp) are part of the ANOVA output (discussed later). If there is an obvious correlation between the residuals and the independent variable x (say, residuals systematically increase with increasing x), it means that the chosen model is not adequate to fit the experiment (e.g. we may need to add an extra term x4=z4 to our model (1b)). A plot of residuals is very helpful in detecting such a correlation. This plot will be included in the regression output if the box Residual Plots was checked in the regression input dialog window (Fig. 1). Example

X Variable 1 Residual Plot 1

Residuals (r)

0.5 0 -0.5 -1 X Variable 1 (x1) 0.0 5.0 10.0 15.0 20.0 25.0

However, the fact that the residuals look random and that there is no obvious correlation with the variable x does not necessarily mean by itself that the model is adequate. More tests are needed. Standard ( or standardized ) residual is a residual scaled with respect to the standard error (deviation) Sy in a dependent variable: rj = rj / Sy (2a)

The quantity Sy is part of the Regression statistics output (discussed later). Standardized residuals are used for some statistical tests, which are not usually needed for models in physical sciences.

ANOVA output

There are two tables in ANOVA (Analysis of Variance). Example Table 4. ANOVA output (part I)* df SS MS 3 (dfR) 723630.06 (SSR) 241210.02 (MSR) Regression 1.70 (SSE) 0.57 (MSE) Residual (error) 3 (dfE) 6 (dfT) 723631.76 (SST) N/A (MST) Total

F Significance F 425507.02 (FR) 6.12E-09 (PR)

Table 4a. ANOVA output (part II)* Coefficients Standard (bi) Error (se (bi)) 0.52292226 1.77984111 Intercept (b0) 0.73039587 X Variable 1 (x1) 2.91437225 0.07318737 X Variable 2 (x2) 2.02376459 0.00206174 X Variable 3 (x3) -0.0009602

t Stat P-value Lower 95% Upper 95% (ti) (Pi) (bL,(1-Pi)) (bU,(1-Pi)) 0.293802778 0.7881 -5.1413318 6.1871763 3.990126957 0.0282 0.58992443 5.2388201 27.65182747 0.0001 1.79084949 2.2566797 -0.46574477 0.6731 -0.0075216 0.0056011

* - Corresponding notation used in this handout is given in parenthesis; N/A means not available in Microsoft Excel regression output.

Coefficients. The regression program determines the best set of parameters b (b0, b1, b2, bp) in the model yj=b0 +b1x1j+b2x2j+ bpxpj by minimizing the error sum of squares SSE (discussed later). Coefficients are listed in the second table of ANOVA (see Table 4a). These coefficients allow the program to calculate predicted values of the dependent variable y (y1, y2, yn), which were used above in formula (2) and are part of Residual output ( Table 3).

Sum of squares. In general, the sum of squares of some arbitrary variable q is determined as: SSq = j n(qj - qavg)2, where (3)

qj - jth observation out of n total observations of quantity q qavg - average value of q in n observations: qavg= jn qj)/n

In the ANOVA regression output one will find three types of sum of squares (see Table 4): 1). Total sum of squares SST : SST =jn(yj* - y*avg)2, where y*avg =jn yj*)/n (3a)

It is obvious that SST is the sum of squares of deviations of the experimental values of dependent variable y* from its average value. SST could be interpreted as the sum of deviations of y* from the simplest possible model (y is constant and does not depend on any variable x): y = b0, with b0 = y*avg (4)

SST has two contributors: residual (error) sum of squares (SSE ) and regression sum of squares(SSR): SST = SSE + SSR 2). Residual (or error) sum of squares SSE : SSE = jn (rj - ravg)2 (6) (5)

Since in the underlying theory the expected value of residuals ravg is assumed to be zero, expression (6) simplifies to: SSE = jn (rj)2 (6a)

The significance of this quantity is that by the minimization of SSE the spreadsheet regression tool determines the best set of parameters b= b0, b1, b2, , bp for a given regression model. SSE could be also viewed as the due-to-random-scattering-of -y*-aboutpredicted-line contributor to the total sum of squares SST. This is the reason for calling the quantity due to error (residual) sum of squares. 3). Regression sum of squares SSR: SSR = jn (yj - y*avg)2 (7)

SSR is the sum of squares of deviations of the predicted-by-regression-model values of dependent variable y from its average experimental value y*avg. It accounts for addition of p variables (x1, x2, , xp) to the simplest possible model (4) (variable y is just a constant and does not depend on variables x), i.e. y = b0 vs. y = b0 + b1x1 + b2x2 + + bpxp. Since this is a transformation from the non-regression model (4) to the true regression model (1), SSR is called the due to regression sum of squares.

