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help The accompanying data provides the results of a survey of 27 employees in a tax division of a Fortune 100 company. a. Test the null hypothesis that the average number of years of service is the same for males and females. Assume that the population variances are unequal. b. Test the null hypothesis that the average years of undergraduate study is the same for males and females Assume that the populion variances are Click the icon to view the survey data. a. Is there sufficient evidence at the 0.05 level of significance that the average number of years of service is the same for males and females? Determine the null hypothesis, H 0 , and the alternative hypothesis, H 1 . Let females be population 1 and males be population 2 H 0 1 2 = 0 H 1 1 2 = 0 (Type integers or decimals. Do not round.) Compute the test statistic. (Round to two decimal places as needed.) Survey data.
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can i get a quote. i got one from barzzy but she never replied.docx
can i get a quote. i got one from barzzy but she never replied
At this point we know the following about male and female salaries.
a.
Male and female overall average salaries are not equal in the population.
b.
Male and female overall average compas are equal in the population, but males are a bit more spread out.
c.
The male and female salary range are almost the same, as is their age and service.
d.
Average performance ratings per gender are equal.
Let's look at some other factors that might influence pay - education(degree) and performance ratings.
1
Last week, we found that average performance ratings do not differ between males and females in the population.
Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?
(Assume variances are equal across the grades for this ANOVA.)
You can use these columns to place grade Perf Ratings if desired.
A
B
C
D
E
F
Null Hypothesis:
Alt. Hypothesis:
Place
B17 in Outcome range box.
Interpretation:
What is the p-value:
Is P-value < 0.05?
Do we REJ or Not reject the null?
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
What does that decision mean in terms of our equal pay question:
2
While it appears that average salaries per each grade differ, we need to test this assumption.
Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.)
Use the input table to the right to list salaries under each grade level.
Null Hypothesis:
If desired, place salaries per grade in these columns
Alt. Hypothesis:
A
B
C
D
E
F
Place
B55 in Outcome range box.
What is the p-value:
Is P-value < 0.05?
Do you reject or not reject the null hypothesis:
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
Interpretation:
3
The table and analysis below demonstrate a 2-way ANOVA with replication.
Please interpret the results.
BA
MA
Ho: Average compas by gender are equal
Male
1.017
1.157
Ha: Ave.
Employment trends sas project
Economies and societies become more interdependent, the need to enhance our understanding of the world of work becomes increasingly important. Timely and focused information on the world's labor markets is essential. So Developed a project on Employment Trends
Chi-square tests are great to show if distributions differ or i.docx
Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you?
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe 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)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement 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, performance rating, and service; find the mean, standard deviation, and range for 3 groups: ...
How does Age Influence IncomeECO490Danni SongNanc.docx
How does Age Influence Income?
ECO490
Danni Song
Nancy Haskell () - How Does Age Influence Income?
This is a more grammatically correct title.
You should remove my name and remove the last line, which merely repeats the title.
Introduction
The aim of this study is to find out if an individual age has any influence on income
How do age influence income?
As workers gains more experience there should be a premium to compesate their skills
Knowing the relationship between the age and income is important in determining salaries and also individuals can use it to project their future earnings
In the current generation the young population has outnumbered old people
Nancy Haskell () - You can remove the 2nd bullet point because it is already stated on the previous slide.
Literature review
According to Cloninger (2016), age is a natural cause of income disparity and it can’t be easily affected by government policies.
Bares (2016), increase in age leads to increase in income up to the age of 55 years and after this age the level of income begin to decline.
Organizations reported that older workers are more expensive than younger ones.
Parramore (2016), Human capital theory argues that experience can increase income
Knowledge of how age affects income is useful in explaining income disparity among workers
The past studies have not clearly explained the type of relationship between age and income.
Age-earning relationship is important in explaining income levels in employment life-cycle
Model
Regression equation is represented below
𝑤= 𝛽0 +𝛽1w+𝛽2b+𝛽3as+𝛽4a1539+ 𝛽4a4069+𝜀
where; income =w, age=𝛽0, black=𝛽1w,Asian =𝛽2b, age15_39= 𝛽3as, age40_69=𝛽4a4069, and 𝜀 =error component.
Income is the independent variable, age is a dependent variable.
Control variables are white, black, Asian, age15_39, age40_69
The prediction is that age affects income
Nancy Haskell () - Try to make sure your text does not run into the purple area, where we cannot read it.
You can remove the first bullet point.
