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REAL STATISTICS RESOURCE PACK The Real Statistics Resource Pack contains a variety of supplemental functions and data analysis tools not provided by Excel. These complement the standard Excel capabilities and make it easier for you to perform the statistical analyses described in the rest of thiswebsite.
CORRESPONDENCE PLOTS Correspondence Plots. We now create CA plots for rows and columns in Figure 1. This is the main objective of correspondence analysis. The rows plot is a scatter plot consisting of the six points corresponding to the factor coordinates of each of the row profiles described in Figure 6 of Correspondence Analysis Basic Concepts.LU FACTORIZATION
Permutation Matrix. A permutation matrix is a square matrix that has exactly one non-zero element in each row and each column, and the only permissible nonzero element is a one.. This type of matrix captures the row permutations that are used in Gaussian elimination (see Systems of Linear Equations). Example 1: The permutation matrix P in F4:I7 of Figure 1 transforms the square matrix A in NOMINAL-ORDINAL CHI-SQUARE TEST In Independence Testing, we describe how to perform testing for contingency tables where both factors are nominal.In Ordered Chi-square Testing for Independence, we describe how to perform similar testing when both factors are ordinal.On this webpage, we consider the case where one factor is nominal and the other is ordinal. Example 1: 127 people who attended a training course wereasked to
DIEBOLD-MARIANO TEST Figure 1 – Diebold-Mariano Test. We begin by calculating the residuals for the 20 data elements based on the two forecasts (columns F and G). E.g. cell F4 contains the formula =A4-B4 and cell G4 contains =A4-C4. From these values we can calculate the loss-differentials in PROPORTION PARAMETER CI upper = BETA.INV (1-α/2, x+1, n-x) where x = np = the number of successes in n trials. This approach gives good results even when np(1-p) < 5. Agresti-Coull interval where. Example 1: A new AIDS drug is shown to cure 30% of 50 patients. Find the 95% confidence interval for the cure rate. Observation: For most situations, the Wilsoninterval is
ASSUMPTIONS FOR ANCOVA First we use Excel’s regression data analysis tool to create the complete model (see Figure 3) using the range B4:H39 from Figure 1 of Regression Approach to ANCOVA when prompted for the Input X range. Figure 3 – Complete model (y, x, t, x*t) for data in Example 1. Now we test (see Figure 4) whether there is a significant differencebetween
COCHRAN-ARMITAGE TEST The Cochran-Armitage test is used to test whether there is a linear trend when the response is binary. This test is used with data in the form of a contingency table, such as that described in Independence Testing, where there are only two data rows and we suspect (or hope) that the columns are ordered. Read More Describes how to conduct the Cochran-Armitage test in Excel which determines PRETEST-POSTTEST DESIGN Pretest-Posttest Design. In a pretest-postest design, a sample is randomly assigned to two or more groups (usually one or more treatment groups and one control group); Subjects in each group are measured at two time periods: pretest (before treatment) and posttest (after treatment). Subjects in the same group receive the same treatment. WELCOME TO REAL STATISTICS USING EXCELFREE DOWNLOADRESOURCE PACKREAL STATISTICS FUNCTIONSLOGISTIC REGRESSION Real Statistics Resource Pack: an Excel add-in that extends Excel’s standard statistics capabilities by providing you with advanced worksheet functions and data analysis tools so that you can more easily perform a wide variety of practical statistical analyses. This software supports Excel 2007, 2010, 2013, 2016, 2019 and 365 forWindows and
REAL STATISTICS RESOURCE PACK The Real Statistics Resource Pack contains a variety of supplemental functions and data analysis tools not provided by Excel. These complement the standard Excel capabilities and make it easier for you to perform the statistical analyses described in the rest of thiswebsite.
CORRESPONDENCE PLOTS Correspondence Plots. We now create CA plots for rows and columns in Figure 1. This is the main objective of correspondence analysis. The rows plot is a scatter plot consisting of the six points corresponding to the factor coordinates of each of the row profiles described in Figure 6 of Correspondence Analysis Basic Concepts.LU FACTORIZATION
Permutation Matrix. A permutation matrix is a square matrix that has exactly one non-zero element in each row and each column, and the only permissible nonzero element is a one.. This type of matrix captures the row permutations that are used in Gaussian elimination (see Systems of Linear Equations). Example 1: The permutation matrix P in F4:I7 of Figure 1 transforms the square matrix A in NOMINAL-ORDINAL CHI-SQUARE TEST In Independence Testing, we describe how to perform testing for contingency tables where both factors are nominal.In Ordered Chi-square Testing for Independence, we describe how to perform similar testing when both factors are ordinal.On this webpage, we consider the case where one factor is nominal and the other is ordinal. Example 1: 127 people who attended a training course wereasked to
DIEBOLD-MARIANO TEST Figure 1 – Diebold-Mariano Test. We begin by calculating the residuals for the 20 data elements based on the two forecasts (columns F and G). E.g. cell F4 contains the formula =A4-B4 and cell G4 contains =A4-C4. From these values we can calculate the loss-differentials in PROPORTION PARAMETER CI upper = BETA.INV (1-α/2, x+1, n-x) where x = np = the number of successes in n trials. This approach gives good results even when np(1-p) < 5. Agresti-Coull interval where. Example 1: A new AIDS drug is shown to cure 30% of 50 patients. Find the 95% confidence interval for the cure rate. Observation: For most situations, the Wilsoninterval is
ASSUMPTIONS FOR ANCOVA First we use Excel’s regression data analysis tool to create the complete model (see Figure 3) using the range B4:H39 from Figure 1 of Regression Approach to ANCOVA when prompted for the Input X range. Figure 3 – Complete model (y, x, t, x*t) for data in Example 1. Now we test (see Figure 4) whether there is a significant differencebetween
COCHRAN-ARMITAGE TEST The Cochran-Armitage test is used to test whether there is a linear trend when the response is binary. This test is used with data in the form of a contingency table, such as that described in Independence Testing, where there are only two data rows and we suspect (or hope) that the columns are ordered. Read More Describes how to conduct the Cochran-Armitage test in Excel which determines PRETEST-POSTTEST DESIGN Pretest-Posttest Design. In a pretest-postest design, a sample is randomly assigned to two or more groups (usually one or more treatment groups and one control group); Subjects in each group are measured at two time periods: pretest (before treatment) and posttest (after treatment). Subjects in the same group receive the same treatment. XREALSTATS | REAL STATISTICS USING EXCEL 2021 Real Statistics Using Excel • Built with GeneratePress. Close. Home; Free Download. Resource Pack; Examples Workbooks REAL STATISTICS RELEASE 7.7.2 Announcing a minor bug-fix release of the Real Statistics software, Rel 7.7.2. This release is now available for free download for both Windows and Mac environments. NON-PARAMETRIC DATA ANALYSIS TOOLS The Real Statistics T Tests and Non-parametric Equivalents data analysis tool supports the Mann-Whitney and Wilcoxon Signed-Ranks tests, while the One Factor ANOVA data analysis tool supports the Kruskal-Wallis non-parametric test. We now describe another data analysis tool which provides access to a number of non-parametric tests. Real Statistics Data Analysis Tool: The Real Statistics BASIC CONCEPTS OF ANCOVA As usual we will try to understand how ANCOVA works via an example. We provide two approaches for performing ANCOVA: one a modified ANOVA and the other using regression. Example 1: A school system is exploring four methods of teaching reading to their children, and would like to determine which method is best. It selects a random sample of 40 BUILDING A RASCH MODEL We show how to build a Rasch model via the following example. The approach described is based on the UCON method (i.e. unconditional maximum likelihood estimation using Newton’s Method).. Example 1: Nine students (subjects) took a test consisting of the same 10 questions (items).Whether each student answered each of the questions correctly is shown in the data range A4:K13 of Figure 1. STUDENTIZED RANGE Q TABLE Studentized Range q Table with critical value for q(k, df, α) for α = .10, .025, .05 and .01, .005, .001 and values of k up to 40. THREE-PARAMETER WEIBULL There is also a three-parameter version of the Weibull distribution, which adds a location parameter γ.The probability density function (pdf) of this distribution is. for x ≥ γ.Here β > 0 is the shape parameter and α > 0 is the scale parameter.. The cumulative distribution function (cdf) isGUMBEL DISTRIBUTION
Figure 2 – Chart of the Gumbel distribution. Real Statistics Functions: The Real Statistics Resource Pack provides the following functions for the Gumbel distribution. GUMBEL_DIST(x, μ, β, cum) = the pdf of the Gumbel distribution f(x) when cum = FALSE and the corresponding cumulative distribution function F(x) when cum = TRUE.GUMBEL_INV(p
WEIGHTED MEAN AND MEDIAN Definition 1: For any set of weights W = {w 1, w 2, , w n} where each w i ≥ 0 and w i > 0 for at least one i. the weighted mean (also called the weighted average) of the data set S = {x 1, x 2, , x n} is defined by. where w = the sum of the w i.When w i =1 for all i, the weighted mean is the same as the mean.In fact, this is also true when all the weights are the same. LOGISTIC REGRESSION SAMPLE SIZE Normally distributed variable case. We begin with the case of one independent variable, i.e. a logistic regression model of form y = β1x +β0. In particular, we assume that this variable is normally distributed. The minimum sample size when comparing the null hypothesis H0: β1 = 0 with the alternative hypothesis H1: β1= b canbe estimated by.
