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MEAN(R1) IQR(R1) MAD(R1) RNG(R1) STDERR(R1) RANK_AVG(x, R1, order) FREQTABLE(R1) same as AVERAGE(R1) interquartile range of data in range R1 median absolute deviation of the data in range R1 range of data in range R1, i.e. MAX(R1) β MIN(R1) standard error of the data in range R1, i.e. STDEV(R1)/SQRT(COUNT(R1)) rank of x in range R1 taking ties into account; order is optional (default = 0), where a zero value indicates increasing order and a non-zero value indicates decreasing order; equivalent to Excel 2010βs RANK.AVG(x, R1, order). array function which returns an n x 3 array which contains the frequency table for the data in range R1, where n = the number of unique values in R1 (i.e. the number of data elements in R1 without duplicates) CONFIDENCE_T(alpha, s, size) T_CONF(R1, alpha) T_LOWER(R1, alpha) T_UPPER(R1, alpha) DF_POOLED(R1, R2) STDERR_POOLED(R1, R2) STDEV_POOLED(R1, R2) VAR_POOLED(R1, R2) T_DIST(x, df, cum) the value k such that x β k, x + k is the confidence interval for the population mean based on the stated alpha value, standard deviation s and sample size, assuming a t distribution the value k such that π₯ β π, π₯ + π is the confidence interval for the population mean based on the stated alpha value and sample data in range R1 assuming a π‘ distribution the value π₯ β π such that π₯ β π, π₯ + π is the confidence interval for the population mean based on the stated alpha value and sample data in range R1 assuming a π‘ distribution the value π₯ + π such that π₯ β π, π₯ + π is the confidence interval for the population mean based on the stated alpha value and sample data in range R1 assuming a t distribution degrees of freedom for the two sample t test for samples in ranges R1 and R2, especially when the two samples have unequal variances pooled standard error for two sample t test for samples in ranges R1 and R2 pooled standard deviation for the two sample t test for samples in ranges R1 and R2 when the two samples have equal variances pooled variance for the two sample t test for samples in ranges R1 and R2 when the two samples have equal variances value of the cumulative distribution function of the π‘ distribution with df degrees of freedom at x when cum = TRUE, and value of the pdf of the π‘ distribution with df degrees of freedom at x when cum = FALSE NORM_CONF(R1, alpha) NORM_LOWER(R1, alpha) NORM_UPPER(R1, alpha) SHAPIRO(R1) SWTEST(R1) the value k such that x β k, x + k is the confidence interval for the population mean based on the stated alpha value and sample data in range R1 assuming a normal distribution the value π₯ β π such that π₯ β π, π₯ + π is the confidence interval for the population mean based on the stated alpha value and sample data in range R1 assuming a normal distribution the value π₯ + π such that π₯ β π, π₯ + π is the confidence interval for the population mean based on the stated alpha value and sample data in range R1 assuming a normal distribution the Shapiro-Wilk test statistic W for the data in the range R1 the p-value of the Shapiro-Wilk test on the data in range R1 CHI_STAT2(R1, R2) CHI_MAX2(R1, R2) CHI_STAT(R1) CHI_MAX(R1) CHI_TEST(R1) CHI_MAX_TEST(R1) CHISQ_DIST(x, df, cum) FISHERTEST(R1, t) Pearsonβs chi-square statistic for observation values in range R1 and expectation values in range R2 maximum likelihood chi-square statistic for observation values in range R1 and expectation values in range R2 Pearsonβs chi-square statistic for observation values in range R1 maximum likelihood chi-square statistic for observation values in range R1 p-value for Pearsonβs chi-square statistic for observation values in range R1 p-value for maximum likelihood chi-square statistic for observation values in range R1 value of the cumulative distribution function of the chi-square distribution with df degrees of freedom at x when cum = TRUE, and value of the pdf of the chi-square distribution with df degrees of freedom at x when cum = FALSE probability calculated by the Fisher exact test for the 2 x 2 contingency table in range R1 where t = the number of tails: 1 (one-tail) or 2 (two tail) COVARP(R1, R2) COVARS(R1, R2 CORREL_ADJ(R1, R2) MCORREL(R, R1, R2) COV(R1) COVP(R1) population covariance of the populations defined by ranges R1 and R2; equivalent to COVAR(R1, R2) sample