Harold analysis independent t test part 2 gender jobper motiv
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This document summarizes the results of independent t-tests run to compare the mean scores of two groups (males and females) on measures of motivation and job performance. It provides statistics on the number of cases analyzed, reports the mean and standard deviation for each group on each measure, and presents the results of Levene's test and the independent samples t-test for each comparison. No significant differences between males and females were found for motivation or job performance.
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![T-Test
Notes
Output Created 21-SEP-2021 08:56:47
Comments
Input Filter <none>
Weight <none>
Split File <none>
N of Rows in Working
Data File
181
Missing Value
Handling
Definition of Missing User defined missing values are
treated as missing.
Cases Used Statistics for each analysis are based
on the cases with no missing or
out-of-range data for any variable in
the analysis.
Syntax T-TEST
GROUPS = GENDER(1 2)
/MISSING = ANALYSIS
/VARIABLES = MOTIV JOBPERF
/CRITERIA = CI(.95) .
Resources Elapsed Time
0:00:00.09
[DataSet2]
Group Statistics
GENDER N Mean Std. Deviation
Std. Error
Mean
MOTIV 1 141 3.8779 .34345 .02892
2 39 3.9287 .28569 .04575
JOBPERF 1 141 4.2379 .40973 .03451
2 39 4.1713 .33261 .05326
Independent Samples Test
Levene's Test for Equality
of Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval of
the Difference
Lower Upper
MOTIV Equal variances
assumed 2.628 .107 -.847 178 .398 -.05085 .06006 -.16937 .06768
Equal variances not
assumed -.939 71.359 .351 -.05085 .05412 -.15875 .05706](https://image.slidesharecdn.com/haroldanalysisindependentttestpart2genderjobpermotiv-210923115516/85/Harold-analysis-independent-t-test-part-2-gender-jobper-motiv-1-320.jpg)
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Output
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This document contains the results of two one-way ANOVAs that analyzed the relationship between attitude and department (DataSet1) and attitude and gender (DataSet2). For DataSet1, post hoc tests found no significant differences in attitude between departments. For DataSet2, ANOVA found a significant effect of gender on attitude, and follow up t-tests showed females had significantly lower attitudes than males.TCI in general pracice - reliability (2006)
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OutputUniversity of the Philippines-Los Banos, De La Salle University-Manila, Cavite State University
This document contains the results of two one-way ANOVAs that analyzed the relationship between attitude and department (DataSet1) and attitude and gender (DataSet2). For DataSet1, post hoc tests found no significant differences in attitude between departments. For DataSet2, ANOVA found a significant effect of gender on attitude, and follow up t-tests showed females had significantly lower attitudes than males.TCI in general pracice - reliability (2006)
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2) The GLM assumes linear relationships between variables and additive effects of predictor variables. However, it can accommodate non-linear and non-additive effects through transformations or moderation analysis.
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Output
The document describes analyses conducted in SPSS to examine the relationships between various variables. Regression analysis was performed with Q1 as the dependent variable and race, age, marital status, income, and education as predictors. No significant relationships were found. Independent samples t-tests were also conducted to compare gender by race, but errors occurred due to invalid or missing data in the dataset.
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This document provides information about independent samples t-tests and dependent/paired samples t-tests. It explains that independent samples t-tests are used to compare two independent groups, while dependent samples t-tests are used to compare observations within a single group. The key steps for each test are outlined, including stating hypotheses, calculating the test statistic, determining critical values, and making conclusions. Examples are provided to demonstrate how to perform the tests by hand and using SPSS.
SPSS.ppt
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This spss analysis file it contains basis analysis tips
Data Analysis file, which may help to analyze the data
om
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om
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Review the SPSS output file which reports the results of the indepen.docx
Review the SPSS output file which reports the results of the independent t-test to compare the mean price per 6-pack for regular vs. reduced calorie brands of wheat beer. Answer the following questions based on your observations of the SPSS output file:
Is the F-value for Levene's Test for Equality of Variances significant (that is, is the
p
<
.05)? What is the exact significance level? Note: if the Levene's Test for Equality of Variances is significant, then equal variances cannot be assumed.
