5.
Getting Started – Excel Menu
QE Tools appears as aamenu
QE Tools appears as menu
option in the main Excel toolbar.
option in the main Excel toolbar.
5
6.
Getting Started – New Data Sheet
QE Tools uses its own data sheet
QE Tools uses its own data sheet
when performing analyses.
when performing analyses.
You may begin by creating an
You may begin by creating an
initial blank datasheet using the
initial blank datasheet using the
New Data Sheet menu pick.
New Data Sheet menu pick.
6
7.
Data Sheets
A new, pre-formatted worksheet is inserted with the
A new, pre-formatted worksheet is inserted with the
name DataSheet.
name DataSheet.
After you create aadata sheet, you can add and
After you create data sheet, you can add and
manipulate data in most of the ways familiar to you in
manipulate data in most of the ways familiar to you in
Excel (e.g., copy, paste, add formulas, etc.).
Excel (e.g., copy, paste, add formulas, etc.).
Note: You must define aavariable name for each data
Note: You must define variable name for each data
series in the Row: “Variable Name”.
series in the Row: “Variable Name”.
Optional, you may include upper and lower
Optional, you may include upper and lower
specification limits (USL and LSL) as well as aatarget
specification limits (USL and LSL) as well as target
(nominal) value for each variable.
(nominal) value for each variable.
These will automatically be referenced for those
These will automatically be referenced for those
tools that require specification limits for analysis.
tools that require specification limits for analysis.
7
8.
Data Format in “DataSheet”
Data variables may either be values or
Data variables may either be values or
calculations of other variables.
calculations of other variables.
Examples:
Examples:
‘TotalVisit’ list values
‘TotalVisit’ list values
‘TotalWait’ and ‘WattoVisit’ are formula.
‘TotalWait’ and ‘WattoVisit’ are formula.
Data for any variable may be constructed
Data for any variable may be constructed
using standard Excel formulas.
using standard Excel formulas. 8
9.
Variable Names
When entering variable names, QE Tools may update
after you enter them or paste from another worksheet.
QE Tools uses an algorithm to standardize variable
names. The algorithm ensures that:
Certain characters are not allowed in variable names.
The following characters are stripped from variable names:
:, , /, ?, *, [, ], ‘ (apostrophe), <space>
Variable names are no longer than 16 characters.
Names that are longer are shortened by using the first 8
and last 8 characters of whatever is entered.
Duplicate names are not allowed to insure QE Tools knows
which variable you wish to analyze.
9
10.
Number of Worksheet Warnings
QETools warns of having too many active
worksheets because performance may be
diminished with increasing file size.
After 30 worksheets, QE tools issues a
warning message.
Recommend creating a second analysis file or
removing unused worksheets.
10
12.
Six Sigma Tool Roadmap
QE Tools provides a Six Sigma problem solving
roadmap with common analysis steps and
hyperlinks to analysis tools and templates.
12
13.
Example: Measure Phase
Blue Text
Represent
Hyperlinks
To Various
Analysis and
Templates
in QE Tools
13
14.
III. Process Analysis –
Qualitative Analysis Tools
Working with ideas / text
14
15.
Process Analysis – Qualitative Tools
SIPOC Diagram
Cause-Effect Diagram * Sample Templates Provided
QFD - House of Quality*
FMEA Table*
Process Control Plan Manufacturing or Transactional*
15
16.
Process Analysis Tools > SIPOC
Sample Excel Data File:
qetools-sampledata.xls
Select Variables Using
SIPOC Dialogue Box
SIPOC Diagram - Loan Process
OUTPUT:
Suppliers Inputs Process Outputs Customers
• Appraisers • Lender • Loan Documents • Mortgage
Programs Customers
• Insurance • Interest Rates • Mortgage • External
Companies Underwriter
• Title Companies • Type of Loan • Lending
Institution
• Government • Loan Value
Step 1: Step 2: Step 3: Step 4: Final:
•Prepare •Process •Underwrite •Clear •Close Loan
Loan Loan Loan Conditions
16
17.
