Embed Size (px)
DESCRIPTION
An overview of SigmaXL's Statistical Tools Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation. SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies. Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course. DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes. DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.
Citation preview
Stats Tools: Feature Overview
Two-Sample Comparison Tests
One-Way Anova & Means Matrix
Correlation Matrix
Multiple Linear Regression
Multiple Regression
Predicted Response Calculator
Statistical Tools - SigmaXL®
Version 6.1
Multiple Regression with Residuals Plots
Binary & Ordinal Logistic Regression
Non-Parametric Tests
Chi-Square Test
Power & Sample Size Calculators
Power & Sample Size Charts
Statistical Tools
P-values turn red when results are significant (p-value < alpha)
Descriptive Statistics including Anderson-Darling Normality test, Skewness and Kurtosis with p-values
1 Sample t-test and confidence intervals
Paired t-test, 2 Sample t-test
2 Sample Comparison Tests
Normality, Mean, Variance, Median
Yellow Highlight to aid Interpretation Back to Index
Statistical Tools
One-Way ANOVA and Means Matrix
Two-Way ANOVA Balanced and Unbalanced
Equal Variance Tests:
Bartlett
Levene
Welch’s ANOVA
Correlation Matrix Pearson’s Correlation Coefficient
Spearman’s Rank
Back to Index
Statistical Tools
Multiple Linear Regression
Binary and Ordinal Logistic Regression
Chi-Square Test (Stacked Column data and
Two-Way Table data)
Nonparametric Tests
Power and Sample Size Calculators
Power and Sample Size Charts
Back to Index
Statistical Tools:
Two-Sample Comparison
Tests
P-values turn red
when results are
significant! Rules based
yellow highlight to
aid interpretation!
Back to Index
Statistical Tools: One-Way
ANOVA & Means Matrix
3.08
3.28
3.48
3.68
3.88
4.08
4.28
4.48
1 2 3
Customer Type
Mean
/CI -
Overa
ll S
ati
sfa
cti
on
Back to Index
Statistical Tools:
Correlation Matrix
Back to Index
Statistical Tools:
Multiple Linear Regression
Accepts continuous and/or categorical (discrete)
predictors.
Categorical Predictors are coded with a 0,1 scheme
making the interpretation easier than the -1,0,1
scheme used by competitive products.
Interactive Predicted Response Calculator with
95% Confidence Interval and 95% Prediction
Interval.
Back to Index
Statistical Tools:
Multiple Linear Regression
Residual plots: histogram, normal probability plot, residuals vs. time, residuals vs. predicted and residuals vs. X factors
Residual types include Regular, Standardized, Studentized
Cook's Distance (Influence), Leverage and DFITS
Highlight of significant outliers in residuals
Durbin-Watson Test for Autocorrelation in Residuals with p-value
Pure Error and Lack-of-fit report
Collinearity Variance Inflation Factor (VIF) and Tolerance report
Fit Intercept is optional Back to Index
Statistical Tools:
Multiple Regression
Multiple Regression accepts Continuous and/or
Categorical Predictors! Back to Index
Statistical Tools:
Multiple Regression
Durbin-Watson Test with p-values
for positive and negative
autocorrelation! Back to Index
Statistical Tools: Multiple
Regression – Predicted
Response Calculator with
Confidence Intervals
Easy-to-use Calculator with
Confidence Intervals and Prediction Intervals!
Back to Index
Statistical Tools:
Multiple Regression with
Residual Plots
0
10
20
30
40
50
60
-0.88
-0.71
-0.54
-0.37
-0.19
-0.02
0.15
0.32
0.50
0.67
0.84
1.01
1.19
Regular Residuals
Fre
qu
en
cy
-3
-2
-1
0
1
2
3
-0.90
-0.40
0.10
0.60
1.10
Residuals
NS
CO
RE
-1
-0.5
0
0.5
1
1.5
0.00
20.00
40.00
60.00
80.00
100.00
120.00
Fitted Values
Reg
ula
r R
esid
uals
-1.00
-0.50
0.00
0.50
1.00
1.50
0 20 40 60 80 100
120
Observation Order
Reg
ula
r R
esid
uals
Back to Index
Statistical Tools:
Binary and Ordinal
Logistic Regression
Powerful and user-friendly logistic regression.
Report includes a calculator to predict the response
event probability for a given set of input X values.
Categorical (discrete) predictors can be included in the
model in addition to continuous predictors.
Model summary and goodness of fit tests including
Likelihood Ratio Chi-Square, Pseudo R-Square, Pearson
Residuals Chi-Square, Deviance Residuals Chi-Square,
Observed and Predicted Outcomes – Percent Correctly
Predicted.
Back to Index
Statistical Tools:
Nonparametric Tests
1 Sample Sign
1 Sample Wilcoxon
2 Sample Mann-Whitney
Kruskal-Wallis Median Test
Mood’s Median Test
Kruskal-Wallis and Mood’s include a graph of Group Medians and 95% Median Confidence Intervals
Runs Test Back to Index
Statistical Tools:
Chi-Square Test
Back to Index
Statistical Tools: Power &
Sample Size Calculators
1 Sample t-Test
2 Sample t-Test
One-Way ANOVA
1 Proportion Test
2 Proportions Test
The Power and Sample Size Calculators
allow you to solve for Power (1 – Beta),
Sample Size, or Difference (specify two, solve
for the third). Back to Index
Statistical Tools: Power &
Sample Size Charts
Power & Sample Size: 1 Sample t-Test
0
0.2
0.4
0.6
0.8
1
1.2
0 10 20 30 40 50 60
Sample Size (N)
Po
wer
(1 -
Beta
)
Difference = 0.5
Difference = 1
Difference = 1.5
Difference = 2
Difference = 2.5
Difference = 3
Back to Index