Which
t-
value should you use now that you know whether or not equal variances can be assumed? What is that
t
-value?
What is the two-tailed significance level for that
t
-value? State the exact significance value. Report the results of the
t
-test as follows:
t
(df value) = ___, and
p
-value.
Is there a statistically significant difference between the price per 6-pack of regular beers versus reduced calorie beers?
T-TEST GROUPS=Reducedcals(0 1)
/MISSING=ANALYSIS
/VARIABLES=price
/CRITERIA=CI(.95).
T-Test
Notes
Output Created
07-JUN-2013 11:20:55
Comments
Input
Data
C:\Users\donn\Documents\GCU Lead fac\Project with Judy for modifying PSY845 to introduce SPSS\drinks database -revised for course applications DH.sav
Active Dataset
DataSet1
File Label
SPSS/PC+
Filter
Weight
Split File
N of Rows in Working Data File
35
Missing Value Handling
Definition of Missing
User defined missing values are treated as missing.
Cases Used
Statistics for each analysis are based on the cases with no missing or out-of-range data for any variable in the analysis.
Syntax
T-TEST GROUPS=Reducedcals(0 1)
/MISSING=ANALYSIS
/VARIABLES=price
/CRITERIA=CI(.95).
Resources
Processor Time
00:00:00.02
Elapsed Time
00:00:00.01
[DataSet1] C:\Users\donn\Documents\GCU Lead fac\Project with Judy for modifying PSY845 to introduce SPSS\drinks database -revised for course applications DH.sav
Group Statistics
Regular or reduced calorie for brand
N
Mean
Std. Deviation
Price per 6-pack for brand
Regular
28
3.1154
1.24040
Reduced Calories
7
2.6757
.21870
Group Statistics
Regular or reduced calorie for brand
Std. Error Mean
Price per 6-pack for brand
Regular
.23441
Reduced Calories
.08266
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
Price per 6-pack for brand
Equal variances assumed
6.987
.012
.924
Equal variances not assumed
1.769
Independent Samples Test
t-test for Equality of Means
df
Sig. (2-tailed)
Mean Difference
Price per 6-pack for brand
Equal variances assumed
33
.362
.43964
Equal variances not assumed
31.912
.086
.43964
Independent Samples Test
t-test for Equality of Means
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
Price per 6-pack for brand
Equal variances assumed
.47576
-.52829
1.40758
Equal variances not assumed
.24856
-.06671
.94600
.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.157.71.012573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 227.80.897315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB459.21.03857421001605.51METhe column labels in the table mean:549.51.0314836901605.71MDID – Employee sample number Salary – Salary in thousands 675.71.1306736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.71.0434032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.41.018233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise980.81.206674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.61.027233080714.71FAGender1 (Male or Female)Compa - salary divided by midpoint1123.61.02423411001914.81FA1266.91.1745752952204.50ME1341.61.0414030100214.70FC1421.50.93623329012161FA1524.41.059233280814.91FA16390.975404490405.70MC1768.81.2075727553131FE1834.91.1263131801115.60FB1923.21.008233285104.61MA20361.1603144701614.80FB2175.31.1246743951306.31MF2256.71.182484865613.81FD2322.60.984233665613.30FA2451.51.072483075913.80FD2525.51.1092341704040MA2622.90.994232295216.20FA2743.51.088403580703.91MC2874.41.111674495914.40FF2973.51.097675295505.40MF3045.70.9524845901804.30MD3123.71.031232960413.91FA3226.90.867312595405.60MB3355.10.967573590905.51ME34280.904312680204.91MB3521.90.953232390415.30FA3623.71.032232775314.30FA3723.21.010232295216.20FA3857.61.0105745951104.50ME3934.31.108312790615.50FB4024.41.062232490206.30MA4140.51.012402580504.30MC4223.31.0122332100815.71FA4377.21.1526742952015.50FF4456.90.9995745901605.21ME4557.71.202483695815.21FD4665.41.1485739752003.91ME4756.80.997573795505.51ME4859.71.0485734901115.31FE4962.41.0955741952106.60ME5056.50.9925738801204.60ME
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, .