Process Analysis Tools >
Cause-Effect Diagram
May enter data
Directly in dialogue
Box.
However, we recommend
entering reasons for
each cause category
in data sheet column.
17
18.
Process Analysis Tools > Control
Plan
Select
Control
Plan Template
18
19.
QE Tools Control Plan Template
Note: worksheets may be modified per user preference.
19
21.
Process Capability Summary
Data Analysis Tools
Sigma Level Calculator
DPM Calculator - Normal
Process Capability Graphical Summary*
Variable is Normal
Variable is Non-Normal – Best Fit with Weibull Distribution
Variable is Binary – Assume Binomial Distribution
Note: Process Capability Graphical Summary includes:
summary statistics, observed DPM, expected DPM (distribution),
histogram, run charts, box plot, control charts where applicable
21
22.
Process Capability Summary >
Sigma Level Calculator
22
23.
Sigma Level Calculator - Example
Three different methods are available to calculate the “sigma level”
Three different methods are available to calculate the “sigma level”
depending on the format of information available from your process. Enter
depending on the format of information available from your process. Enter
the appropriate information in white boxes and sigma level is calculated
the appropriate information in white boxes and sigma level is calculated
automatically.
automatically.
23
24.
Process Capability Summary >
DPM Calculator - Normal
IfIfdata may be assumed to
data may be assumed to
be normal, you may input
be normal, you may input
the average, standard
the average, standard
deviation and specification
deviation and specification
limits in white boxes and QE
limits in white boxes and QE
Tools automatically
Tools automatically
estimates Defects per
estimates Defects per
million.
million.
24
25.
Process Capability –
Graphical Summary*
Different Process Capability Summaries are
available depending on data / distribution.
Continuous Variable and Normal Distribution
Continuous Variable and Non-Normal –
Best Fit with Weibull Distribution
Binary Variable – Distribution assumed Binomial
*Note: Process Capability Graphical Summary includes:
summary statistics, observed DPM, expected DPM (distribution),
histogram, run charts, box plot, control charts where applicable
25
27.
Process Capability Summary –
Normal – Dialogue Box
Select one or more variables from the variable
Select one or more variables from the variable
list to analyze (note: each variable is output
list to analyze (note: each variable is output
to its own results worksheet).
to its own results worksheet).
Select type of control charts to
Select type of control charts to
display on the results worksheet
display on the results worksheet
(note: subgroup size is assumed 11
(note: subgroup size is assumed
for “Ind / /Moving Range”.
for “Ind Moving Range”.
Options ––
Options
-- show out-of-control patterns.
-- show out-of-control patterns.
-- manual scale run chart
-- manual scale run chart
-- enter specification limits ififnot
-- enter specification limits not
already entered on “data sheet”.
already entered on “data sheet”.
27
28.
Process Capability Summary –
Normal – Using Data Ranges
Optionally select aarange of data to
Optionally select range of data to
analyze from aaworksheet other than
analyze from worksheet other than
the DataSheet (note: the first row is
the DataSheet (note: the first row is
assumed to be aalabel used as the
assumed to be label used as the
variable name).
variable name). 28
29.
Process Capability Summary –
Normal Results
The output contains several sections:
The output contains several sections:
• • Statistical summary
Statistical summary
• • Expected Defects per Million
Expected Defects per Million
(distribution)
(distribution)
• • Observed Defects per Million
Observed Defects per Million
• • Histogram
Histogram
• • Run chart
Run chart
• • Box plot
Box plot
• • Control charts
Control charts
Sample Excel Data File:
qetools-sampledata.xls
Output: Time in Waiting Room
29
30.
Results – Summary Stats - Histogram
Notice that the Upper Specification
Notice that the Upper Specification
Limit (USL) from the datasheet is
Limit (USL) from the datasheet is
displayed on the chart and
displayed on the chart and
summarized in the data output.
summarized in the data output. 30
31.
Results – Run Chart – Box Plot
The Run Chart provides aatime trend.
The Run Chart provides time trend.