WELCOME TO REAL STATISTICS USING EXCELFREE DOWNLOADRESOURCE PACKREAL STATISTICS FUNCTIONSLOGISTIC REGRESSION Real Statistics Resource Pack: an Excel add-in that extends Excel’s standard statistics capabilities by providing you with advanced worksheet functions and data analysis tools so that you can more easily perform a wide variety of practical statistical analyses. This software supports Excel 2007, 2010, 2013, 2016, 2019 and 365 forWindows and
REAL STATISTICS RESOURCE PACK The Real Statistics Resource Pack contains a variety of supplemental functions and data analysis tools not provided by Excel. These complement the standard Excel capabilities and make it easier for you to perform the statistical analyses described in the rest of thiswebsite.
CORRESPONDENCE PLOTS Correspondence Plots. We now create CA plots for rows and columns in Figure 1. This is the main objective of correspondence analysis. The rows plot is a scatter plot consisting of the six points corresponding to the factor coordinates of each of the row profiles described in Figure 6 of Correspondence Analysis Basic Concepts. BASIC CONCEPTS OF ANCOVA As usual we will try to understand how ANCOVA works via an example. We provide two approaches for performing ANCOVA: one a modified ANOVA and the other using regression. Example 1: A school system is exploring four methods of teaching reading to their children, and would like to determine which method is best. It selects a random sample of 40 THREE FACTOR ANOVA ANALYSIS TOOL To do this, enter Ctrl-m and select the Three Factor ANOVA option from the menu that appears. When the dialog box in Figure 1 appears, enter A3:D38 in the Input Range, unclick Column headings included with data, select Std by Columns as the Input Format, select ANOVA as the Analysis Type and click on the OK button. The output is shown inFigure 3.
SEN'S SLOPE
Real Statistics Function: The Real Statistics Resource Pack supplies the following array function to automate the steps required to calculate Sen’s slope. SEN_SLOPE(R1, lab, alpha): returns a column array with the values: Sen’s slope along with the lower and upper limits of the 1–alpha confidence interval. R1 is a column arraycontaining
ASSUMPTIONS FOR ANCOVA First we use Excel’s regression data analysis tool to create the complete model (see Figure 3) using the range B4:H39 from Figure 1 of Regression Approach to ANCOVA when prompted for the Input X range. Figure 3 – Complete model (y, x, t, x*t) for data in Example 1. Now we test (see Figure 4) whether there is a significant differencebetween
GRANGER CAUSALITY
Figure 7 – Test for Granger Causality. Here we use the Real Statistics function RSquare on the full model (cell AP3) as well as the reduced model (AP4), although we could have gotten all the values in the figure by actually conducting the regression. Since p-value = 0.003892 is small, we conclude that Eggs Granger-cause Chickens forlags = 4.
WELCOME TO REAL STATISTICS USING EXCELFREE DOWNLOADRESOURCE PACKREAL STATISTICS FUNCTIONSLOGISTIC REGRESSION Real Statistics Resource Pack: an Excel add-in that extends Excel’s standard statistics capabilities by providing you with advanced worksheet functions and data analysis tools so that you can more easily perform a wide variety of practical statistical analyses. This software supports Excel 2007, 2010, 2013, 2016, 2019 and 365 forWindows and
REAL STATISTICS RESOURCE PACK The Real Statistics Resource Pack contains a variety of supplemental functions and data analysis tools not provided by Excel. These complement the standard Excel capabilities and make it easier for you to perform the statistical analyses described in the rest of thiswebsite.
CORRESPONDENCE PLOTS Correspondence Plots. We now create CA plots for rows and columns in Figure 1. This is the main objective of correspondence analysis. The rows plot is a scatter plot consisting of the six points corresponding to the factor coordinates of each of the row profiles described in Figure 6 of Correspondence Analysis Basic Concepts. BASIC CONCEPTS OF ANCOVA As usual we will try to understand how ANCOVA works via an example. We provide two approaches for performing ANCOVA: one a modified ANOVA and the other using regression. Example 1: A school system is exploring four methods of teaching reading to their children, and would like to determine which method is best. It selects a random sample of 40 THREE FACTOR ANOVA ANALYSIS TOOL To do this, enter Ctrl-m and select the Three Factor ANOVA option from the menu that appears. When the dialog box in Figure 1 appears, enter A3:D38 in the Input Range, unclick Column headings included with data, select Std by Columns as the Input Format, select ANOVA as the Analysis Type and click on the OK button. The output is shown inFigure 3.
ASSUMPTIONS FOR ANCOVA First we use Excel’s regression data analysis tool to create the complete model (see Figure 3) using the range B4:H39 from Figure 1 of Regression Approach to ANCOVA when prompted for the Input X range. Figure 3 – Complete model (y, x, t, x*t) for data in Example 1. Now we test (see Figure 4) whether there is a significant differencebetween
SEN'S SLOPE
Real Statistics Function: The Real Statistics Resource Pack supplies the following array function to automate the steps required to calculate Sen’s slope. SEN_SLOPE(R1, lab, alpha): returns a column array with the values: Sen’s slope along with the lower and upper limits of the 1–alpha confidence interval. R1 is a column arraycontaining
GRANGER CAUSALITY
Figure 7 – Test for Granger Causality. Here we use the Real Statistics function RSquare on the full model (cell AP3) as well as the reduced model (AP4), although we could have gotten all the values in the figure by actually conducting the regression. Since p-value = 0.003892 is small, we conclude that Eggs Granger-cause Chickens forlags = 4.
LOGISTIC REGRESSION SAMPLE SIZE Normally distributed variable case. We begin with the case of one independent variable, i.e. a logistic regression model of form y = β1x +β0. In particular, we assume that this variable is normally distributed. The minimum sample size when comparing the null hypothesis H0: β1 = 0 with the alternative hypothesis H1: β1= b canbe estimated by.