The regresison equation needs the numbers after the betas to be subscripts. Also, the last betas should be a 5 (you have 4 listed twice).
Income is your dependent variable. Age15-39 and Age40-69 are your key independent variables. Black, White, and Asian are your control variables.
You should remove the part that says age = B0.... that is wrong.
You want to predict how age affect income in the last bullet. Meaning, what is the sign on the coefficient estimates?
Data
The data was collected from US Bureau of Labor Statistics (2017).
The data is a time series for a period of 15 years.
Sample size is made up of 350 observations
Data collected consist earnings from employees of different age.
The data contain all the required variables
Data
Summary statistics of the studyData MeanStandard dev.Min25thpercentileMedian75thpercentileMaxIncome 52847.168646.4833679646693508365825274551Age1539.34.02.291.328.336.345.396Age4069.37.02.27
AB TESTING
Project presentation about AB Testing that we learned within the scope of Data Scientist Training Program organized by Veri Bilimi Okulu
Soon-to-beDr. Sharon ReedResults ppt Slides for your Conside.docx
Soon-to-be
Dr. Sharon Reed
Results ppt Slides for your Consideration
Research Questions
Research Question 1 (RQ1)
Is there a difference in aims or goals emphasis, as a leadership behavior, between non-profit human services managers with and without human services degrees?
Research Question 2 (RQ2)
Is there a difference in personal backing, as a leadership behavior, between non-profit human services managers with and without human services degrees?
Research Question 3 (RQ3)
Is there a difference in work easing or facilitation, as a leadership behavior, between non-profit human services managers with and without human services degrees?
This study was guided by three research questions. They correspond to the three leadership behaviors of interest (aims or goals emphasis; personal backing; and work easing or facilitation). Associated hypotheses are listed in sections that present the results for the research questions.
2
Data Handling
Percentages rounded to whole numbers
Data were analyzed with SPSS v 25
Statistical significance alpha = .050
Reliability statistics w Cronbach’s alpha
Aims emphasis SS
Personal Backing SS
Work Easing SS
Percentages were rounded off to whole numbers and may not add up to precisely 100%. Data were analyzed with SPSS v 25, which is dedicated statistical software. Statistical significance was set at alpha = .050.
Reliability
Reliability statistics (i.e., internal consistency) were run on conceptually-similar survey items with Cronbach’s alpha for the three leadership behaviors of interest (aims or goals emphasis; personal backing; and work easing or facilitation). Cronbach’s alpha statistics range in value from 0 to 1. The closer Cronbach’s alpha is to 1, the greater the reliability of the database. Indices of .70 or higher reflect an adequately reliable database (Gliner & Morgan, 2000).
Summated Scale Scores
A summated scale was generated for each of the three leadership behaviors of interest (aims or goals emphasis; personal backing; and work easing or facilitation). A summated scale is a single empirical measure that represents multiple aspects of a construct in one variable (Hair et al., 2010). Deriving a single measure from several related aspects decreases the measurement error in the original data points, which increases data reliability and validity as well as parsimony in the overall number of variables (Hair et al., 2010). Because each summated score was a mean, its possible values ranged 1 – 7 (1 = strongly disagree, 7 = strongly agree) like the Likert scale used to measure responses to survey items.
Aims emphasis. Two survey items measured the leadership dimension of “aims emphasis.” An example of a survey item that measured aims emphasis is: “My manager makes sure subordinates have clear goals to achieve.” Internal consistency was high, Cronbach’s alpha = .74. Because of high internal consistency, a summated scale (SS) was generated, using the mean response of the related items. It is herea.
Hypothesis TestingIn doing research, one of the most common acti
Hypothesis Testing
In doing research, one of the most common activities is testing hypotheses. The Afrobarometer data set below is a survey of African citizens’ attitudes on democracy, governance, the economy, and other related topics (www.afrobarometer.org). Using this data set, you might want to examine hypotheses related to whether rural and urban citizens differ, on average, in how much they trust the government. The tables below present results from an independent samples t-test to examine these hypotheses using a random sample of 44 participants from the complete data set. Each respondent’s score is a value between 0 and 15 with a higher score indicating greater trust. You can see that the mean for the urban group is 7.00 ( SD = 4.17) and the mean for the rural group is 7.74 ( SD = 4.38). The observed value of the t-statistic is -.564 and the p-value equals 0.576 (see the column labeled “Sig. (2-tailed)”).