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Similar to Harold analysis independent t test part 2 gender jobper motiv
Output
The document describes analyses conducted in SPSS to examine the relationships between various variables. Regression analysis was performed with Q1 as the dependent variable and race, age, marital status, income, and education as predictors. No significant relationships were found. Independent samples t-tests were also conducted to compare gender by race, but errors occurred due to invalid or missing data in the dataset.
Unit-5.-t-test.ppt
This document provides information about independent samples t-tests and dependent/paired samples t-tests. It explains that independent samples t-tests are used to compare two independent groups, while dependent samples t-tests are used to compare observations within a single group. The key steps for each test are outlined, including stating hypotheses, calculating the test statistic, determining critical values, and making conclusions. Examples are provided to demonstrate how to perform the tests by hand and using SPSS.
SPSS.ppt
SPSS is a popular statistical software package used for data management and analysis. It allows users to perform complex calculations and analyses with simple commands. The default window contains a data editor with two views - the data view shows raw data while the variable view describes each variable. SPSS can analyze different scales of measurement like nominal, ordinal, interval, and ratio scales. Common analyses introduced include frequencies, descriptives to find averages and standard deviations, linear regression, t-tests to compare groups, and graphs to visualize results.
This spss analysis file it contains basis analysis tips
Data Analysis file, which may help to analyze the data
om
The document discusses design of experiments (DOE) and Taguchi methods. It explains key concepts in Taguchi's approach such as quality robustness, quality loss function, and target specifications. Taguchi techniques use experimental design methods to identify important variables affecting a product or process and optimize the design. The document provides examples of full and fractional factorial experiments to study the effects of multiple factors and interactions. It emphasizes the importance of considering interactions between factors rather than just main effects.
om
The document discusses design of experiments (DOE) and Taguchi methods. It explains key concepts in Taguchi's approach such as quality robustness, quality loss function, and target specifications. Taguchi techniques use experimental design methods to identify important variables affecting a product or process and optimize the design. The document provides examples of full and fractional factorial experiments to study the effects of multiple factors and interactions. It emphasizes the importance of considering interactions between factors rather than just main effects.
Review the SPSS output file which reports the results of the indepen.docx
Review the SPSS output file which reports the results of the independent t-test to compare the mean price per 6-pack for regular vs. reduced calorie brands of wheat beer. Answer the following questions based on your observations of the SPSS output file:
Is the F-value for Levene's Test for Equality of Variances significant (that is, is the
p
<
.05)? What is the exact significance level? Note: if the Levene's Test for Equality of Variances is significant, then equal variances cannot be assumed.
Which
t-
value should you use now that you know whether or not equal variances can be assumed? What is that
t
-value?
What is the two-tailed significance level for that
t
-value? State the exact significance value. Report the results of the
t
-test as follows:
t
(df value) = ___, and
p
-value.
Is there a statistically significant difference between the price per 6-pack of regular beers versus reduced calorie beers?
T-TEST GROUPS=Reducedcals(0 1)
/MISSING=ANALYSIS
/VARIABLES=price
/CRITERIA=CI(.95).
T-Test
Notes
Output Created
07-JUN-2013 11:20:55
Comments
Input
Data
C:\Users\donn\Documents\GCU Lead fac\Project with Judy for modifying PSY845 to introduce SPSS\drinks database -revised for course applications DH.sav
Active Dataset
DataSet1
File Label
SPSS/PC+
Filter
Weight
Split File
N of Rows in Working Data File
35
Missing Value Handling
Definition of Missing
User defined missing values are treated as missing.
Cases Used
Statistics for each analysis are based on the cases with no missing or out-of-range data for any variable in the analysis.
Syntax
T-TEST GROUPS=Reducedcals(0 1)
/MISSING=ANALYSIS
/VARIABLES=price
/CRITERIA=CI(.95).