LIN'S CONCORDANCE CORRELATION Like Bland-Altman, Lin’s Concordance Correlation Coefficient (CCC) is a method for comparing two measurements of the same variable.This is especially important if you are trying to introduce a new measurement capability which has some advantages (e.g. it is less expensive or safer to use) over an existing measurement technique (the “gold standard”). NON-PARAMETRIC DATA ANALYSIS TOOLS The Real Statistics T Tests and Non-parametric Equivalents data analysis tool supports the Mann-Whitney and Wilcoxon Signed-Ranks tests, while the One Factor ANOVA data analysis tool supports the Kruskal-Wallis non-parametric test. We now describe another data analysis tool which provides access to a number of non-parametric tests. Real Statistics Data Analysis Tool: The Real StatisticsGRANGER CAUSALITY
Figure 7 – Test for Granger Causality. Here we use the Real Statistics function RSquare on the full model (cell AP3) as well as the reduced model (AP4), although we could have gotten all the values in the figure by actually conducting the regression. Since p-value = 0.003892 is small, we conclude that Eggs Granger-cause Chickens forlags = 4.
DATA ANALYSIS TOOL FOR ITEM ANALYSIS Example 1: Repeat Example 1 from Partial Score for Item Analysis using the Reliability data analysis tool (the data is reproduced in Figure 1 below). Figure 2 – Data for Example 1. As usual, press Ctrl-m and select Reliability from the menu that is displayed. Fill in the dialog box that appears as shown in Figure 1. NOMINAL-ORDINAL CHI-SQUARE TEST In Independence Testing, we describe how to perform testing for contingency tables where both factors are nominal.In Ordered Chi-square Testing for Independence, we describe how to perform similar testing when both factors are ordinal.On this webpage, we consider the case where one factor is nominal and the other is ordinal. Example 1: 127 people who attended a training course wereasked to
MULTIPLE CORRELATION The multiple correlation coefficient for the kth variable with respect to the other variables in R1 can be calculated by the formula =SQRT (RSquare (R1, k)). Thus if R1, R2 and R3 are the three columns of the m × 3 data range R, with R1 and R2 containing the samples for the independent variables x and y and R3 containing the sample data for GLS METHOD FOR AUTOCORRELATION We now demonstrate the generalized least squares (GLS) method for estimating the regression coefficients with the smallest variance. Now suppose that all the linear regression assumptions hold, except that there is autocorrelation, i.e. E ≠ 0 where h ≠ 0. Let’s assume, in particular, that we have first-order autocorrelation, and DIEBOLD-MARIANO TEST Figure 1 – Diebold-Mariano Test. We begin by calculating the residuals for the 20 data elements based on the two forecasts (columns F and G). E.g. cell F4 contains the formula =A4-B4 and cell G4 contains =A4-C4. From these values we can calculate the loss-differentials in column H. INTERRATER RELIABILITY Interrater Reliability. Interrater reliability measures the agreement between two or more raters. Topics: Cohen’s Kappa. Weighted Cohen’s Kappa. Fleiss’ Kappa.HOLT'S LINEAR TREND
The data in Figure 3 of Simple Exponential Smoothing (as well as previous figures on that webpage) shows a distinct upward trend. The Moving Average and Simple Exponential Smoothing methods don’t adequately model this, but Holt’s Linear Trend Method (aka Double Exponential Smoothing) does.This is accomplished by adding a second single exponential smoothing model to capture the trend WILCOXON SIGNED-RANKS TABLE Wilcoxon Signed-Ranks Table for samples of size up to 50 and for alpha = .01, 02, 05, .10. Used for one sample and paired sample tests. WELCOME TO REAL STATISTICS USING EXCELFREE DOWNLOADRESOURCE PACKREAL STATISTICS FUNCTIONSLOGISTIC REGRESSION Real Statistics Resource Pack: an Excel add-in that extends Excel’s standard statistics capabilities by providing you with advanced worksheet functions and data analysis tools so that you can more easily perform a wide variety of practical statistical analyses. This software supports Excel 2007, 2010, 2013, 2016, 2019 and 365 forWindows and
REAL STATISTICS RESOURCE PACK The Real Statistics Resource Pack contains a variety of supplemental functions and data analysis tools not provided by Excel. These complement the standard Excel capabilities and make it easier for you to perform the statistical analyses described in the rest of thiswebsite.
CORRESPONDENCE PLOTS Correspondence Plots. We now create CA plots for rows and columns in Figure 1. This is the main objective of correspondence analysis. The rows plot is a scatter plot consisting of the six points corresponding to the factor coordinates of each of the row profiles described in Figure 6 of Correspondence Analysis Basic Concepts. BASIC CONCEPTS OF ANCOVA As usual we will try to understand how ANCOVA works via an example. We provide two approaches for performing ANCOVA: one a modified ANOVA and the other using regression. Example 1: A school system is exploring four methods of teaching reading to their children, and would like to determine which method is best. It selects a random sample of 40 THREE FACTOR ANOVA ANALYSIS TOOL To do this, enter Ctrl-m and select the Three Factor ANOVA option from the menu that appears. When the dialog box in Figure 1 appears, enter A3:D38 in the Input Range, unclick Column headings included with data, select Std by Columns as the Input Format, select ANOVA as the Analysis Type and click on the OK button. The output is shown inFigure 3.
MULTIPLE CORRELATION The multiple correlation coefficient for the kth variable with respect to the other variables in R1 can be calculated by the formula =SQRT (RSquare (R1, k)). Thus if R1, R2 and R3 are the three columns of the m × 3 data range R, with R1 and R2 containing the samples for the independent variables x and y and R3 containing the sample data for ASSUMPTIONS FOR ANCOVA First we use Excel’s regression data analysis tool to create the complete model (see Figure 3) using the range B4:H39 from Figure 1 of Regression Approach to ANCOVA when prompted for the Input X range. Figure 3 – Complete model (y, x, t, x*t) for data in Example 1. Now we test (see Figure 4) whether there is a significant differencebetween
GRANGER CAUSALITY
Figure 7 – Test for Granger Causality. Here we use the Real Statistics function RSquare on the full model (cell AP3) as well as the reduced model (AP4), although we could have gotten all the values in the figure by actually conducting the regression. Since p-value = 0.003892 is small, we conclude that Eggs Granger-cause Chickens forlags = 4.
LOGISTIC REGRESSION SAMPLE SIZE Normally distributed variable case. We begin with the case of one independent variable, i.e. a logistic regression model of form y = β1x +β0. In particular, we assume that this variable is normally distributed. The minimum sample size when comparing the null hypothesis H0: β1 = 0 with the alternative hypothesis H1: β1= b canbe estimated by.
LIN'S CONCORDANCE CORRELATION Like Bland-Altman, Lin’s Concordance Correlation Coefficient (CCC) is a method for comparing two measurements of the same variable.This is especially important if you are trying to introduce a new measurement capability which has some advantages (e.g. it is less expensive or safer to use) over an existing measurement technique (the “gold standard”). WELCOME TO REAL STATISTICS USING EXCELFREE DOWNLOADRESOURCE PACKREAL STATISTICS FUNCTIONSLOGISTIC REGRESSION Real Statistics Resource Pack: an Excel add-in that extends Excel’s standard statistics capabilities by providing you with advanced worksheet functions and data analysis tools so that you can more easily perform a wide variety of practical statistical analyses. This software supports Excel 2007, 2010, 2013, 2016, 2019 and 365 forWindows and
REAL STATISTICS RESOURCE PACK The Real Statistics Resource Pack contains a variety of supplemental functions and data analysis tools not provided by Excel. These complement the standard Excel capabilities and make it easier for you to perform the statistical analyses described in the rest of thiswebsite.
CORRESPONDENCE PLOTS Correspondence Plots. We now create CA plots for rows and columns in Figure 1. This is the main objective of correspondence analysis. The rows plot is a scatter plot consisting of the six points corresponding to the factor coordinates of each of the row profiles described in Figure 6 of Correspondence Analysis Basic Concepts. BASIC CONCEPTS OF ANCOVA As usual we will try to understand how ANCOVA works via an example. We provide two approaches for performing ANCOVA: one a modified ANOVA and the other using regression. Example 1: A school system is exploring four methods of teaching reading to their children, and would like to determine which method is best. It selects a random sample of 40 THREE FACTOR ANOVA ANALYSIS TOOL To do this, enter Ctrl-m and select the Three Factor ANOVA option from the menu that appears. When the dialog box in Figure 1 appears, enter A3:D38 in the Input Range, unclick Column headings included with data, select Std by Columns as the Input Format, select ANOVA as the Analysis Type and click on the OK button. The output is shown inFigure 3.