Box Plot summarizes basic
Box Plot summarizes basic
distribution. Example shown is skewed
distribution. Example shown is skewed
right (more points >>median).
right (more points median).
31
32.
Process Capability Summary –
Non-normal (Weibull)
32
33.
Results- Non-normal (Weibull)
The output contains:
The output contains:
• • Statistical summary
Statistical summary
• • Expected Defects per Million
Expected Defects per Million
(distribution)
(distribution)
• • Observed Defects per Million
Observed Defects per Million
• • Histogram
Histogram
• • Run chart
Run chart
• • Box plot
Box plot
33
34.
Process Capability Summary –
Binary (Binomial)
34
35.
Process Capability Summary –
Binary (Binomial) Dialogue Box
Select two variables for the
Select two variables for the
analysis (one variable represents
analysis (one variable represents
the number of units and the
the number of units and the
second is for the number
second is for the number
defective).
defective).
Do not enter defective
Do not enter defective
percentages ––QE tools
percentages QE tools
automatically calculates.
automatically calculates.
Alternatively, select one variable
Alternatively, select one variable
for the number defective and
for the number defective and
enter aaconstant sample size.
enter constant sample size.
Specify aatarget for the process
Specify target for the process
(note: the target does not figure
(note: the target does not figure
into any calculations but does
into any calculations but does
appear on the results worksheet
appear on the results worksheet
for reference).
for reference).
35
38.
Descriptive Statistics > Basic
Descriptive Statistics Dialogue Box
Select one or more variables
Select one or more variables
from the variable list to analyze.
from the variable list to analyze.
Stats may also be calculated by
Stats may also be calculated by
aa“grouping variable.” Select aa
“grouping variable.” Select
grouping variable from the
grouping variable from the
variable list or select aarange
variable list or select range
from aaworksheet.
from worksheet.
38
39.
Descriptive Statistics > Basic
Descriptive Statistics – Results
Basic statistics are calculated for one or more variables that
Basic statistics are calculated for one or more variables that
are entered into the analysis.
are entered into the analysis.
One Variable Multiple Output Variables
One Output Variable Stratified by Grouping Variable
Here, output is shown for the
Here, output is shown for the
variable “TotalWait” with aagrouping
variable “TotalWait” with grouping
variable of “Team” (which has
variable of “Team” (which has
values of “A,” “B” and “C”).
values of “A,” “B” and “C”).
39
42.
Graphical Tools >
Run Chart Dialogue Box
Select one or more variables from the variable
Select one or more variables from the variable
list to analyze (note: all output appear on aa
list to analyze (note: all output appear on
single run chart).
single run chart).
Optionally, modify the scale
Optionally, modify the scale
setting for the Y axis.
setting for the Y axis.
42
43.
Run Chart – Sample Results
Single Variable
Or, You May Select
Multiple Variables
43
45.
Graphical Tools >
Pareto Analysis Dialogue Box
Select aadata and aacategory variable to
Select data and category variable to
create aaPareto chart.
create Pareto chart.
Optionally, modify the output
Optionally, modify the output
setting for the left and right Y
setting for the left and right Y
axes.
axes.
45
46.
Graphical Tools >
Pareto Analysis Data Format
Category Sum Data*
*Note: sum data may be calculated by summing up data columns for different
categories, or using the tabulation tool inside QETools. 46
47.
Graphical Tools >
Pareto Analysis – Sample Result
Tool Option: Show Relative and Cumulative Frequency
47
49.
Histogram – Dialogue Box
Replace
Select aavariable from the variable list to
Select variable from the variable list to
analyze (note: ififselect more than one
analyze (note: select more than one
variable, each variable is output to its own
variable, each variable is output to its own
results worksheet).
results worksheet).
Use either Absolute or Relative
Use either Absolute or Relative
Frequency for Y-axis.
Frequency for Y-axis.
49
50.