Resources
Processor Time
00:00:00.02
Elapsed Time
00:00:00.01
[DataSet1] C:\Users\donn\Documents\GCU Lead fac\Project with Judy for modifying PSY845 to introduce SPSS\drinks database -revised for course applications DH.sav
Group Statistics
Regular or reduced calorie for brand
N
Mean
Std. Deviation
Price per 6-pack for brand
Regular
28
3.1154
1.24040
Reduced Calories
7
2.6757
.21870
Group Statistics
Regular or reduced calorie for brand
Std. Error Mean
Price per 6-pack for brand
Regular
.23441
Reduced Calories
.08266
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
Price per 6-pack for brand
Equal variances assumed
6.987
.012
.924
Equal variances not assumed
1.769
Independent Samples Test
t-test for Equality of Means
df
Sig. (2-tailed)
Mean Difference
Price per 6-pack for brand
Equal variances assumed
33
.362
.43964
Equal variances not assumed
31.912
.086
.43964
Independent Samples Test
t-test for Equality of Means
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
Price per 6-pack for brand
Equal variances assumed
.47576
-.52829
1.40758
Equal variances not assumed
.24856
-.06671
.94600
.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.157.71.012573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 227.80.897315280703.90MBNote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3341.096313075513.61FB459.21.03857421001605.51METhe column labels in the table mean:549.51.0314836901605.71MDID – Employee sample number Salary – Salary in thousands 675.71.1306736701204.51MFAge – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)741.71.0434032100815.71FCService – Years of service (rounded)Gender – 0 = male, 1 = female 823.41.018233290915.81FAMidpoint – salary grade midpoint Raise – percent of last raise980.81.206674910010041MFGrade – job/pay gradeDegree (0= BS\BA 1 = MS)1023.61.027233080714.71FAGender1 (Male or Female)Compa - salary divided by midpoint1123.61.02423411001914.81FA1266.91.1745752952204.50ME1341.61.0414030100214.70FC1421.50.93623329012161FA1524.41.059233280814.91FA16390.975404490405.70MC1768.81.2075727553131FE1834.91.1263131801115.60FB1923.21.008233285104.61MA20361.1603144701614.80FB2175.31.1246743951306.31MF2256.71.182484865613.81FD2322.60.984233665613.30FA2451.51.072483075913.80FD2525.51.1092341704040MA2622.90.994232295216.20FA2743.51.088403580703.91MC2874.41.111674495914.40FF2973.51.097675295505.40MF3045.70.9524845901804.30MD3123.71.031232960413.91FA3226.90.867312595405.60MB3355.10.967573590905.51ME34280.904312680204.91MB3521.90.953232390415.30FA3623.71.032232775314.30FA3723.21.010232295216.20FA3857.61.0105745951104.50ME3934.31.108312790615.50FB4024.41.062232490206.30MA4140.51.012402580504.30MC4223.31.0122332100815.71FA4377.21.1526742952015.50FF4456.90.9995745901605.21ME4557.71.202483695815.21FD4665.41.1485739752003.91ME4756.80.997573795505.51ME4859.71.0485734901115.31FE4962.41.0955741952106.60ME5056.50.9925738801204.60ME
Week 1Week 1.Measurement and Description - chapters 1 and 2The goal this week is to gain an understanding of our data set - what kind of data we are looking at, some descriptive measurse, and a look at how the data is distributed (shape).1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, .
R&R Analysis Using SEDana
This document discusses standard data analysis tools for evaluating measurement repeatability and reproducibility (R&R) using statistical methods. It covers topics such as distribution and variation, measurement errors, process control and capability, basic statistics, and methods for evaluating repeatability, reproducibility, and gauge R&R including the range method, average/range method, and ANOVA method. Examples of tables and charts used in R&R studies are also presented.
Basics of SPSS and how to use it first time
SPSS is a popular statistical software package that allows users to perform complex data analysis with simple instructions. It displays data in a data editor with two views: the data view shows raw data while the variable view describes each variable. SPSS can analyze nominal, ordinal, interval, and ratio scales of measurement. Basic analyses introduced include frequencies, descriptives to calculate averages and standard deviations, linear regression, t-tests, and graphs like bar charts, pie charts, line charts and histograms. Users select analyses from menus, define variables and groups, and view automated output of statistical results.
Homework Computing a t-test using AGE as the independent var
Homework
Computing a t-test using AGE as the independent variable and CSSE (Current statistics self-efficacy) as the dependent measure. We know from research that age 37 is a good cutpoint for our AGE variable. Use tables below to answer the questions.