MULTIPLE CORRELATION The multiple correlation coefficient for the kth variable with respect to the other variables in R1 can be calculated by the formula =SQRT (RSquare (R1, k)). Thus if R1, R2 and R3 are the three columns of the m × 3 data range R, with R1 and R2 containing the samples for the independent variables x and y and R3 containing the sample data for ASSUMPTIONS FOR ANCOVA First we use Excel’s regression data analysis tool to create the complete model (see Figure 3) using the range B4:H39 from Figure 1 of Regression Approach to ANCOVA when prompted for the Input X range. Figure 3 – Complete model (y, x, t, x*t) for data in Example 1. Now we test (see Figure 4) whether there is a significant differencebetween
GRANGER CAUSALITY
Figure 7 – Test for Granger Causality. Here we use the Real Statistics function RSquare on the full model (cell AP3) as well as the reduced model (AP4), although we could have gotten all the values in the figure by actually conducting the regression. Since p-value = 0.003892 is small, we conclude that Eggs Granger-cause Chickens forlags = 4.
LOGISTIC REGRESSION SAMPLE SIZE Normally distributed variable case. We begin with the case of one independent variable, i.e. a logistic regression model of form y = β1x +β0. In particular, we assume that this variable is normally distributed. The minimum sample size when comparing the null hypothesis H0: β1 = 0 with the alternative hypothesis H1: β1= b canbe estimated by.
LIN'S CONCORDANCE CORRELATION Like Bland-Altman, Lin’s Concordance Correlation Coefficient (CCC) is a method for comparing two measurements of the same variable.This is especially important if you are trying to introduce a new measurement capability which has some advantages (e.g. it is less expensive or safer to use) over an existing measurement technique (the “gold standard”). NON-PARAMETRIC DATA ANALYSIS TOOLS The Real Statistics T Tests and Non-parametric Equivalents data analysis tool supports the Mann-Whitney and Wilcoxon Signed-Ranks tests, while the One Factor ANOVA data analysis tool supports the Kruskal-Wallis non-parametric test. We now describe another data analysis tool which provides access to a number of non-parametric tests. Real Statistics Data Analysis Tool: The Real Statistics MULTIPLE CORRELATION The multiple correlation coefficient for the kth variable with respect to the other variables in R1 can be calculated by the formula =SQRT (RSquare (R1, k)). Thus if R1, R2 and R3 are the three columns of the m × 3 data range R, with R1 and R2 containing the samples for the independent variables x and y and R3 containing the sample data forGRANGER CAUSALITY
Figure 7 – Test for Granger Causality. Here we use the Real Statistics function RSquare on the full model (cell AP3) as well as the reduced model (AP4), although we could have gotten all the values in the figure by actually conducting the regression. Since p-value = 0.003892 is small, we conclude that Eggs Granger-cause Chickens forlags = 4.
DATA ANALYSIS TOOL FOR ITEM ANALYSIS Example 1: Repeat Example 1 from Partial Score for Item Analysis using the Reliability data analysis tool (the data is reproduced in Figure 1 below). Figure 2 – Data for Example 1. As usual, press Ctrl-m and select Reliability from the menu that is displayed. Fill in the dialog box that appears as shown in Figure 1. NOMINAL-ORDINAL CHI-SQUARE TEST In Independence Testing, we describe how to perform testing for contingency tables where both factors are nominal.In Ordered Chi-square Testing for Independence, we describe how to perform similar testing when both factors are ordinal.On this webpage, we consider the case where one factor is nominal and the other is ordinal. Example 1: 127 people who attended a training course wereasked to
AUTHOR | REAL STATISTICS USING EXCEL Dear Dr.Charles, Thanks for your reply. I learned a lot. And as I further studied my data, one more question came out. When Y, X, and residual series(ERR) are proved cointegrated by ADF, the Pearson correlation is only 0.6. DIEBOLD-MARIANO TEST Figure 1 – Diebold-Mariano Test. We begin by calculating the residuals for the 20 data elements based on the two forecasts (columns F and G). E.g. cell F4 contains the formula =A4-B4 and cell G4 contains =A4-C4. From these values we can calculate the loss-differentials in column H. INTERRATER RELIABILITY Interrater Reliability. Interrater reliability measures the agreement between two or more raters. Topics: Cohen’s Kappa. Weighted Cohen’s Kappa. Fleiss’ Kappa.HOLT'S LINEAR TREND
The data in Figure 3 of Simple Exponential Smoothing (as well as previous figures on that webpage) shows a distinct upward trend. The Moving Average and Simple Exponential Smoothing methods don’t adequately model this, but Holt’s Linear Trend Method (aka Double Exponential Smoothing) does.This is accomplished by adding a second single exponential smoothing model to capture the trend WILCOXON SIGNED-RANKS TABLE Wilcoxon Signed-Ranks Table for samples of size up to 50 and for alpha = .01, 02, 05, .10. Used for one sample and paired sample tests. REAL STATISTICS USING EXCEL Everything you need to do real statistical analysis using ExcelSkip to content
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WHAT IS _REAL STATISTICS USING EXCEL_? _Real Statistics Using Excel_ is a practical guide for how to do statistical analysis in Excel plus free statistics software which extends Excel’s built-in statistical capabilities so that you can more easily perform a wide variety of statistical analyses in Excel. WHAT DOES _REAL STATISTICS USING EXCEL_ CONSIST OF? _Real Statistics Using Excel_ is comprised of the following fourcomponents:
REAL STATISTICS RESOURCE PACK: an Excel add-in which extends Excel’s standard statistics capabilities by providing you with advanced worksheet functions and data analysis tools so that you can more easily perform a wide variety of practical statistical analyses. This software supports Excel 2007, 2010, 2013, 2016 and 2019 for Windows and Excel 2011, 2016 and 2019 for the Mac. There is also limited support for Excel 2002 and 2003. REAL STATISTICS WEBSITE (i.e. this site): * Lets you download a free copy of the Real Statistics Resource Pack * Provides descriptions of how to perform a variety of statistical analyses using built-in Excel capabilities as well as supplemental capabilities provided by the Real Statistics Resource Pack * Presents numerous examples in the form of Excel worksheets which you can download to your computer For the student and the novice, the Real Statistics website is an excellent tutorial for learning the basic concepts of statistics and how to do statistical analysis. For all users, it provides a step-by-step guide for how to do statistical analysis in the Excel environment and the tools necessary to carry out these analyses. REAL STATISTICS EXAMPLES WORKBOOKS: five Excel files which contain all the examples contained on the website. These examples files can be downloaded for free. Each example focuses on a specific statistical concept and has been designed to demonstrate simple concepts before moving on to more complicated topics. You can use this website to learn how to perform statistical analyses in Excel even without using the Real Statistics Resource Pack, but we recommend that you download the resource pack so that you can have access to its powerful capabilities. REAL STATISTICS COMMUNITY: each webpage has a Comments section where you can make suggestions, identify errors, or ask questions to others in the community or request their advice. HOW DO I GET STARTED? STEP 1: If you elect to use the Real Statistics Resource Pack or would like a copy of the examples used throughout the website, click on the following icon and you will be given the opportunity to download and install for free the Real Statistics Resource Pack and/or the ExamplesWorkbook.