African Citizens' Attitudes on Democracy
The tables below present results from an independent samples t-test to examine these hypotheses using a random sample of 44 participants from the complete data set. Each respondent’s score is a value between 0 and 15 with a higher score indicating greater trust. You can see that the mean for the urban group is 7.00 ( SD = 4.17) and the mean for the rural group is 7.74 ( SD = 4.38). The observed value of the t-statistic is -.564 and the p-value equals 0.576 (see the column labeled “Sig. (2-tailed)”).
t
df
Sig.
(2-tailed)
Mean Difference
Std. Error Difference
Trust in Government Index
(higher scores = more trust)
-.564
41
.576
-.73913
1.30978
Group Statistics
Urban or Rural Primary
Sampling Unit
N
Mean
Std. Deviation
Std. Error Mean
Trust in Government Index
(higher scores = more trust)
Urban
20
7.000
4.16754
.93189
Rural
30
7.7391
4.38196
.91370
The p-value is the probability of obtaining a value more extreme than .564 (less than -.564 or greater than +.564) if you were to repeat the test with a new sample of data and if the null hypothesis is true. You will see in this Skill Builder that the p-valuecan easily be used to make statistical decisions in hypothesis testing. However, while the p-valueis important in determining statistical significance, it does not tell the whole story.
Steps of Hypothesis Testing
To interpret p-values, let's review the key steps in hypothesis testing. Use the < and > icons to navigate between the steps.
Step 1
State the null and alternative hypotheses
Recall that hypotheses are statements about population parameters. For the Trust in Government example from the Afrobarometer data set, the null (HO) and alternative hypotheses (HA) is seen in the above image.
The Greek letter, µ, indicates a population mean, and the subscripts indicate levels of the independent variable (“urban” and “rural”). Here the null is saying that the mean for the urban population on the Trust In Government variable is the same as the mean for the rural population. The alternative hypothe ...
Recommended
can i get a quote. i got one from barzzy but she never replied.docx
can i get a quote. i got one from barzzy but she never replied
At this point we know the following about male and female salaries.
a.
Male and female overall average salaries are not equal in the population.
b.
Male and female overall average compas are equal in the population, but males are a bit more spread out.
c.
The male and female salary range are almost the same, as is their age and service.
d.
Average performance ratings per gender are equal.
Let's look at some other factors that might influence pay - education(degree) and performance ratings.
1
Last week, we found that average performance ratings do not differ between males and females in the population.
Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?
(Assume variances are equal across the grades for this ANOVA.)
You can use these columns to place grade Perf Ratings if desired.
A
B
C
D
E
F
Null Hypothesis:
Alt. Hypothesis:
Place
B17 in Outcome range box.
Interpretation:
What is the p-value:
Is P-value < 0.05?
Do we REJ or Not reject the null?
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
What does that decision mean in terms of our equal pay question:
2
While it appears that average salaries per each grade differ, we need to test this assumption.
Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.)
Use the input table to the right to list salaries under each grade level.
Null Hypothesis:
If desired, place salaries per grade in these columns
Alt. Hypothesis:
A
B
C
D
E
F
Place
B55 in Outcome range box.
What is the p-value:
Is P-value < 0.05?
Do you reject or not reject the null hypothesis:
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
Interpretation:
3
The table and analysis below demonstrate a 2-way ANOVA with replication.
Please interpret the results.
BA
MA
Ho: Average compas by gender are equal
Male
1.017
1.157
Ha: Ave.
Employment trends sas project
Economies and societies become more interdependent, the need to enhance our understanding of the world of work becomes increasingly important. Timely and focused information on the world's labor markets is essential. So Developed a project on Employment Trends
Chi-square tests are great to show if distributions differ or i.docx
Chi-square tests are great to show if distributions differ or if two variables interact in producing outcomes. What are some examples of variables that you might want to check using the chi-square tests? What would these results tell you?