Histogram – Sample Results
The output contains:
The output contains:
• • Frequency table (frequency of observations
Frequency table (frequency of observations
falling within aacertain data range or bin)
falling within certain data range or bin)
Slide the slider to
Slide the slider to • • Frequency count: (previous bin ~~
dynamically adjust the bin Frequency count: (previous bin
dynamically adjust the bin current bin]
current bin]
widths (and update the
widths (and update the • • Histogram
data table and the Histogram
data table and the
histogram)
histogram)
Enter aa“1ststBin” and/or
Enter “1 Bin” and/or
“Width” value to adjust the
“Width” value to adjust the
output to your liking. “1ststBin”
output to your liking. “1 Bin”
and the slider adjustment (for
and the slider adjustment (for
bin width) can be used
bin width) can be used
simultaneously.
simultaneously. 50
51.
Graphical Tools > Box Plot
(single or multi)
51
52.
Box Plot (single or multi) –
Dialogue Box Example Single
52
57.
Graphical Tools > Scatter Plot
Dialogue Box
Select an input (X) and output
Select an input (X) and output
(Y) variable to plot.
(Y) variable to plot.
Alternatively, select aa2-column
Alternatively, select 2-column
data range from any worksheet
data range from any worksheet
(include data labels in the first
(include data labels in the first
row, and use Column 11for X,
row, and use Column for X,
Col 22for Y).
Col for Y).
Scatter plot chart options:
Scatter plot chart options:
• • Trend line ––show linear,
Trend line show linear,
quadratic, or no trend line.
quadratic, or no trend line.
• • Show R2 2
Show R
• • Show best fit equation
Show best fit equation
57
58.
Graphical Tools > Scatter Plot
Sample Results
Correlation
Coefficient, R R-squared
Linear Model
R R2
0.86 0.73 Timeinwatingroom TotalWait Scatter Plot
160
y = 1.6054x + 17.045
2
140 R = 0.7313
120
100
TotalWait
80
60
40
Note: Scatter Plot also provided 20
under simple regression tool.
0
0 20 40 60 80 100
Timeinwatingroom
58
60.
Regression and Correlation >
Correlation Matrix
60
61.
Regression and Correlation >
Correlation Matrix Dialogue Box
Select one or more variables from the
Select one or more variables from the
variable list to analyze.
variable list to analyze.
Variables must contain numeric data to
Variables must contain numeric data to
be included in aacorrelation matrix.
be included in correlation matrix.
“Text” data will be omitted [more].
“Text” data will be omitted [more].
Enter aathreshold to highlight data
Enter threshold to highlight data
with aastrong correlation
with strong correlation
(|correlation| >>threshold will be
(|correlation| threshold will be
highlighted). Typically, aa0.7
highlighted). Typically, 0.7
cutoff is standard to indicate aa
cutoff is standard to indicate
strong correlation.
strong correlation. 61
62.
Regression and Correlation >
Correlation Matrix, R, Results
Matrix shows all pairwise comparisons of
selected variables (Max 50 variables).
Data with aacorrelation stronger than the threshold is emphasized with bold
Data with correlation stronger than the threshold is emphasized with bold
text (note: either aastrong positive or strong negative will be emphasized).
text (note: either strong positive or strong negative will be emphasized).
62
63.
Regression and Correlation >
Simple Linear Regression
63
64.
Regression and Correlation >
Simple Regression Dialogue Box
Select the variables to analyze.
Select the variables to analyze.
X and Y variables must have the
X and Y variables must have the
same N or the analysis will
same N or the analysis will
terminate.
terminate.
64
65.
Regression and Correlation >
Simple Linear Regression Results
Response optimizer allows you to input an X and
Response optimizer allows you to input an X and
solve for Y (or Vice Versa) based on best fit equation.
solve for Y (or Vice Versa) based on best fit equation.
Alternatively, you may use slider buttons directly in graph. 65
Alternatively, you may use slider buttons directly in graph.
67.
1. Measurement Systems Analysis
> Gage R and R
67
68.
Measurement Systems Analysis >
Gage R and R Template
68
69.