Note:
¨ Levenes equality of variance tests: a significance value larger than 0.05 is good.
This means the variances for groups DO NOT differ from each other.
What is Levenes equality of variance tests significance?
What is the value of
t
when variances are equal? What about unequal?
Is either value of
t
significant?
T-Test
Group Statistics
Age
N
Mean
Std. Deviation
Std. Error Mean
Current Stats Self-Efficacy
>= 37
45
6.44
2.833
.422
< 37
68
6.47
2.391
.290
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
Current Stats Self-Efficacy
Equal variances assumed
1.782
.185
-.053
111
.958
-.026
.495
-1.007
.954
Equal variances not assumed
-.051
83.123
.959
-.026
.512
-1.045
.993
Homework
Computing a t-test using AGE as the independent variable and CSSE (Current statistics self-efficacy) as the dependent measure. We know from research that age 37 is a good cutpoint for our AGE variable. Use tables below to answer the questions.
Note:
¨ Levenes equality of variance tests: a significance value larger than 0.05 is good.
This means the variances for groups DO NOT differ from each other.
What is Levenes equality of variance tests significance?
What is the value of
t
when variances are equal? What about unequal?
Is either value of
t
significant?
T-Test
Group Statistics
Age
N
Mean
Std. Deviation
Std. Error Mean
Current Stats Self-Efficacy
>= 37
45
6.44
2.833
.422
< 37
68
6.47
2.391
.290
Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F
Sig.
t
df
Sig. (2-tailed)
Mean Difference
Std. Error Difference
95% Confidence Interval of the Difference
Lower
Upper
Current Stats Self-Efficacy
Equal variances assumed
1.782
.185
-.053
111
.958
-.026
.495
-1.007
.954
Equal variances not assumed
-.051
83.123
.959
-.026
.512
-1.045
.993
...
1. Outline the differences between Hoarding power and Encouraging..docx
1. Outline the differences between Hoarding power and Encouraging.
2. Explain about the power of Congruency in Leadership.
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrCopy Employee Data set to this page.822.10.962233290915.81FAThe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 1522.60.984233280814.91FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.3522.60.984232390415.30FA37230.999232295216.20FAThe column labels in the table mean:1023.11.003233080714.71FAID – Employee sample number Salary – Salary in thousands 2323.11.004233665613.30FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)1123.31.01223411001914.81FASERvice – Years of serviceGender: 0 = male, 1 = female 2623.51.020232295216.20FAMidpoint – salary grade midpoint Raise – percent of last raise3123.61.028232960413.91FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)3623.61.026232775314.30FAGender1 (Male or Female)Compa-ratio - salary divided by midpoint4023.81.034232490206.30MA14241.04523329012161FA4224.21.0512332100815.71FA1924.31.055233285104.61MA25251.0872341704040MA3226.50.855312595405.60MB227.70.895315280703.90MB3428.60.923312680204.91MB3933.91.094312790615.50FB2034.11.1013144701614.80FB1834.51.1133131801115.60FB335.11.132313075513.61FB1341.11.0274030100214.70FC741.31.0324032100815.71FC1642.21.054404490405.70MC4145.81.144402580504.30MC2746.91.172403580703.91MC548.21.0044836901605.71MD3049.31.0274845901804.30MD2456.31.173483075913.80FD4556.91.185483695815.21FD4757.21.003573795505.51ME3357.51.008573590905.51ME4581.01857421001605.51ME3858.81.0325745951104.50ME5059.61.0465738801204.60ME4660.21.0575739752003.91ME2260.31.257484865613.81FD161.61.081573485805.70ME4461.81.0855745901605.21ME49631.1055741952106.60ME1763.71.1185727553131FE1264.71.1355752952204.50ME4869.51.2195734901115.31FE973.91.103674910010041MF4375.61.1286742952015.50FF2976.31.139675295505.40MF2177.21.1526743951306.31MF678.11.1656736701204.51MF2878.31.169674495914.40FF
Week 2This assignment covers the material presented in weeks 1 and 2.Six QuestionsBefore starting this assignment, make sure the the assignment data from the Employee Salary Data Set file is copied over to this Assignment file.You can do this either by a copy and paste of all the columns or by opening the data file, right clicking on the Data tab, selecting Move or Copy, and copying the entire sheet to this file(Weekly Assignment Sheet or whatever you are calling your master assignment file).It is highly recommended that you copy the data columns (with labels) and paste them to the right so that whatever you do will not disrupt the original data values and relationships.To Ensure full credit for each question, you need to show how you got your results. For example, Question 1 asks for several data values. If you obtain them using descript ...