The definition of SSR in the form (7) is not always given in the literature. One can find different expressions in books on statistics [1, 2] : SSR = ip bi jn (xi j - xavg) yj, where xavg = jn xj*)/n or SSR = ip bi jn xi j yj* - (jn yj*)2 /n (7b) (7a)

Relationships (7a-b) give the same numerical result, however, it is difficult to see the physical meaning of SSR from them.

Mean square (variance) and degrees of freedom The general expression for the mean square of an arbitrary quantity q is: MSq = SSq / df (8)

SSq is defined by (3) and df is the number of degrees of freedom associated with quantity SSq. MS is also often referred to as the variance. The number of degrees of freedom could be viewed as the difference between the number of observations n and the number of constraints (fixed parameters associated with the corresponding sum of squares SSq).

1). Total mean square MST (total variance): MST = SST/(n - 1) (9)

SST is associated with the model (4), which has only one constraint (parameter b0), therefore the number of degrees of freedom in this case is: dfT = n - 1 2). Residual (error) mean square MSE (error variance): MSE = SSE / (n - k) (11) (10)

SSE is associated with the random error around the regression model (1), which has k=p+1 parameters (one per each variable out of p variables total plus intercept). It means there are k constraints and the number of degrees of freedom is : dfE = n - k (12)


3). Regression mean square MSR (regression variance): MSR = SSR /(k - 1) (13)

The number of degrees of freedom in this case can be viewed as the difference between the total number of degrees of freedom dfT (10) and the number of degrees of freedom for residuals dfE (12) : dfR = dfT - dfE = (n - 1) - (n - k) dfR = k - 1 = p (14)

Tests of significance and F-numbers The F-number is the quantity which can be used to test for the statistical difference between two variances. For example, if we have two random variables q and v, the corresponding F- number is: Fqv = MSq / MSv (15)

The variances MSq and MSv are defined by an expression of type (8). In order to tell whether two variances are statistically different, we determine the corresponding probability P from F-distribution function: P=P(Fqv, dfq, dfv) (16)

The quantities dfq, dfv - degrees of freedom for numerator and denominator - are parameters of this function. Tabulated numerical values of P for the F-distribution can be found in various texts on statistics or simply determined in a spreadsheet directly by using the corresponding statistical function (e.g. in Microsoft Excel one would use FDIST(Fqv, dfq, dfv) to return the numerical value of P). An interested reader can find the analytical form of P=P(Fqv, dfq, dfv) in the literature (e.g. [1, p.383]). The probability P given by (16) is a probability that the variances MSq and MSv are statistically indistinguishable. On the other hand, 1-P is the probability that they are different and is often called confidence level. Conventionally, a reasonable confidence level is 0.95 or higher. If it turns out that 1-P < 0.95, we say that MSq and MSv are statistically the same. If 1-P > 0.95, we say that at least with the 0.95 (or 95%) confidence MSq and MSv are different. The higher the confidence level, the more reliable our conclusion. The procedure just described is called the F-test. There are several F-tests related to regression analysis. We will discuss the three most common ones. They deal with significance of parameters in the regression model . The first


and the last of them is performed by spreadsheet regression tool automatically, whereas the second one is not.

1). Significance test of all coefficients in the regression model In this case we ask ourselves: With what level of confidence can we state that AT LEAST ONE of the coefficients b (b1, b2, bp) in the regression model is significantly different from zero?. The first step is to calculate the F-number for the whole regression (part of the regression output (see Table 4)): FR = MSR / MSE (17)

The second step is to determine the numerical value of the corresponding probability P R (also part of the regression output ( see Table 4)) : P R = FDIST(FR, dfR, dfE) Taking into account expressions (12) and (14) we obtain: P R = FDIST(FR, k - 1, n - k) (18a) (18)