Measurement Systems Analysis >
Gage R and R – Header Section
Needed for Calculations Width = USL- LSL
Enter Study Variation Multiplier: K=5.15 or 6
5.15 (predict 99% of the area under normal distribution curve),
Or 6 (predict 99.73% of the area under normal distribution curve)
69
70.
Measurement Systems Analysis >
Gage R and R – Data Entry Section
Data Entry Section for Measurement System Study
Max 10 parts, 3 operators, 3 trials
70
72.
Measurement Systems Analysis >
Gage R and R -- Calculations
See Notes for Formulas
Sample Output
72
73.
2. Measurement Systems Analysis
> Repeated Measurements Study
73
74.
Measurement Systems Analysis >> Repeated
Measurements Study Template
Enter Data
in White Cells
74
75.
Data Entry
Max = 50 pairs Note: % tolerance calculations
Requires a USL and LSL to
obtain tolerance width for
Note:
Calculations in Yellow
75
76.
Sample Results
Solid Line: Best Fit
To remove best fit
line, click on line on
graph and hit delete.
76
77.
3. Measurement Systems Analysis >
Attribute Matching Study
77
78.
Measurement Systems Analysis >
Attribute Matching Study - Template
Enter Data --
Must include a
Standard (or
reference)
in 1st Column
Enter Alpha
Enter Data for (Type I) error
1..3 Appraisers
in additional
columns Note:
Calculations
In yellow box
Max 50 samples
78
79.
Measurement Systems Analysis >
Attribute Matching Study - Template
OK, if
confidence
intervals
overlap
79
82.
Control Charts > X-bar / Range
Dialogue Box – Data in 1 column
Data Entry Options
All data in 1 column
Subgroup size every XX rows
Sample Data Sheet
Etc. 82
83.
Control Charts > X-bar / Range
Dialogue Box – Data across columns
Data Across Columns
Each row is a subgroup
Select 2 or more variables
Sample Data Sheet
83
84.
Control Charts >
X-bar / Range Sample Output
Note: after creating
A chart, you may
Exclude Points and hit
“UPDATE CHARTS” to
reconfigure control charts.
84
85.
2) Control Charts >
Individual / Moving Range
85
86.
Control Charts > Individual /
Moving Range Dialogue Box
Select one or more variables from the
Select one or more variables from the
variable list to analyze (note: each variable is
variable list to analyze (note: each variable is
outputted to its own results worksheet).
outputted to its own results worksheet).
Data can also be selected using aa
Data can also be selected using
1-column data range in any worksheet
1-column data range in any worksheet
(Include aalabel name in the first row).
(Include label name in the first row).
Check this box to show out-of-control
Check this box to show out-of-control
data points on the control charts
data points on the control charts
86
87.
Control Charts > Individual /
Moving Range – Sample Output
etc ..
Note: after creating
A chart, you may
Exclude Points and hit
“UPDATE CHARTS” to
reconfigure charts.
87
89.
Control Charts > P-chart
Dialogue Box
Select two variables ––
Select two variables
one with number of units inspected and
one with number of units inspected and
another with number of defective units. .
another with number of defective units
Do not enter the “Percent Defective (p)” ––
Do not enter the “Percent Defective (p)”
QETools calculates this in the analysis.
QETools calculates this in the analysis.
Alternatively, specify aaconstant sample
Alternatively, specify constant sample
size and select aasingle variable with the
size and select single variable with the
number of defective units. .
number of defective units
Data also can be selected using aa2-
Data also can be selected using 2-
column data range.
column data range.
(Col 1: Number of Units;
(Col 1: Number of Units;
Col2: Number of Defects;
Col2: Number of Defects;
Include label names in the first row).
Include label names in the first row).
89
90.
Control Charts > P-chart Sample
Results (Unequal Sample Size)
Note: after creating
a chart, you may
Exclude Points and hit
“UPDATE CHARTS” to
reconfigure charts. Only appears if unequal sample size
90
91.
Control Charts > P-chart Sample
Results (Constant Sample Size)
Example with
constant subgroup
sample size
P Chart
0.40
0.35 0.35
0.30 0.29
0.25
Sample p
0.23
0.20
0.15
0.10
1 3 5 7 9 11 13 15 17 19 21 23 25
Subgroup
91
95.