covariance of the samples defined by ranges R1 and R2; equivalent to COVAR(R1, R2) * n/(n-1) where n = COUNT(R1) = COUNT(R2) adjusted correlation coefficient for the data sets defined by ranges R1 and R2 multiple correlation of dependent variable z with x and y where the samples for z, x and y are the ranges R, R1 and R2 respectively array function which returns the sample covariance matrix for the array defined by range R1 array function which returns the population covariance matrix for the array defined by range R1 DESIGN(R1) HAT(R1) CORE(R1) LEVERAGE(R1) RegCov(R1, R2) RegCoeff(R1, R2) RegCoeffSE(R1, R2) RegY(R1, R2) RegE(R1, R2) RegStudE(R1, R2) the design matrix for the data in R1 the hat matrix for the data in R1 the core of the hat matrix for the data in R1 the leverage vector = diagonal of hat matrix for the data in R1 the covariance matrix for the regression coefficients of the regression line a vector with the regression coefficients for the regression line a vector with the standard errors of the coefficients for the regression line a vector of predicted values for Y based on the regression line = TREND(R2,R1) a vector of residuals based on the regression line a vector of studentized residuals based on the regression line RegAIC(R1, R2) RegAICc(R1, R2) p-value of the test of the significance of X data in R3 (reduce model) vs. X data in R1 (full model) the Akaikeβs Information Criterion (AIC) for the regression model corrected AICc for the regression model TOLERANCE(R1, j) Tolerance of the πth variable for the data in range R1; i.e. 1 β π π2 VIF(R1, j) VIF of the πth variable for the data in range R1; i.e. 1 β π π2 RSquareTest(R1, R3, R2) LEVENE(R1) DunnSidak(Ξ±, k) FSTAR(R1) DFSTAR(R1) BFTEST(R1) p-value of for Leveneβs test for the data in range R1 (organized by columns) / 1 π 1β 1β πΌ Brown-Forsytheβs test statistic F* on the data in range R1 df* for Brown-Forsytheβs test on the data in range R1 p-value of the Brown-Forsytheβs test statistic on the data in range R1 SignTest(R1, m) p-value for the sign test where R1 contains the sample data and m = the hypothesized median WILCOXON(R1, R2) WILCOXON1(R1, n) minimum of W and Wβ for the samples contained in ranges R1 and R2 minimum of W and Wβ for the samples contained in the first n columns of range R1 and the remaining columns of range R1. If the second argument is omitted it defaults to 1 WTEST(R1, R2) p-value of the Wilcoxon rank-sum test for the samples contained in ranges R1 and R2 WTEST1(R1, n) p-value of the Wilcoxon rank-sum test for the samples contained in the first n columns of range R1 and the remaining columns of range R1. If the second argument is omitted it defaults to 1 MANN(R1, R2) MANN1(R1, n) MTEST(R1, R2) MTEST1(R1, n) RankSign(R1) RTEST(R1) RSignPair(R1) RTESTPair(R1) KRUSKAL(R1) KTEST(R1) FRIEDMAN(R1) FrTEST(R1) U for the samples contained in ranges R1 and R2 U for the samples contained in the first n columns of range R1 and the remaining columns of range R1. If the second argument is omitted it defaults to 1 p-value of the Mann-Whitney U test for the samples contained in ranges R1 and R2 p-value of the Mann-Whitney U test for the samples contained in the first n columns of range R1 and the remaining columns of range R1. If the second argument is omitted it defaults to 1 T for a single sample contained in range R1 p-value for Rank-Sign test using the normal distribution approximation for the sample contained in range R1 T for a pair of samples contained in range R1, where R1 consists of two columns, one for each paired sample p-value for Rank-Sign test using the normal distribution approximation for the pair of samples contained in range R1, where R1 consists of two columns, one for each paired sample value of the Kruskal-Wallisβs test statistic on the data in range R1 p-value of the Kruskal-Wallisβs test on the data in range R1 value of the Friedmanβs test statistic on the data in range R1 p-value of the Friedmanβs test on the data in range R1 MULTINOMDIST(R1, R2) value of multinomial distribution where R1 contain the number of successes and R2 contain the corresponding probabilities of success DET(R1) DIAG(R1) IDENTITY(R1) ISCELL(R1) ISSQUARE(R1) LENGTH(R1) NORM(R1) TRACE(R1) same as MDETERM(R1) array function that returns a column vector with the values on