Finally we can determine the confidence level 1 - PR. At this level of confidence, the variance due to regression MSR is statistically different from the variance due to error MSE . In its turn it means that the addition of p variables (x1, x2, , xp) to the simplest model (4) (dependent variable y is just a constant) is a statistically significant improvement of the fit. Thus, at the confidence level not less than 1- P R we can say: At least ONE of coefficients in the model is significant. FR could be also used to compare two models describing the same experimental data: the higher FR the more adequate the corresponding model. Example In our illustrative exercise we have PR= 6.12E-09 (Table 4), the corresponding level of confidence 1 - PR = 0.9999. Therefore with the confidence close to 100% we can say that at least one of coefficients b1, b2 and b3 is significant for the model y = b0 + b1x1 + b2x2 + b3x3, where x1=z, x2= z2 and x3= z3. NOTE: From this test, however, we can not be sure that ALL coefficients b1 ,b2 and b3 are non-zero . If 1- P R is not big enough (usually less than 0.95), we conclude that ALL the coefficients in the regression model are zero (in other words, the hypothesis that the variable y is just a constant is better than it is function of variables x (x1, x2, , xp) ).


2). Significance test of subset of coefficients in the regression model Now we want to decide With what level of confidence can we be sure that at least ONE of the coefficients in a selected subset of all the coefficients is significant?. Let us test a subset of the last m coefficients in the model with a total of p coefficients (b1, b2, bp). Here we need to consider two models: y = b0 + b1x1 + b2x2 + bpxp and y = b0 + b1x1 + b2x + bp-mxp-m (restricted) (20)



These models are called unrestricted (19) and restricted (20) respectively. We need to perform two separate least square regression analyses for each model. From the regression output (see Table 4) for each model we obtain the corresponding error sum of squares SSE and SSE as well as variance MSE for the unrestricted model. The next step is to calculate the F-number for testing a subset of m variables by hand (it is not part of Microsoft Excel ANOVA for an obvious reason, i.e. you must decide how many variables to include in the subset): Fm = {( SSE - SSE) / m} / MSE


Fm could be viewed as an indicator of whether the reduction in the error variance due to the addition of the subset of m variables to the restricted model (20) (( SSE - SSE) / m ) is statistically significant with respect to the overall error variance MSE for the unrestricted model (19). It is equivalent to testing the hypothesis that at least one of coefficients in the subset is not zero. In the final step, we determine probability Pm (also by hand):

P m = FDIST(Fm, m, n - k)


At the confidence level 1- P m at least ONE of the coefficients in the subset of m is significant. If 1- P m is not big enough (less than 0.95) we state that ALL m coefficients in the subset are insignificant.


Example The regression output for the unrestricted model (y = b0 + b1x1 + b2x2 + b3x3, where x1=z, x2= z2 and x3= z3) is presented in Table 4. Say, we want to test whether the quadratic and the cubic terms are significant. In this case the restricted model is: y = b0 + b1x1, where x1 = z (restricted model) (23)

The subset of parameters consists of two parameter and m=2. By analogy with the input table for the unrestricted model (Table 2) we prepare one for the restricted model: Table 5. Regression input for restricted model Data point # Dependent var. Independent var. j y* x1(=z) 1 2 3 4 5 6 7 20.6947 28.5623 157.0020 334.6340 406.5697 696.0331 945.1385 2.5 3.1 8.1 12.2 13.5 17.9 21.0

We perform an additional regression using this input table and as part of ANOVA obtain: Table 6. Regression ANOVA output for the restricted model df SS MS F Regression Residual (error) Total 1 5 6 689216 34415.70 723632 689216 6883

Significance F 100 1.70-4

From Table 4 and Table 6 we have: SSE = 1.70 MSE = 0.57 dfE =(n - k)= 3 SSE = 34415.70 Now we are able to calculate Fm=2: Fm=2 = {(34415.70-1.70)/ 2} / 0.57 Fm=2 = 30187.72 (error sum of squares; unrestricted model) (error mean square; unrestricted model) (degrees of freedom; unrestricted model) (error sum of squares; restricted model)


Using the Microsoft Excel function for the F-distribution we determine the probability Pm=2: Pm=2 = FDIST(30187.72, 2, 3) Pm=2 = 3.50E-07 Finally we calculate the level of confidence 1- Pm=2: 1-Pm=1 = 1 - 3.50E-07 1-Pm=1 = 0.99999 The confidence level is high (more than 99.99 %). We conclude that at least one of the parameters (b2 or b3) in the subset is non-zero. However, we can not be sure that both quadratic and cubic terms are significant.