MeansFeeling Thermometer Clinton V16NotesOutput .docx
Means
Feeling Thermometer Clinton V16
Notes
Output Created
25-APR-2018 14:30:41
Comments
Input
Data
/Users/xxxx/Desktop/ANES2016REVISED (3).sav
Active Dataset
DataSet1
Filter
<none>
Weight
Post-election weight -full sample
Split File
<none>
N of Rows in Working Data File
3649
Missing Value Handling
Definition of Missing
For each dependent variable in a table, user-defined missing values for the dependent and all grouping variables are treated as missing.
Cases Used
Cases used for each table have no missing values in any independent variable, and not all dependent variables have missing values.
Syntax
MEANS TABLES=V16 BY V79_30D
/CELLS=COUNT MEDIAN MEAN RANGE STDDEV.
Resources
Processor Time
00:00:00.02
Elapsed Time
00:00:00.00
[DataSet1] /Users/xxxx /Desktop/ANES2016REVISED (3).sav
Case Processing Summary
Cases
Included
Excluded
Total
N
Percent
N
Percent
N
Percent
PRE: Feeling Thermometer: Democratic Presidential cand * ILLEGAL IMMIGRANTS
3015
82.6%
634
17.4%
3649
100.0%
Report
PRE: Feeling Thermometer: Democratic Presidential cand
ILLEGAL IMMIGRANTS
N
Median
Mean
Range
Std. Deviation
Negative (0-49)
1891
15.00
30.09
100
31.556
Positive (51-100)
1124
66.00
57.69
100
31.319
Total
3015
40.00
40.38
100
34.177
MEANS TABLES=V17 BY V79_30D
/CELLS=COUNT MEDIAN MEAN RANGE STDDEV.
Means
Feeling Thermometer Trump V17
Notes
Output Created
25-APR-2018 14:31:04
Comments
Input
Data
/Users/xxxx/Desktop/ANES2016REVISED (3).sav
Active Dataset
DataSet1
Filter
<none>
Weight
Post-election weight -full sample
Split File
<none>
N of Rows in Working Data File
3649
Missing Value Handling
Definition of Missing
For each dependent variable in a table, user-defined missing values for the dependent and all grouping variables are treated as missing.
Cases Used
Cases used for each table have no missing values in any independent variable, and not all dependent variables have missing values.
Syntax
MEANS TABLES=V17 BY V79_30D
/CELLS=COUNT MEDIAN MEAN RANGE STDDEV.
Resources
Processor Time
00:00:00.01
Elapsed Time
00:00:00.00
Case Processing Summary
Cases
Included
Excluded
Total
N
Percent
N
Percent
N
Percent
PRE: Feeling Thermometer: Republican Presidential cand * ILLEGAL IMMIGRANTS
3013
82.6%
636
17.4%
3649
100.0%
Report
PRE: Feeling Thermometer: Republican Presidential cand
ILLEGAL IMMIGRANTS
N
Median
Mean
Range
Std. Deviation
Negative (0-49)
1885
60.00
48.37
100
34.458
Positive (51-100)
1127
1.00
20.33
100
29.585
Total
3013
31.00
37.88
100
35.419
...