DataSee comments at the right of the data set.IDSalaryCompaMidpointAgePerformance RatingServiceGenderRaiseDegreeGender1Grade8231.000233290915.80FAThe 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)? 10220.956233080714.70FANote: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.11231.00023411001914.80FA14241.04323329012160FAThe column labels in the table mean:15241.043233280814.90FAID – Employee sample number Salary – Salary in thousands 23231.000233665613.31FAAge – Age in yearsPerformance Rating – Appraisal rating (Employee evaluation score)26241.043232295216.21FAService – Years of service (rounded)Gender: 0 = male, 1 = female 31241.043232960413.90FAMidpoint – salary grade midpoint Raise – percent of last raise35241.043232390415.31FAGrade – job/pay gradeDegree (0= BS\BA 1 = MS)36231.000232775314.31FAGender1 (Male or Female)Compa - salary divided by midpoint37220.956232295216.21FA42241.0432332100815.70FA3341.096313075513.60FB18361.1613131801115.61FB20341.0963144701614.81FB39351.129312790615.51FB7411.0254032100815.70FC13421.0504030100214.71FC22571.187484865613.80FD24501.041483075913.81FD45551.145483695815.20FD17691.2105727553130FE48651.1405734901115.31FE28751.119674495914.41FF43771.1496742952015.51FF19241.043233285104.61MA25241.0432341704040MA40251.086232490206.30MA2270.870315280703.90MB32280.903312595405.60MB34280.903312680204.91MB16471.175404490405.70MC27401.000403580703.91MC41431.075402580504.30MC5470.9794836901605.71MD30491.0204845901804.30MD1581.017573485805.70ME4661.15757421001605.51ME12601.0525752952204.50ME33641.122573590905.51ME38560.9825745951104.50ME44601.0525745901605.21ME46651.1405739752003.91ME47621.087573795505.51ME49601.0525741952106.60ME50661.1575738801204.60ME6761.1346736701204.51MF9771.149674910010041MF21761.1346743951306.31MF29721.074675295505.40MF
Week 1Week 1.Measurement and Description - chapters 1 and 21Measurement 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, performance rating, and service; find the mean, standard deviation, and range for 3 groups: ...
How does Age Influence IncomeECO490Danni SongNanc.docx
How does Age Influence Income?
ECO490
Danni Song
Nancy Haskell () - How Does Age Influence Income?
This is a more grammatically correct title.
You should remove my name and remove the last line, which merely repeats the title.
Introduction
The aim of this study is to find out if an individual age has any influence on income
How do age influence income?
As workers gains more experience there should be a premium to compesate their skills
Knowing the relationship between the age and income is important in determining salaries and also individuals can use it to project their future earnings
In the current generation the young population has outnumbered old people
Nancy Haskell () - You can remove the 2nd bullet point because it is already stated on the previous slide.
Literature review
According to Cloninger (2016), age is a natural cause of income disparity and it can’t be easily affected by government policies.
Bares (2016), increase in age leads to increase in income up to the age of 55 years and after this age the level of income begin to decline.
Organizations reported that older workers are more expensive than younger ones.
Parramore (2016), Human capital theory argues that experience can increase income
Knowledge of how age affects income is useful in explaining income disparity among workers
The past studies have not clearly explained the type of relationship between age and income.
Age-earning relationship is important in explaining income levels in employment life-cycle
Model
Regression equation is represented below
𝑤= 𝛽0 +𝛽1w+𝛽2b+𝛽3as+𝛽4a1539+ 𝛽4a4069+𝜀
where; income =w, age=𝛽0, black=𝛽1w,Asian =𝛽2b, age15_39= 𝛽3as, age40_69=𝛽4a4069, and 𝜀 =error component.
Income is the independent variable, age is a dependent variable.
Control variables are white, black, Asian, age15_39, age40_69
The prediction is that age affects income
Nancy Haskell () - Try to make sure your text does not run into the purple area, where we cannot read it.
You can remove the first bullet point.
The regresison equation needs the numbers after the betas to be subscripts. Also, the last betas should be a 5 (you have 4 listed twice).
Income is your dependent variable. Age15-39 and Age40-69 are your key independent variables. Black, White, and Asian are your control variables.
You should remove the part that says age = B0.... that is wrong.
You want to predict how age affect income in the last bullet. Meaning, what is the sign on the coefficient estimates?
Data
The data was collected from US Bureau of Labor Statistics (2017).
The data is a time series for a period of 15 years.
Sample size is made up of 350 observations
Data collected consist earnings from employees of different age.
The data contain all the required variables
Data
Summary statistics of the studyData MeanStandard dev.Min25thpercentileMedian75thpercentileMaxIncome 52847.168646.4833679646693508365825274551Age1539.34.02.291.328.336.345.396Age4069.37.02.27
AB TESTING
Project presentation about AB Testing that we learned within the scope of Data Scientist Training Program organized by Veri Bilimi Okulu
Soon-to-beDr. Sharon ReedResults ppt Slides for your Conside.docx
Soon-to-be
Dr. Sharon Reed
Results ppt Slides for your Consideration
Research Questions
Research Question 1 (RQ1)
Is there a difference in aims or goals emphasis, as a leadership behavior, between non-profit human services managers with and without human services degrees?