Control Charts > U-chart
Dialogue Box
Enter variable with the #
units (subgroup sizes) or
constant sample size.
Enter # defects (errors)
95
96.
Control Charts > U-chart Sample
Output (Unequal Sample Size)
Note: after creating
a chart, you may
Exclude Points and hit
“UPDATE CHARTS” to
reconfigure charts.
Only appears if unequal sample size
96
97.
Control Charts > U-chart
with Constant Sample Size
97
102.
Tabulation > Cross Tabulation –
Dialogue Box
Data Entry Options:
Data Entry Options:
One or more variables from
One or more variables from
Datasheet
Datasheet
Or,
Or,
Data range in excel worksheet
Data range in excel worksheet
Tabulation may also be
Tabulation may also be
performed using aa“grouping
performed using “grouping
variable.”
variable.”
A grouping variable may be
A grouping variable may be
selected from the variable list
selected from the variable list
or using aaworksheet range.
or using worksheet range.
102
103.
Tabulation > Cross Tabulation –
Sample Output (binary data)
Sample output (binary data)
Data are
Data are
tallied by aa
tallied by Overall binary
Overall binary
grouping
grouping data are summed
data are summed
variable
variable to the right.
to the right.
(“LikelyReturn”)
(“LikelyReturn”)
Totals are calculated for each
Totals are calculated for each
variable in the analysis.
variable in the analysis.
103
104.
Tabulation >
Cross Tabulation –
Sample Output
(non-binary data)
Data are
Data are
tallied by aa
tallied by
grouping
grouping
variable
variable
(“#Concerns”)
(“#Concerns”)
Group totals and
Group totals and
overall data are
overall data are
summed to the right.
summed to the right.
Totals are calculated for
Totals are calculated for
each variable in the
each variable in the
analysis.
analysis. 104
106.
Tabulation > Binary Cross
Tabulation – Dialogue Box
Data Entry Options:
Data Entry Options:
One or more variables from
One or more variables from
Datasheet
Datasheet
Or,
Or,
Data range in excel worksheet
Data range in excel worksheet
Note: Data must be binary (0/1).
Note: Data must be binary (0/1).
Tabulation may also be
Tabulation may also be
performed using aa“grouping
performed using “grouping
variable.”
variable.”
A grouping variable may be
A grouping variable may be
selected from the variable list
selected from the variable list
or using aaworksheet range.
or using worksheet range.
106
107.
Tabulation > Binary Cross
Tabulation – Sample Output
(no grouping variable)
Data are tallied by 0/1
Data are tallied by 0/1 Binary data and
Binary data and
and totaled for each
and totaled for each overall data are
overall data are
Overall DPMO is variable in the analysis.
variable in the analysis. summed to the right.
summed to the right.
Overall DPMO is
calculated when aa Row% and Column% are
Row% and Column% are
calculated when
defect (failure) is also calculated.
also calculated.
defect (failure) is
coded as 1.
coded as 1.
Ex: PoorMealService
Ex: PoorMealService
Row% ==111/381
Row% 111/381
Column% ==111/447
Column% 111/447
107
108.
Tabulation > Binary Cross
Tabulation – Sample Output
(w/ grouping variable)
Data are
Data are
tallied by aa
tallied by
grouping
grouping Data are tallied by 0/1
Data are tallied by 0/1 Grouped (binary)
Grouped (binary)
variable
variable and totaled for each
and totaled for each and overall data
and overall data
(“LikelyReturn”) variable in the analysis. are summed to the
(“LikelyReturn”) variable in the analysis. are summed to the
Overall DPMO is right.
right.
Overall DPMO is
calculated when aa
calculated when
defect (failure) is
defect (failure) is
coded as 1.
coded as 1. 108
111.