week 1 ScoreWeek 1.Measurement and Description - chapters 1 and 2.docx
week 1 Score:Week 1.Measurement and Description - chapters 1 and 2<1 point>1Measurement issues. Data, even numerically coded variables, can be one of 4 levels - nominal, ordinal, interval, or ratio. It is important to identify which level a variable is, asthis impact the kind of analysis we can do with the data. For example, descriptive statistics such as means can only be done on interval or ratio level data.Please list under each label, the variables in our data set that belong in each group.NominalOrdinalIntervalRatiob.For each variable that you did not call ratio, why did you make that decision?<1 point>2The first step in analyzing data sets is to find some summary descriptive statistics for key variables.For salary, compa, age, performance rating, and service; find the mean, standard deviation, and range for 3 groups: overall sample, Females, and Males.You can use either the Data Analysis Descriptive Statistics tool or the Fx =average and =stdev functions. (the range must be found using the difference between the =max and =min functions with Fx) functions.Note: Place data to the right, if you use Descriptive statistics, place that to the right as well.SalaryCompaAgePerf. Rat.ServiceOverallMeanStandard DeviationRangeFemaleMeanStandard DeviationRangeMaleMeanStandard DeviationRange<1 point>3What is the probability for a:Probabilitya. Randomly selected person being a male in grade E?b. Randomly selected male being in grade E? Note part b is the same as given a male, what is probabilty of being in grade E?c. Why are the results different?<1 point>4For each group (overall, females, and males) find:OverallFemaleMalea.The value that cuts off the top 1/3 salary in each group.Hint: can use these Fx functionsb.The z score for each value:Excel's standize functionc.The normal curve probability of exceeding this score:1-normsdist functiond.What is the empirical probability of being at or exceeding this salary value?e.The value that cuts off the top 1/3 compa in each group.f.The z score for each value:g.The normal curve probability of exceeding this score:h.What is the empirical probability of being at or exceeding this compa value?i.How do you interpret the relationship between the data sets? What do they mean about our equal pay for equal work question?<2 points>5. What conclusions can you make about the issue of male and female pay equality? Are all of the results consistent? What is the difference between the sal and compa measures of pay?Conclusions from looking at salary results:Conclusions from looking at compa results:Do both salary measures show the same results?Can we make any conclusions about equal pay for equal work yet?
dataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1Gr164.41.13573485805.70METhe ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)? 227.40.884315280703.90MBNote: to simplfy the a.
Chapter36a
This document discusses inferential statistics and various statistical tests used to analyze differences between groups. It describes measures of difference such as the t-test, analysis of variance (ANOVA), chi-square test, Mann-Whitney test, and Kruskal-Wallis test. It also covers regression analysis techniques like simple and multiple linear regression. Key steps are outlined for conducting t-tests, ANOVA, and interpreting their results from SPSS output. Degrees of freedom and their role in statistical tests are also explained.
A05 Continuous One Variable Stat Tests
This document provides guidance on using statistical tests to determine which process inputs (X's) are critical and impact the process output (Y). It outlines common statistical tests for continuous and discrete data, including tests for normality, 1-sample t-tests, and 1-sample sign tests. Steps are provided to gather input data, apply appropriate hypothesis tests to verify which X's are critical, and list the critical X's.
A05 Continuous One Variable Stat Tests
This document provides guidance on using statistical tests to determine which process inputs (X's) are critical and influence outcomes (Y's). It outlines common statistical tests for continuous and discrete data, including tests for normality, one-sample t-tests to compare a mean to a target, and one-sample sign tests to compare a median when data is not normal. Examples are provided to illustrate how to use Minitab to conduct these tests and interpret the results.
SheffieldR July Meeting - Multiple Imputation with Chained Equations (MICE) p...
Presentation given by Rich Jacques, talking thorough the use of the package "mice", which eases the pain of doing multiple imputations for analysis with incomplete datasets
Dr.Dinesh-BIOSTAT-Tests-of-significance-1-min.pdf
This document discusses various statistical tests used to analyze dental research data, including parametric and non-parametric tests. It provides information on tests of significance such as the t-test, Z-test, analysis of variance (ANOVA), and non-parametric equivalents. Key points covered include the differences between parametric and non-parametric tests, assumptions and applications of the t-test, Z-test, ANOVA, and non-parametric alternatives like the Mann-Whitney U test and Kruskal-Wallis test. Examples are provided to illustrate how to perform and interpret common statistical analyses used in dental research.
T_test_of_dependent_means (2).ppt
This document provides information about performing a paired t-test, including the steps and an example. A paired t-test is used to compare two related samples, such as pre-test and post-test scores from the same group. The steps are: formulate hypotheses, determine if the test is one-tailed or two-tailed, calculate the degrees of freedom, select the t-test, compute the test statistic, state the decision rule based on critical value, and make a conclusion. An example tests if a new diet significantly reduces weight by comparing weights before and after for 7 women.