Research Question 2 (RQ2)
Is there a difference in personal backing, as a leadership behavior, between non-profit human services managers with and without human services degrees?
Research Question 3 (RQ3)
Is there a difference in work easing or facilitation, as a leadership behavior, between non-profit human services managers with and without human services degrees?
This study was guided by three research questions. They correspond to the three leadership behaviors of interest (aims or goals emphasis; personal backing; and work easing or facilitation). Associated hypotheses are listed in sections that present the results for the research questions.
2
Data Handling
Percentages rounded to whole numbers
Data were analyzed with SPSS v 25
Statistical significance alpha = .050
Reliability statistics w Cronbach’s alpha
Aims emphasis SS
Personal Backing SS
Work Easing SS
Percentages were rounded off to whole numbers and may not add up to precisely 100%. Data were analyzed with SPSS v 25, which is dedicated statistical software. Statistical significance was set at alpha = .050.
Reliability
Reliability statistics (i.e., internal consistency) were run on conceptually-similar survey items with Cronbach’s alpha for the three leadership behaviors of interest (aims or goals emphasis; personal backing; and work easing or facilitation). Cronbach’s alpha statistics range in value from 0 to 1. The closer Cronbach’s alpha is to 1, the greater the reliability of the database. Indices of .70 or higher reflect an adequately reliable database (Gliner & Morgan, 2000).
Summated Scale Scores
A summated scale was generated for each of the three leadership behaviors of interest (aims or goals emphasis; personal backing; and work easing or facilitation). A summated scale is a single empirical measure that represents multiple aspects of a construct in one variable (Hair et al., 2010). Deriving a single measure from several related aspects decreases the measurement error in the original data points, which increases data reliability and validity as well as parsimony in the overall number of variables (Hair et al., 2010). Because each summated score was a mean, its possible values ranged 1 – 7 (1 = strongly disagree, 7 = strongly agree) like the Likert scale used to measure responses to survey items.
Aims emphasis. Two survey items measured the leadership dimension of “aims emphasis.” An example of a survey item that measured aims emphasis is: “My manager makes sure subordinates have clear goals to achieve.” Internal consistency was high, Cronbach’s alpha = .74. Because of high internal consistency, a summated scale (SS) was generated, using the mean response of the related items. It is herea.
Hypothesis TestingIn doing research, one of the most common acti
Hypothesis Testing
In doing research, one of the most common activities is testing hypotheses. The Afrobarometer data set below is a survey of African citizens’ attitudes on democracy, governance, the economy, and other related topics (www.afrobarometer.org). Using this data set, you might want to examine hypotheses related to whether rural and urban citizens differ, on average, in how much they trust the government. The tables below present results from an independent samples t-test to examine these hypotheses using a random sample of 44 participants from the complete data set. Each respondent’s score is a value between 0 and 15 with a higher score indicating greater trust. You can see that the mean for the urban group is 7.00 ( SD = 4.17) and the mean for the rural group is 7.74 ( SD = 4.38). The observed value of the t-statistic is -.564 and the p-value equals 0.576 (see the column labeled “Sig. (2-tailed)”).
African Citizens' Attitudes on Democracy
The tables below present results from an independent samples t-test to examine these hypotheses using a random sample of 44 participants from the complete data set. Each respondent’s score is a value between 0 and 15 with a higher score indicating greater trust. You can see that the mean for the urban group is 7.00 ( SD = 4.17) and the mean for the rural group is 7.74 ( SD = 4.38). The observed value of the t-statistic is -.564 and the p-value equals 0.576 (see the column labeled “Sig. (2-tailed)”).
t
df
Sig.
(2-tailed)
Mean Difference
Std. Error Difference
Trust in Government Index
(higher scores = more trust)
-.564
41
.576
-.73913
1.30978
Group Statistics
Urban or Rural Primary
Sampling Unit
N
Mean
Std. Deviation
Std. Error Mean
Trust in Government Index
(higher scores = more trust)
Urban
20
7.000
4.16754
.93189
Rural
30
7.7391
4.38196
.91370
The p-value is the probability of obtaining a value more extreme than .564 (less than -.564 or greater than +.564) if you were to repeat the test with a new sample of data and if the null hypothesis is true. You will see in this Skill Builder that the p-valuecan easily be used to make statistical decisions in hypothesis testing. However, while the p-valueis important in determining statistical significance, it does not tell the whole story.