Once you have downloaded and installed the Real Statistics Resource Pack, you will be able use the supplemental capabilities from the copy of Excel that you run on your computer as described throughout the rest of the website and summarized in Real Statistics Functions,
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If you choose not to download the resource pack or examples now, you can do so later at any time. STEP 2: Browse through the website to to learn how to perform a wide range of statistical analyses in Excel using standard built-in as well as supplemental capabilities. We suggest that you begin by clicking on the Website Introduction(and especially
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for further information about how to navigate the website to get the information you need to run any specific statistical test or learn about any particular topic. WHY DO STATISTICAL ANALYSIS IN EXCEL? The reasons for choosing Excel are as follows: * It is widely available and so many more people know how to use it * It is not necessary to incur the cost of yet another tool * It is not necessary to learn new methods of manipulating data anddrawing graphs
* It already contains some basic statistics functions and dataanalysis tools
* It is much easier to see what is going on since unlike the more popular statistical analysis tools very little is hidden from the user * It provides the user with a lot of control and flexibility This makes Excel an ideal tool for learning statistical concepts and performing some basic statistical analyses, but unfortunately its built-in statistics capabilities are limited, and so it is often easier to use statistical tools such as SPSS or SAS for carrying out more advanced statistical analyses. It is to address Excel’s shortcomings that we have created the Real Statistics Resource Pack. This software package contains various supplemental tools that enable you to carry out a wide range of advanced statistical analyses without leaving the Excel environment. You can download the Real Statistics Resource Pack free of charge from this website (as described above). 594 RESPONSES TO _WELCOME_← Older Comments
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Alessandro Zarpellon says: March 13, 2020 at 8:36 am Hi Charles, I am happy to see that the website is still here! I also hope that everything is fine at this hard time in Italy. I need once again some help to understand which statistic is better to use when analyzing some data. I am measuring flow (in ml/min) in blood vessels before to induce a lesion that will block the flow. That is my control group (A) for which I record the time it takes to block the flow. Then I test the effect of a drug (B) into the occlusion time. Sometimes the treatment prolongs, sometimes it decreases this time. Most of the time the effect is concentration dependant: at the highest doses, there is never an occlusion. By never I mean within the predefined time of observation which cannot be infinite nor equal to the lifetime of the animal, of course. So most of the time I have a group A control and then several B1,n… where n is all the different doses of my treatment, and my hypothesis is if the mean of any of this Bn are different from the A control. What is the best way to analyze these kinds of data? I think we used in the past a non-parametric approach but I`m not sure if it is the best. What do you think?Reply
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Charles says:
March 13, 2020 at 2:19 pmHello Alessandro,
This seems like a fit for ANOVA. If you get a significant result you would use Dunnett’s follow-up test. If the assumptions for ANOVA are not met, then you could use a non-parametric test, such as Kruskal-Wallis, or Welch’s ANOVA, but this depends on which assumptions are not met. If you use Kruskal-Wallis, then Steel’s test is probably the appropriate follow-up test. All of these testscan be found at
One-way ANOVA
Charles
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Alessandro Zarpellon says: March 13, 2020 at 8:07 pm Hi Charles, thanks for your quick response! The problem with ANOVA is that in a number of studies we end up having 1 group in which all vessels are open with an arbitrary value of 1900 seconds. This to me does not fit the “Within each sample, the observations are sampled randomly and independently of each other”, because the value is not really random but assigned by me. It could be that next year we decide to go for 2400 as our limit so is arbitrary. It is like if I have all groups good for ANOVA except this one in which I arbitrarily assign a value. My concern, in any case, is not if that group is really different from the control (I mean I decide those values), but if because of the arbitrary group that I have put it messes up all the follow-up tests. I hope I`m making sense. I mean in the most extreme case I can have a group of control with its own variance, and then a treated group in which all values equals 1900 seconds because all vessel stays open indefinitely. How do I analyze a situation like this? I mean they are clearly different, but How do I formalize it?Reply
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Richard Garcia Ishimine says: February 28, 2020 at 5:47 am A mi me piden contraseña. Que pongo?Reply
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Charles says:
February 28, 2020 at 7:14 amHello Richard,
See Password Prompt
Charles
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Charley Kyd says:
February 24, 2020 at 11:15 pm Occasionally, I’ve wanted to run an A/B split test, to decide whether a new method/sales page/whatever is superior to the current method. However, I’ve never seen a way to specify the number of tests needed to reach a decision at a certain level of confidence. What type of test would you suggest? Do you have any examples that Icould read?
Thanks!
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Charles says:
February 25, 2020 at 12:06 pmHi Charley,
It all depends on which test you use. If you use a two-sample t-test as your A/B split test, then typically the sample size (assuming this is what you mean by “number of tests”) required is based on statistical power (as well as significance level and effect size); how to calculate the sample size in this case is described atPower of t Tests
Sample size requirements for t-tests If, instead, you base the sample size on achieving a certain confidence interval, then you can use the fact the confidence interval can be expressed as sample mean +/- s.e. * t-crit where s.e. = standard deviation divided by the square root of the sample size n; t-crit depends on the significance level (i.e. one minus the level of confidence) but also on n since the degrees of freedom for the t-test is n-2. Thus, you must solve for n, which requires using numerical means (since there is not an analytic formula). How to do this is shown in the case of the one-sample t-test at Sample Size based on theConfidence Interval
If you use a different A/B test, then the calculations will differ. E.g. for the Mann-Whitney test, see Power of Mann-Whitney Test Please let me know whether or not I am addressing the question that you are intending to ask.Charles
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Charley Kyd says:
February 25, 2020 at 9:05 pmCharles,
Thanks for the links. I’ll dig into them. I think, however, that I need to back up and describe two common scenarios in web sites. First, suppose we’re trying to determine whether price A or price B yields the greatest total revenue. So we set up a sales page that toggles between price A and price B. How do we know that we’ve had enough trials to accept the results? Second, suppose we want to compare two different subscription pages and our goal is to determine which page delivers the greatest number of subscriptions? Again, how do we know that we’ve had enough trials to accept the results? What test would you recommend in each case?Thanks, Charles!
Charley
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Charles says:
February 26, 2020 at 8:44 amHello Charley,
The previous response is relevant to these sorts of scenarios. I would add one important comment. These scenarios represent business decisions, and so while statistical analyses can be applied, decision theory is probably more applicable. E.g. it costs money to increase the number of trials and so you need to determine whether or not it is worth the money of more trials to reduce the type II error (the statistical power approach) or reduce the confidence intervals and whatever upside return these provide. Of course, if more trials cost little (in time and money), then the more trials the better.Charles
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Lora says:
February 20, 2020 at 11:23 pmHi Charles,
My survey topic is The Economic Analysis of the Fiji Sugar Industry from 2010 -2018. What test shall i used.Thank you.
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Charles says:
February 21, 2020 at 11:26 amLora,
This depends on many factors, but most importantly on what hypothesis do you want to test.Charles
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Nathan Bowen says:
February 14, 2020 at 2:07 am On Mac, the StdRes function is working fine, but I can’t seem to getthe AdjRes to work.
Thanks for your wonderful site and AddInReply
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Charles says:
February 14, 2020 at 9:48 pmHello Nathan,
Glad that you like the website and add-in. I just checked the AdjRes function on my Mac and it seems to be working fine. What sort of problem are you having? Are you getting an error message or are you seeing error values?Charles
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Victor says:
January 28, 2020 at 5:51 pmEstimado Charles,
He instalado el paquete en office 365, siguiendo las instrucciones para el caso, pero, al tratar de utilizar la herramienta de RealStatistics, éste no arranca y debo reinstalar cada vez que cierro un libro de excel en ejecución.Saludos
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Charles says:
January 28, 2020 at 5:58 pmHello Victor,
Perhaps the following webpage will be helpful: http://www.real-statistics.com/appendix/faqs/disappearing-addins-ribbon/Charles
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Victor says:
February 19, 2020 at 4:15 pmHola Charles,
he intentado con el protocolo del enlace y no me es posible solucionarel inconveniente.
saludos
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Charles says:
February 19, 2020 at 6:33 pmHello Victor,
What do you see when you insert the following formula in any cell?=VER()
Charles
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Undraa says:
January 28, 2020 at 5:20 amDear Charles
I have forest plant coverage data by percentage in several years. How to calculate diversity indexes? I have excel data. Could you help me?Reply
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Charles says:
January 28, 2020 at 9:43 am See Diversity IndicesCharles
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Svilen says:
January 26, 2020 at 4:02 pm Hello, Mr Charles Zaiontz! There is a question about the followingtopic:
https://www.real-statistics.com/binomial-and-related-distributions/poisson-distribution/skellam-distribution/ How to find λ and μ, if we only know the probability. In this case, the probabilities are:win – 0.51
lose – 0.19
tie – 0.20
Thank you in advance!Reply
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Charles says:
January 27, 2020 at 10:13 am Generally, you will need data to estimate parameters and not just somesummary statistics.