3). Significance test of an individual coefficient in the regression model Here the question to answer is: With what confidence level can we state that the ith coefficient bi in the model is significant?. The corresponding F-number is: Fi = bi2 / [se(bi)]2 (24)

se(bi) is the standard error in the individual coefficient bi and is part of the ANOVA output (see Table 4a). The corresponding probability P i = FDIST(Fi, 1, n - k) (25)

leads us to the confidence level 1- P i at which we can state that coefficient bi is significant. If this level is lower than desired one we say that coefficient bi is insignificant. Fi is not part of spreadsheet regression output, but might be calculated by hand if needed. However, there is another statistics for testing individual parameters, which is part of ANOVA (see Table 4a): ti = bi / se(bi) (26)

The ti - number is the square root of Fi (expression (24)). It has a Students distribution (see [1, p. 381] for the analytical form of the distribution). The corresponding probability is numerically the same as that given by (25). There is a statistical function in Microsoft Excel which allows one to determine Pi ( part of ANOVA (see Table 4a)): P i= TDIST(ti, n-k, 2) (27)


Parameters of the function (27) are: the number of degrees of freedom df (df E =n - k) and form of test (TL=2). If TL=1 a result for a one-tailed distribution is returned; if TL=2 two-tailed distribution result is returned. An interested reader can find more information about the issue in ref. [1]

Example In our illustration P0=0.7881 and P3 = 0.6731 (see Table 4a) corresponds to fairly low confidence levels, 1 - P0 = 0.2119 and 1 - P3 = 0.3269. This suggests that parameters b0 and b3 are not significant. The confidence levels for b1 and b2 are high (1 - P1 = 1 - 0.0282 = 0.9718 and 1- P2 = 1 - 0.0001 = 0.9999), which means that they are significant. In conclusion of this F-test discussion, it should be noted that in case we remove even one insignificant variable from the model, we need to test the model once again, since coefficients which were significant in certain cases might become insignificant after removal and visa versa. It is a good practice to use a reasonable combination of all three tests in order to achieve the most reliable conclusions.

Confidence interval In the previous section we were obtaining confidence levels given F-numbers or tnumbers. We can go in an opposite direction: given a desired minimal confidence level 1-P (e.g. 0.95) calculate the related F- or t-number. Microsoft Excel provides two statistical functions for that purpose: F(1-P)=FINV (P, dfq, dfv) t(1-P)=TINV(P, df ) (28) (29)

dfq, dfv - degrees of freedom of numerator and denominator, respectively (see (15)) df - degree of freedom associated with a given t-test (varies from test to test) NOTE: in expression (29) P is the probability associated with so called twotailed Students distribution. A one- tailed distribution has the different probability The relationship between the two is: =P/2 (30)

Values of F-numbers and t-numbers for various probabilities and degrees of freedom are tabulated and can be found in any text on statistics [1,2,3,4] . Usually the one-tailed Students distribution is presented.


Knowing the t-number for a coefficient bi we can calculate the numerical interval which contains the coefficient bi with the desired probability 1-Pi:

bL, (1- Pi)= bi - se(bi)*t(1-Pi) (lower limit) bU, (1- Pi)= bi + se(bi)*t(1-Pi) (upper limit) t(1-Pi)=TINV(Pi, n-k)

(31) (31a) (32)

The standard errors for individual parameters se(bi) are part of ANOVA (Table 4a). The interval [bL, (1- Pi); bU, (1- Pi)] is called the confidence interval of parameter bi with the 1-Pi confidence level. The upper and lower limits of this interval at a 95% confidence are listed in the ANOVA output by default ( Table 4a; columns Lower 95% and Upper 95%). If in addition to this default, the confidence interval at a confidence other than 95% is desired, the box Confidence level should be checked and the value of the alternative confidence entered in the corresponding window of the Regression input dialog box (see Fig. 1).

Example For the unrestricted model (1b), the lower and upper 95% limits for intercept are -5.1413 and 6.1872 respectively (see Table 4a). The fact that with the 95% probability zero falls in this interval is consistent with our conclusion of insignificance of b0 made in the course of F-testing of individual parameters (see Example at the end of previous section). The confidence intervals at the 95% level for b1 and b2 do not include zero. This also agrees with the F-test of individual parameters.