Similar to Harold analysis independent t test part 2 gender jobper motiv (20)
This spss analysis file it contains basis analysis tips
This spss analysis file it contains basis analysis tips
Review the SPSS output file which reports the results of the indepen.docx
Review the SPSS output file which reports the results of the indepen.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
Homework Computing a t-test using AGE as the independent var
Homework Computing a t-test using AGE as the independent var
1. Outline the differences between Hoarding power and Encouraging..docx
1. Outline the differences between Hoarding power and Encouraging..docx
MeansFeeling Thermometer Clinton V16NotesOutput .docx
MeansFeeling Thermometer Clinton V16NotesOutput .docx
week 1 ScoreWeek 1.Measurement and Description - chapters 1 and 2.docx
week 1 ScoreWeek 1.Measurement and Description - chapters 1 and 2.docx
SheffieldR July Meeting - Multiple Imputation with Chained Equations (MICE) p...
SheffieldR July Meeting - Multiple Imputation with Chained Equations (MICE) p...
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Computational Engineering IITH Presentation
This Presentation will give you a brief idea about what Computational Engineering at IIT Hyderabad has to offer.
Design and optimization of ion propulsion drone
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
Null Bangalore | Pentesters Approach to AWS IAM
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
AI assisted telemedicine KIOSK for Rural India.pptx
It gives the overall description of SIH problem statement " AI assisted telemedicine KIOSK for Rural India".
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
An improved modulation technique suitable for a three level flying capacitor ...
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building
Comparative analysis between traditional aquaponics and reconstructed aquapon...
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
cnn.pptx Convolutional neural network used for image classication
Convolutional Neural Network used for image classification
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
Slides for the 4th Presentation on LLM Fine-Tuning with QLoRA Presented by Anant, featuring DataStax Astra
Data Driven Maintenance | UReason Webinar
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
AI assisted telemedicine KIOSK for Rural India.pptx
AI assisted telemedicine KIOSK for Rural India.pptx
Embedded machine learning-based road conditions and driving behavior monitoring
Embedded machine learning-based road conditions and driving behavior monitoring
An improved modulation technique suitable for a three level flying capacitor ...
An improved modulation technique suitable for a three level flying capacitor ...
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
2008 BUILDING CONSTRUCTION Illustrated - Ching Chapter 02 The Building.pdf
Comparative analysis between traditional aquaponics and reconstructed aquapon...
Comparative analysis between traditional aquaponics and reconstructed aquapon...
cnn.pptx Convolutional neural network used for image classication
cnn.pptx Convolutional neural network used for image classication
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
LLM Fine Tuning with QLoRA Cassandra Lunch 4, presented by Anant
Harold analysis independent t test part 2 gender jobper motiv
- 1. T-Test Notes Output Created 21-SEP-2021 08:56:47 Comments Input Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 181 Missing Value Handling Definition of Missing User defined missing values are treated as missing. Cases Used Statistics for each analysis are based on the cases with no missing or out-of-range data for any variable in the analysis. Syntax T-TEST GROUPS = GENDER(1 2) /MISSING = ANALYSIS /VARIABLES = MOTIV JOBPERF /CRITERIA = CI(.95) . Resources Elapsed Time 0:00:00.09 [DataSet2] Group Statistics GENDER N Mean Std. Deviation Std. Error Mean MOTIV 1 141 3.8779 .34345 .02892 2 39 3.9287 .28569 .04575 JOBPERF 1 141 4.2379 .40973 .03451 2 39 4.1713 .33261 .05326 Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper MOTIV Equal variances assumed 2.628 .107 -.847 178 .398 -.05085 .06006 -.16937 .06768 Equal variances not assumed -.939 71.359 .351 -.05085 .05412 -.15875 .05706
- 2. JOBPERF Equal variances assumed 4.137 .043 .934 178 .352 .06666 .07138 -.07420 .20752 Equal variances not assumed 1.050 73.100 .297 .06666 .06346 -.05981 .19313