Charles
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Svilen says:
February 29, 2020 at 1:52 pmHi Charles!
At the beginning of the task we had only two known numbers. Where to find this summary statistics? Thanks!Reply
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Charles says:
March 1, 2020 at 9:36 amHi Svilen,
Sorry, but I don’t understand your question. Can you provide moredetails?
Charles
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William Peck says:
January 13, 2020 at 4:03 pm can’t wait to do logistic regression … I am not a statistician but was put in this role a year ago with the daunting challenge of learning SPSS. I got the basics done in SPSS and learned how to manipulate the software a bit, but am now in the process of eliminating SPSS. 95% of the work formerly done in SPSS could be done in Business Objects (or Excel) using the very basics of data analysis. The other 5% is a LOGISTIC REGRESSION test that I am working on, to replace SPSS solution. Looks like I found my moving buddy!Reply
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Charles says:
January 13, 2020 at 7:10 pmHi William,
Welcome aboard. Yes, you can use the Real Statistics software to perform logistic regression in Excel.Charles
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MIGUEL ANGEL AGUILAR SUAREZsays:
January 6, 2020 at 7:10 pm Hi Charles, is it necessary to have a software access key? If so, could you tell me how to get it. I am a professor of statistical hydrology at the Autonomous University of Chiapas, Mexico, and for us it is important to use your software to calculate droughtsand extreme events.
Regards
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Charles says:
January 7, 2020 at 1:23 pmHello Miguel,
You don’t need a software access key. The software is free. Go to Real Statistics Download.
Charles
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Tiffany J Pereira says: January 2, 2020 at 9:15 pm How would cite this software in a paper? “using the Real-Statistics Using Excel add-in for Microsoft Excel (Charles Zaiontz, 2019).”Reply
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Charles says:
January 3, 2020 at 8:40 am Yes, but also see CitationCharles
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Hugo L.F. says:
December 23, 2019 at 9:53 amHi Charles,
Thank you for this awesome blog! We are using your functions to calculate the sample size for a MANOVA as described here: https://www.real-statistics.com/multivariate-statistics/multivariate-analysis-of-variance-manova/manova-power-and-sample-size/ However, we do not know what are the “small medium large” values for the partial eta-square effect size. Can you help us with this?Regards,
Hugo.
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Charles says:
December 23, 2019 at 8:36 pmHello Hugo,
Glad that you are getting value from the Real Statistics website. small, medium and large are .01, .06 and .14 respectively.Charles
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GERARDO ARDILA DUARTE says: December 15, 2019 at 4:31 am Doc Charles, good night, please how can I make a comparison of two proportions, and one of more proportions? For example, for two proportions in the sex variable, the proportionof men vs. women?
For three characteristics, the ratio of the first vs the second and the third at the same time.Reply
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Charles says:
January 2, 2020 at 9:57 am Hello Gerardo and Happy New Year, 1. If you are comparing men vs women, you can use a t test (or Mann-Whitney if assumptions are not met). Here I am treating proportions just like any other numeric value. Alternatively, you can use the approaches described at http://www.real-statistics.com/binomial-and-related-distributions/proportion-distribution/ 2. I don’t completely understand the scenario that you are describing. This sounds like a fit for ANOVA, but perhaps this is not what you are looking for.Charles
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GERARDO ARDILA DUARTE says: January 9, 2020 at 2:26 pm Doc, thank you very much, the 2nd stage is a case of a single variable, with three or more qualitative in which you want to compare the proportion of three or more qualitative results.Reply
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Charles says:
January 11, 2020 at 10:52 pmHello Gerardo,
I am sorry, but I still don’t understand the situation. Can you email me an Excel file with some sample data, and tell me which columns are supposed to be compared?Charles
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Henry says:
December 14, 2019 at 8:42 pmHi Charles,
Have downloaded. Just starting a ts project. That’s a great resource.Thanks
Henry
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Crispus says:
December 12, 2019 at 6:34 pmDear Dr. Zaiontz,
I have a data set with 1 dependent variable and 3 independentvariables.
All the variables are continuous. The 3 independent variables are metrics of a surgery. The dependent variable is the patient’s pain score. I ran a regression analysis, but I know that my data is not normal. I suppose I could transform the data, but I was wondering if there was some sort of non-parametric procedure that I could use to run such ananalysis.
My data is not from a randomized designed experiment.Any suggestions?
I appreciate your insights.Best regards,
Crispus
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Charles says:
December 14, 2019 at 12:23 pmHello Crispus,
To use regression your data does not need to be normal. The normality requirement only applies to the residuals (that the difference between the actual and predicted values for the dependent variable). Even when the residuals are not normally distributed, the predictions can be used; the usual inferences about the coefficients and confidence intervals for the predictions will depend on the normality of theresiduals.
Charles
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Laurie Roberts says: December 11, 2019 at 3:06 am I can’t tell you how excited I am about finding your website! I have thought about getting back into using my graduate statistics and educational measurement knowledge to learn more from the large amount of data I accumulate in my job as a school psychologist. Sadly, I’m quite rusty, so let me ask for your guidance: to begin, I am thinking I could learn something about whether there are IQ subtests which are more or less predictive of word reading ability. Is this a multiple ANCOVA? Can you tell me where to start? Many thanks!Reply
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Charles says:
December 11, 2019 at 6:02 pmHello Laurie,
Glad that you found the Real Statistics website. There is a lot of information that I hope you find useful. Whether IQ subtests are more or less predictive of word reading ability seems like a use for regression, but I would need more information to give better advice.Charles
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Michael H says:
November 29, 2019 at 1:47 amHey Charles
Thanks for providing such a valuable resource. I’ve got a data set containing forest biomass values (n = 223) across 4 separate vegetation types. The sample sizes for each vegetation type are unequal. I’m looking to see if there is statistical separation between the mean biomass values for each of the vegetation types. I’ve performed a one way ANOVA, and want to use a Scheffe post-hoc, multiple comparison test to see where the significant differences (if any) occur. The output of the Scheffe test requires the input of ‘Scheffe contrast values’. I’m a little confused at how these are calculated. ThanksReply
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Charles says:
November 29, 2019 at 9:23 amHello Michael,
Glad that you are getting value from the resource. If you are only interested in multiple pairwise comparisons, then Scheffe is not the best test to use. If the homogeneity of variances assumption is met then you should probably use Tukey-Kramer (in which case you don’t need to set contrast coefficients). If the homogeneity of variances assumption i not met then Games-Howell is usually a good choice.Charles
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GERARDO ARDILA DUARTE says: November 24, 2019 at 8:06 pm Doctor, thank you very much for the update of very good Real Statistitics software, please excuse my abuse; the probability that more and 2 kaplan Meier curves could be studied could be studied.Reply
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Charles says:
November 25, 2019 at 9:54 amHello Gerardo,
Are you proposing that the Real Statistics software be enhanced to support the comparison of more than two Kaplan-Meier curves?Charles
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GERARDO ARDILA DUARTE says: December 15, 2019 at 4:27 am Yes Sr, I would likeReply
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|Asmaa Eid says:
November 12, 2019 at 7:10 pmHello dear Charles,
My research is about comparing productivity for coastal and inland wetlands, i have two case study areas, and 37 samples from the two sites (17,19), repeated for two seasons summer and winter of year 2019, now i have (stand no, season, area, phytomass of each stand (productivity)), what kind of statistics i can do to analyze my data. when i tried one way anova it gave me no signifcant differences, but in reallity i think there should be significant differences, whatshould i do?