Hypothesis Tests > Test Two
Variances Dialogue Box
Data Entry Options:
Data Entry Options:
Two Variables from Datasheet
Two Variables from Datasheet
Data range in excel worksheet
Data range in excel worksheet
Or,
Or,
Using Summary Statistics
Using Summary Statistics
Need to enter ififone or two-tail
Need to enter one or two-tail
hypothesis test
hypothesis test
Two-tail:
Two-tail:
Alt Hypothesis is <>
Alt Hypothesis is <>
One-tail:
One-tail:
Alt Hypothesis is Max >>Min
Alt Hypothesis is Max Min
Alpha: Type I Ierror
Alpha: Type error
(default ==0.05)
(default 0.05) 111
112.
Hypothesis Tests > Test Two
Variances – Sample Results
Statistical Test Conclusion:
Returns either “Difference exists” or “No difference”
112
113.
Hypothesis Tests > Test Two Variances –
Summary Data and Results
Example: 1 Sided Test
113
114.
2) Hypothesis Tests >
Test Two Means - Independent
114
115.
Hypothesis Tests > Test Two Means –
Independent Dialogue Box
Data Entry Options:
Data Entry Options:
Two Variables from Datasheet
Two Variables from Datasheet
Data range in excel worksheet
Data range in excel worksheet
Or,
Or,
Using Summary Statistics
Using Summary Statistics
Need to enter ififone or two-tail
Need to enter one or two-tail
hypothesis test
hypothesis test
Two-tail:
Two-tail:
Alt Hypothesis is <>
Alt Hypothesis is <>
One-tail:
One-tail:
Alt Hypothesis is Max >>Min
Alt Hypothesis is Max Min
Alpha: Type I Ierror
Alpha: Type error
(default ==0.05)
(default 0.05) 115
116.
Hypothesis Tests > Test Two
Means – Independent Results
116
117.
Hypothesis Tests > Test Two
Means – Summary Statistics
117
119.
Hypothesis Tests > Test Paired
Data Dialogue Box
Data Entry Options:
Data Entry Options:
Two Variables from Datasheet
Two Variables from Datasheet
Data range in excel worksheet
Data range in excel worksheet
Or,
Or,
Using Summary Statistics
Using Summary Statistics
Need to enter ififone or two-tail
Need to enter one or two-tail
hypothesis test
hypothesis test
Recommend 22tail for paired
Recommend tail for paired
Alpha: Type I Ierror
Alpha: Type error
(default ==0.05)
(default 0.05)
119
120.
Hypothesis Tests >
Test Paired Data Sample Results
Hypothesis Test: Mean Difference = 0
Alternative Hypothesis: Difference <> 0
*Note: Repeated Measurements
Analysis under Measurement Note: Box Plot of Differences
Systems performs also performs a
Paired t-test analysis
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121.
4) Hypothesis Tests > Test Two
Proportions
121
122.
Hypothesis Tests > Test Two
Proportions Dialogue Box
Data Entry Options:
Data Entry Options:
Two Variables from Datasheet
Two Variables from Datasheet
(Note: each variable must be aa
(Note: each variable must be
column of 00and 1)
column of and 1)
Data range in excel worksheet
Data range in excel worksheet
Or,
Or,
Using Summary Statistics
Using Summary Statistics
Need to enter ififone or two-tail
Need to enter one or two-tail
hypothesis test
hypothesis test
Two-tail: Alt Hypothesis is <>
Two-tail: Alt Hypothesis is <>
One-tail: Alt Hypothesis is
One-tail: Alt Hypothesis is
Large Prop >>Small Prop
Large Prop Small Prop
Alpha: Type I Ierror
Alpha: Type error
(default ==0.05)
(default 0.05)
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123.
Hypothesis Tests > Test Two
Proportions Sample Results
Hypothesis: P1 = P2 (2-Tail) Hypothesis: P(Large) > P(small)
Alt Hypothesis: P1 not equal P2 Alt Hypothesis: P(Large) <= P(small)
Alt Hypothesis Identified
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124.
Hypothesis Tests > Test Two
Proportions
Example Shown Below:
Data fail normal approximation test
for proportions
Here, a warning message is given.
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