In fact, analysis whether zero falls in a confidence interval could be viewed as a different way to perform the F-test (t-test) of individual parameters and must not be used as an additional proof of conclusions made in such a test.

Regression statistics output The information contained in the Regression statistics output characterizes the goodness of the model as a whole. Note that quantities listed in this output can be expressed in terms of the regression F-number FR (Table 4) which we have already used for the significance test of all coefficients.


Example For our unrestricted model (1b) the output is: Table 7. Regression statistics output* 0.99999882 Multiple R 2 0.99999765 R Square (R ) 0.9999953 Adjusted R Square (R2adj) 0.75291216 Standard Error (Sy) 7 Observations (n)
* - Corresponding notation used in this handout is given in parenthesis

Standard error (Sy): Sy= (MSE)0.5 (33)

MSE is an error variance discussed before (see expression (11)). Quantity Sy is an estimate of the standard error (deviation) of experimental values of the dependent variable y* with respect to those predicted by the regression model. It is used in statistics for different purposes. One of the applications we saw in the discussion of Residual output (Standardized residuals; see expression (2a)). Coefficient of determination R2 (or R Square): R2=SSR / SST = 1 - SSE/SST (34)

SSR, SSE and SST are regression, residual (error) and total sum of squares defined by (7), (6a) and (3a) respectively. The coefficient of determination is a measure of the regression model as whole. The closer R2 is to one, the better the model (1) describes the data. In the case of a perfect fit R2=1. Adjusted coefficient of determination R2 (or Adjusted R Square): R2adj=1- {SSE / (n-k)} / {SST/ (n-1)} (35)

SSE and SST are the residual (error) and the total sum of squares (see expressions (6a) and (3a)). The significance of R2adj is basically the same as that of R2 (the closer to one the better). Strictly speaking R2adj should be used as an indicator of an adequacy of the model, since it takes in to account not only deviations, but also numbers of degrees of freedom.


Multiple correlation coefficient R: R = ( SSR / SST)0.5 This quantity is just the square root of coefficient of determination. Example The fact that R2adj = 0.9999953 in our illustration is fairly close to 1 (see Table 7) suggests that overall model (1b) is adequate to fit the experimental data presented in Table 1. However, it does not mean that there are no insignificant parameters in it. (36)


For references, the following tables present a summary of the formula numbers for individual items in the Microsoft Excel Regression Output. Variables in parenthesis, introduced and used in this handout, do not appear in the output.

Table 8. Formula map of Regression statistics output (36) Multiple R 2 (34) R Square (R ) 2 (35) Adjusted R Square (R adj) (33) Standard Error (Sy) Observations (n)

Table 9. Formula map of Residual output Observation (j) Predicted Y (yj) 1 (1) 2 (1)

Residuals (rj) (2) (2)

Standard Residuals(rj) (2a) (2a)

Table 10. Formula map of ANOVA output (part I) df SS MS (dfR) (14) (SSR) (7) (MSR) (13) Regression (SSE) (6a) (MSE) (11) Residual (error) (dfE) (12) (dfT) (10) (SST) (3a) (MST)* (9) Total *- not reported in Microsoft Excel Regression output

F (FR) (17)

Significance F (PR) (18)


Table 10a. Formula map of ANOVA output (part II) Coefficients Standard t Stat (bi) Error (se(bi)) (ti)

Intercept (b0) X Variable 1 (x1) X Variable 2 (x2)

(26) (26) (26)

Lower Upper 95% 95% (bL,(1-Pi)) (bU,(1Pi)) (25) (27) (31) (31a) (25) (27) (31) (31a) (25) (27) (31) (31a)

P-value (Pi)

LITERATURE 1. Afifi A.A., Azen S.P. Statistical analysis. Computer oriented approach, Academic press, New York (1979) 2. Natrella M.G. Experimental Statistics, National Bureau of Standards, Washington DC (1963) 3. Neter J., Wasserman W. Applied linear statistical models, R.D. Irwin Inc., Homewood, Illinois (1974) 4. Gunst R.F., Mason R.L. Regression analysis and its application, Marcel Dekker Inc., NewYork (1980) 5. Shoemaker D.P., Garland C.W., Nibler J.W. Experiments in Physical Chemistry, The McGraw-Hill Companies Inc. (1996)