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Charles says:
November 13, 2019 at 11:35 am The test to use depends on the hypothesis you want to test. What hypotheses do you want to test? Since you are likely to be testing multiple dependent variables you may need to use MANOVA, but again this depends on the hypotheses you want to test). There are two possibilities: (1) you really don’t have any significant results and should accept this (and there can be many reasons for this) or (2) you are using the wrong test.Charles
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Crispus says:
December 13, 2019 at 12:38 am There may appear to be no differences due to the possibility of omitted variable bias?Reply
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Shari says:
November 7, 2019 at 7:44 pm What formula would I use for the following? The director of the Office of Human Development wants to know the proportion of welfare recipients who own cars. She wants to know the proportion within 3% and wants to be 95% certain. What sample sizedoes she need?
The sheriff takes a sample of 100 former inmates who went through a retraining program. These inmates are traced through the county’s data system to find out whether they had a job within a year. The sheriff considers anyone with a job to be a success. The sample reveals 68 of the 100 inmates have jobs after within a year. What is the standard error of the proportion?Reply
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Charles says:
November 7, 2019 at 11:22 pmHello Shari,
From your description it looks like you have to (1) perform some statistical test (paragraph 2) and to (2) estimate the necessary sample size based on a confidence interval (paragraph 1). What do you think the correct statistical test should be for (1)? BTW, these sorts of problems are addressed in the Real Statisticswebsite.
Charles
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Dr. Kamal Nain Kapoor says: November 9, 2019 at 11:20 am Ans. to first paragraph::: n=1067 Formula: n=1.96^2*p*q/E^2;;;E=0.03, p=q=0.5(if not given)Next Paragraph
p=0.68, q=1-p, n=100, z(alpha/2)=1.96 (if not given), use above formula for computing E.E=0.914
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Amanda says:
October 27, 2019 at 8:38 am I’m trying to run a power calculation for a binomial logistic regression analysis. I have the Real Statistics Power Data Analysis Tool, but when I open the box, which option do I select?? “One-sample binomial”? Is there a description somewhere of what all the information I need to input is – like I don’t know what to put for ‘p0’, ‘p1’ ..etc.
Please help, I’m hopeless.Reply
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Charles says:
October 29, 2019 at 9:40 amHello Amanda,
I have not yet added power support for logistic regression. I will try to to add this shortly, probably in the November release. G*Power can be used to calculate the power for logistic regression. You can download this tool for free. For the one-sample binomial test, you can find definitions of p0 andp1 at
Power for the Binomial DistributionCharles
Charles
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Cordy says:
October 5, 2019 at 10:11 pm Who do I need to use 13 judges to rank 32 scaled items in KCCReply
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Charles says:
October 6, 2019 at 10:23 pm See Intraclass CorrelationCharles
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Cordy says:
October 5, 2019 at 10:07 pm How do I determine the number of raters or judges to rate my 52 identified variables. Any standard required in the choice of numbers of judges to rank the variablesReply
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Charles says:
October 6, 2019 at 11:25 amHello Cordy,
I haven’t tackled this issue yet, but will do so soon. In the meantime, here is an article that might be helpful: https://www.researchgate.net/post/How_to_compute_a_sample_size_based_on_an_ICC_Intra-class_correlationCharles
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Brian Dates says:
September 19, 2019 at 9:52 pmCharles,
I’m having difficulty with the most recent version of Real Statistics. After deleting the prior version and installing the most recent, I have to restart Real Statistics each time I restart Excel. I need to go to Files->Options->Add-ins and uncheck the Real Statistics box; then go back through Files->Options->Add-ins and check the box. Do you have any idea what the difficulty might be? Thanks.Brian
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Charles says:
September 20, 2019 at 2:32 pmHi Brian,
Do you need to go through this procedure in order (1) to get Real Statistics to show up on the ribbon or (2) to get Ctrl-m to work. If (1), then try using the key sequence Ctrl-Shft-m instead.Charles
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Brian Dates says:
September 20, 2019 at 3:46 pmCharles,
Thanks for the reply. Ctrl-m is the problem.Brian
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Brian Dates says:
September 20, 2019 at 10:49 pm Issue solved. Thanks for the help, Charles.Reply
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Ron says:
September 27, 2019 at 5:12 pmHello Charles,
I’m having the same issue. I have to uncheck then recheck the Real Statistics box to get it to show in the ‘Add-ins’ tab and to get ctrl-m and shift-ctrl-m to work. I have to do this each time I open a new spreadsheet. I’m running Windows 10 Pro 64-bit and Excel for Office 365 MSO (16.0.11328.20418) 32-bit. In any case, Thank You for this great add-in.Ron
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Charles says:
September 27, 2019 at 6:24 pmHello Ron,
I don’t yet have a solution beyond what I have already published (namely rename the file XRealstats.xlam).Charles
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Tonya says:
September 19, 2019 at 3:58 pmCharles,
I hope all is well
I wish to compare quantitative exam scores for two groups. Group 1 and Group 2 are pre-calculus courses that are taught with a common syllabus and have a common final exam with a grading rubric. The only difference is that both groups used different teaching resources. Group 1 (N=116) used the traditional costly textbook and homework software, while Group 2 (N=21) used Open Education Resources – OER). I want to also mention that GR1 data is a total of 4 courses / 4 different instructors. GR2 data is from just one instructor who was exploring the use of OER. Both GR1 and GR2 data are approximately normally distributed so a T test was done. The results from the T test (P>.05) indicated that there is no significant difference letting me know that both groups can attain the same level of achievement on the final. While those results do mimic what research is saying about the comparison of these two settings, I wanted to also see if there would be a difference found in the settings if I compared each course in GR1 to GR2. That is comparing GR1/Instructor 1 exam scores to GR2, GR1/Instructor 2 exam scores to GR2, GR1/Instructor 3 exam scores to GR2 and GR1/Instructor 4 exam scores to GR2. It turns out that 3 of the exam distributions in GR1 were approximately normal while one not normal. I ran a t-test for the 3 normal settings compared with GR2. The results indicated that two of the settings found no difference (P>.05), and one found a significant difference (P<.05). As for the one course that is not normal (N=27), which test should I use to compare the settings with GR2 (n=21 – approx. normal data)? I was thinking to use the Mann U Whitley, however, I am not sure if the distributions have to be the same (normal/normal OR not-normal/not normal). I need your help with this. I also want to know if it’s OK for me just to go with only reporting in my study results the outcome of the first T test I ran with GR1 (N=116) vs GR2(N=21) that found no difference in the settings,..and not reporting the last results I found when I compared each individual course separately to GR2. Thanks in advance for your help!Reply
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Charles says:
September 28, 2019 at 4:10 pmHi Tonya,
1. Since you are performing 4 follow-up tests, you need to take family-wise error into account. The most common approach is to use a Bonferroni correction and use alpha = .05/4 = .0125 as the significance level. There are other choices. See the followingwebpage:
Dealing with Family-wise Error 2. Mann-Whitney is the appropriate test to use for the one case where the data is not normally distributed. If the data is reasonably symmetric you can probably even get away with using the t test 3. In my opinion you should report both results.Charles
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Tonya says:
October 1, 2019 at 4:26 am Thank you so much!!! I really appreciate your feedback.God bless you!