the diagonal of the matrix in range R1 (esp. useful when R1 is a square matrix) array function that returns an identity matrix of the size of the highlighted range returns TRUE if R1 is a single cell and FALSE otherwise returns TRUE if R1 is a square range and FALSE otherwise length of matrix in range R1 = the square root of the sum of the squares of all the elements in R1 (esp. useful for column or row vectors) array function that returns the normalized version of the matrix in range R1 trace of matrix in range R1 ELIM(R1) LINEQU(R1) array function which outputs the results of Gaussian Elimination on the augmented matrix found in the array R1. The shape of the output is the same as the shape of R1 array function which returns a column vector with solution to linear equations defined by R1; returns an error if no solution or the solution is not unique INTERPOLATE(r, r1, r2, v1,v2) MLookup(R1, r, c) ILookup(R1, r, c) the value between v1 and v2 that are proportional to the distance that r is between r1 and r2, where v1 corresponds to r1 and v2 corresponds to r2 the value in the table defined by range R1 in the row headed by r and the column headed by c. the value in the table defined by range R1 corresponding to row r and column c. If r or c can refer to some value that must be interpolated between row or column headings, provided those headings are numbers. If the first row (or column) heading is preceded by β>β it refers to values smaller than the next row (or column heading). If the last row (or column) heading is preceded by β>β it refers to values bigger than the previous row (or column heading). TauCRIT(n, Ξ±, t) RhoCRIT(n, Ξ±, t) RSignCRIT(n, Ξ±, t) KSCRIT(n, Ξ±, t) WCRIT(n1, n2, Ξ±, t) MCRIT(n1, n2, Ξ±, t) QCRIT(k, df, Ξ±, t) RLowerCRIT(n1, n2) RUpperCRIT(n1, n2) SWLookup(n, W) critical value in the Kendallβs Tau table critical value in the Spearmanβs Rho table critical value in the Rank Sign table critical value in the Kolmogorov-Smirnov table critical value in the Wilcoxon Rank-Sum table critical value in the Mann-Whitney table critical value in the Studentized Range Q table the lower value in the Runs Test table the upper value in the Runs Test table The p-value for n and W in the Shapiro-Wilk table 2 QSORT(R1, b) NODUPES(R1) REVERSE(R1) RESHAPE(R1) SHUFFLE(R1) RANDOMIZE(R1) array function which fills highlighted array with data from R1 in sorted order (by columns); b is an optional parameter (default = TRUE); if b is TRUE then sort is in ascending order and if b is FALSE (or 0) sort is in descending order. array function which fills highlighted array with data from R1 eliminating any duplicates (by columns); assumes that range R1 is in sorted order (by column). array function which fills highlighted array with data from R1 in reverse order (by columns) array function which fills highlighted array with data from R1 (by columns) array function which fills highlighted array with a permutation of the data from R1 (selection without replacement) array function which fills highlighted array with a random selection of data from R1 (selection replacement) SSRes(R1, R2) = πππ ππ SSReg(R1, R2) = πππ ππ SSRegTot(R2) = πππ MultipleR(R1, R2) = π REGF(R1, R2) = πΉ dfRes(R1) = πππ ππ dfReg(R1) = πππ ππ dfRegTot(R1) = πππ RSquare(R1, R2) = π 2 RegTEST(R1, R2) = p-value MSRes(R1, R2) = πππ ππ MSReg(R1, R2) = πππ ππ MSRegTot(R1, R2) = πππ AdjRSquare(R1, R2) = Adjusted π 2 RegSE(R1, R2) = standard error = πππ ππ SSW(R1) = πππ SSBet(R1) = πππ΅ SSTot(R1) = πππ ANOVA1(R1) = πππ΄ /πππ dfW(R1) = πππ dfBet(R1) = πππ΅ dfTot(R1) = πππ ATEST(R1) = p-value for A factor MSW(R1) = πππ MSBet(R1) = πππ΅ MSTot(R1) = πππ SSWF(R1, r) = πππ SSRow(R1, r) = πππ΄ SSCol(R1, r) = πππ΅ SSInt(R1, r) = πππ΄π΅ SSTot(R1, r) = πππ dfWF(R1, r) = πππ dfRow(R1, r) = πππ΄ dfCol(R1, r) = πππ΅ dfInt(R1, r) = πππ΄π΅ dfTot(R1, r) = πππ MSWF(R1, r) = πππ MSRow(R1, r) = πππ΄ MSCol(R1, r) = πππ΅ MSInt(R1, r) = πππ΄π΅ MSTot(R1, r) = πππ ANOVARow(R1, r) = πππ΄ /πππ ANOVACol(R1, r) = πππ΅ /πππ ANOVAInt(R1, r) = πππ΄π΅ /πππ ATESTRow(R1, r) = p-value for A factor ATESTCol(R1, r) = p-value for B factor ATESTInt(R1, r) = p-value for AB factor Cell F4 F5 F6 F7 F8 Content =MIN(A4:A13) =QUARTILE(A4:A13,1)-F4 =MEDIAN(A4:A13)-QUARTILE(A4:A13,1) =QUARTILE(A4:A13,3)-MEDIAN(A4:A13) =MAX(A4:A13)-QUARTILE(A4:A13,3)