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WILYBARD IPANGELWA says: September 19, 2019 at 11:50 am Good day Charles, can you please give me clear explanation on the meaning of shape and scale parameter?Reply
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Charles says:
September 19, 2019 at 2:02 pm For which distribution?Charles
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Ricardo Dacosta says: September 14, 2019 at 4:29 pm Can this work with Google Sheets?Reply
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Charles says:
September 15, 2019 at 9:05 am Sorry, but no, it doesn’t work with Google Sheets.Charles
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virna says:
September 1, 2019 at 10:05 pmHello Charles,
First of all, thanks for this great tool. I eager to use it on my clases. I am looking for your support on how can use excel program o Real Statistic to weight data (for expample the ones that came with weight on the same variables). I have done this on SPSS with de comand weight by “factor” but dont´know how to do it on Excel. Manythanks.
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Charles says:
September 3, 2019 at 4:52 pmHello Virna,
I don’t use SPSS and so not familiar with this weight capability that you are referring to.Charles
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jms says:
August 29, 2019 at 8:54 pm Neat tool when it works. It frequently disappears from the menu bar. And the latest sweet trick, even though I have the file in the correct roaming directory, solver is checked, etc..it refuses to appear in the menu bar at all now.Reply
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Charles says:
August 30, 2019 at 8:03 am You can always use Ctrl-m to access the data analysis tools. Are you saying that using the key sequence Ctrl-Shft-m doesn’t cause RealStats to appear on the Add-ins ribbon?Charles
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Eric Nolten says:
August 21, 2019 at 1:15 pm I am just a beginner for using excel and statistics. But what I have seen on this website so far makes me very happy. Everything is in a way so structured that i can start immediately with the topics. I’m interested why you developed this website. Is it from an philanthropic attitude or is there an invisible sponsor ? I am very thankful for all this work you have done…..Reply
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Charles says:
August 21, 2019 at 9:25 pmHello Eric,
I am very pleased that you like the website. As I recount at http://www.real-statistics.com/appendix/author/, I started the website so that I could perform statistical analyses for my wife’s research. I continued to develop and expand the website based on two motivations: to give something back to society and to continue to learn and interact with people around the world. I have had a lot of fun doing this and have found the activity to be quite rewarding. There is no invisible sponsor.Charles
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Liza Hipolito says:
August 28, 2019 at 3:28 amHi Charles,
First of all, kudos to you! This is a very helpful tool especially forbeginners.
I tried downloading the file but I can’t seem to install it after multiple attempts. The RealStat doesn’t appear in the AddIns.What do I do?
Thanks!
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Charles says:
August 28, 2019 at 8:00 amHello Liza,
What do you see when you enter the formula =VER() in any cell? What do you see when you press the key sequence Ctrl-m ? Are you using the Windows or Mac version of Excel? Which Excel are you using (Excel 2010, 2016, 365, etc.)?Charles
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Neveen says:
September 7, 2019 at 7:04 pmHi Charles
I am doing a multiple regression analysis, for 5 independent variables and one dependent, along 14 years. So each variable has 14 readings. I have learnt that the sample size should be 30 at least to give called results . in my case , does it mean I have 70 points (readings)or only 14?.
In other words 30 for each variable or all the variabls as a whole?*
Charles says:
September 7, 2019 at 9:55 pmHello Neveen,
Hard for me to say since I don’t know how the number 30 was calculated, but it probably means that you have 5 x 30 = 150 datapoints.
See the following webpage for how to calculate the sample sizerequirement.
http://www.real-statistics.com/multiple-regression/statistical-power-sample-size-multiple-regression/Charles
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Moses says:
August 16, 2019 at 10:19 am I don’t see logistic regression in the tool. I’m a mac user and I opened the tool using ctrl+M. I don’t see it in the regression tab also. Can anyone help me identify? I need help in Linear probability model as well as Logistic regression model.Reply
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Charles says:
August 16, 2019 at 10:37 pmHello Moses,
It is called Binary Logistic and Probit Regression. I don’t know what you mean by a Linear probability model.Charles
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Alexandre Fonseca Torres says: August 13, 2019 at 10:33 pm Hello Charles. I would like to know if you have already created a function that calculates the cummulative probability of a bivariate normal distribution. In the standard Excel (without the Real Stats Add in), it is already possible to calculate the cummulative normal distribution by using thefollowing function:
=dist.norm.n()
I would really like to use a similar function for the bivariate normaldistribution.
Thank you very much for the Real Stats. It has helped me a lot already. In the future, I would like to provide open access solutions as well, if I have the opportunity.Reply
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Charles says:
August 14, 2019 at 12:10 am See the following two webpages: http://www.real-statistics.com/multivariate-statistics/multivariate-normal-distribution/multivariate-normal-distribution-basic-concepts/ http://www.real-statistics.com/multivariate-statistics/multivariate-normal-distribution/multivariate-normality-functions/Charles
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Sara says:
August 9, 2019 at 9:15 pmHi Charles,
Thanks for all the great information. I have a question. I have N attributes, such as N different features of a product and I have two groups of experts who rank these attributes from 1 to N. Also, the number of experts in each group is different. For example, I have 10 in G1 and 15 in G2. So, each one rank the attributes and then I got mean for each attribute for the experts in each group. So, now I have two different ranking (that is in fact the mean of ranking for experts in each group) for these attributes. Which test I can use to tell me if their ranking is different? In other words, I want to know if these two groups of experts have a significant opinion about these attributes. Can I use Excel to perform that test? Thanks much in advance for your guide.Reply
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Charles says:
August 10, 2019 at 9:23 amHello Sara,
If I understand correctly, after taking the means of the rankings, you will have N mean rankings in group G1 and N mean rankings in group G2. This means that you are comparing two vectors each with N elements. This means that you essentially have two samples of size 1. I don’t know of a test which will compare samples of size one.Charles
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Yusuf Akintunde Azeez says: August 8, 2019 at 11:15 pm Thanks for wonderful and great work. I must confess I really benefitted from your work. Sir, I want to ask is there anyway I can make the realstatistics soft permanent on my excel, I had to re-add everytime the need arise. Thank you as I await your responseReply
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Charles says:
August 9, 2019 at 8:08 amHello Yusuf,
If you follow the installation instructions at http://www.real-statistics.com/free-download/real-statistics-resource-pack/ then Real Statistics should be available every time you open Excel. It is important that you follow the installation instructions and not double click on the file containing the software.Charles
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Hossam Attia says:
August 5, 2019 at 1:57 pm Hi Charles , i am looking for your support how can use excel program to estimate the margin cost in Lerner Index , it is regarded as the mark up of price over marginal cost and is a measure of the degree of market power. In other words, a ‘smooth’ measure of the magnitude that price exceeds marginal cost, and is calculated as: 〖Lerner〗_it=(P_it-〖MC〗_it)/P_it Where P_itis the price of total assets for bank i at time t, and 〖MC〗_it is the marginal cost of total assets. The resultant Lerner for each country is averaged over the period under analysis for bank i at time t (Berger et al, 2009). 〖MC〗_it is not directly recognizable for a specific organization, hence are estimated using bounds of a total cost function from the organizations data and obtaining the marginal cost from the subsequent translong costfunction:
〖LnCost〗_it=β_0+β_1 LnQ_it+β_2/2 Ln〖Q_it〗^2+∑_(k=1)^3▒γ_kt lnW_(k,it)+∑_(k=1)^3▒∅_kt LnQ_it lnW_(k,it)+∑_(k=1)^3▒〖∑_(j=1)^3▒Ln W_(k,it) 〗lnW_(j,it)+ ε_it
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Charles says:
August 5, 2019 at 3:53 pmHello Hossam,
I am not familiar with Lerner’s index. Perhaps someone else in the Real Statistics community can respond to your question.Charles
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Hossam Attia says:
August 5, 2019 at 8:56 pm Thanks for your quick response , could you provide with any references i can revert & check with them .regards
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Charles says:
August 7, 2019 at 8:39 amHello Hossam,
Sorry, but I am not familiar with Lerner’s Index and so I don’t know what reference to use.Charles
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Karan says:
July 29, 2019 at 11:02 pm I have found using Excel to be a nightmare for most things. Probably my lack of attention to tutorials. I am going to follow the instructions here and see how I go.Thank you.
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