Steps of Hypothesis Testing
To interpret p-values, let's review the key steps in hypothesis testing. Use the < and > icons to navigate between the steps.
Step 1
State the null and alternative hypotheses
Recall that hypotheses are statements about population parameters. For the Trust in Government example from the Afrobarometer data set, the null (HO) and alternative hypotheses (HA) is seen in the above image.
The Greek letter, µ, indicates a population mean, and the subscripts indicate levels of the independent variable (“urban” and “rural”). Here the null is saying that the mean for the urban population on the Trust In Government variable is the same as the mean for the rural population. The alternative hypothe ...
ScoreWeek 3ANOVA and Paired T-test.docx
Score:
Week 3
ANOVA and Paired T-test
At this point we know the following about male and female salaries.
a.
Male and female overall average salaries are not equal in the population.
b.
Male and female overall average compas are equal in the population, but males are a bit more spread out.
c.
The male and female salary range are almost the same, as is their age and service.
d.
Average performance ratings per gender are equal.
Let's look at some other factors that might influence pay - education(degree) and performance ratings.
<1 point>
1
Last week, we found that average performance ratings do not differ between males and females in the population.
Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?
(Assume variances are equal across the grades for this ANOVA.)
You can use these columns to place grade Perf Ratings if desired.
A
B
C
D
E
F
Null Hypothesis:
Alt. Hypothesis:
Place
B17 in Outcome range box.
Interpretation:
What is the p-value:
Is P-value < 0.05?
Do we REJ or Not reject the null?
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
What does that decision mean in terms of our equal pay question:
<1 point>
2
While it appears that average salaries per each grade differ, we need to test this assumption.
Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.)
Use the input table to the right to list salaries under each grade level.
Null Hypothesis:
If desired, place salaries per grade in these columns
Alt. Hypothesis:
A
B
C
D
E
F
.
Week 2Testing means - T-testsIn qu.docx
Week 2
Testing means - T-tests
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
Males
Females
Ho: Mean salary = 45
Ho: Mean salary = 45
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
Male
Ho
Female
Ho
Mean
52
45
Mean
38
45
Variance
316
0
Variance
334.667
0
Observations
25
25
Observations
25
25
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
df
24
df
24
t Stat
1.96890383
t Stat
-1.9132
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.03386
t Critical one-tail
1.71088208
t Critical one-tail
1.71088
P(T<=t) two-tail
0.0606157
P(T<=t) two-tail
0.06772
t Critical two-tail
2.06389856
t Critical two-tail
2.0639
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
Is P-value > 0.05?
Is P-value > 0.05?
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
2
Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.
(Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)
Ho:
Ha:
Test to use:
Place
B43 in Outcome range box.
P-value is:
Is P-value < 0.05?
Reject or do not reject Ho:
If
the null hypothesis was rejected, what is the effect size value:
Meaning of effect size measure:
Interpretation:
b.
Since the one and two sample t-test results provided different outcomes, which is the proper/correct apporach to comparing salary equality?
Why?
3
Based on our sample data set, can the male and female compas in the population be e.
Synthesizing the LiteratureChristopher.docx
Synthesizing the Literature
Christopher Walters
BTM7300: Scholarly Literature Review
Dr. Eva Philpot
December 6, 2015
Analyzing the increase in pay inequality over the last several years.
After reading J.C. Cunningham’s measuring wage inequality within and across US metropolitan areas, he presented how wages in the US have steadily declined for the unskilled worker and steadily increased for the skilled worker and or top 1 percent in US metropolitan area. The author also presented data showing how wage inequality is either negatively or positively affected within private and public sectors based on employee performance. The author made an interesting statement concerning US economic policies, and how they have impacted wage and pay inequality within the US from the 1970’s until today.
The author used one method or measure to acquire the data needed—the ratio of the 90th wage percentile to the 10th wage percentile, sometimes called the “90–10” ratio, inequality increased by 7 percent in the United States between 2003 and 2013. The study included data from the Occupational Employment Statistics program. Workers within 4 key areas of the wage distribution graph were sampled.
After reading D. J. Hammann, and T. Ren’s wage inequality and performance in nonprofit and for-profit organizations, the authors presented data to show how pay inequality is more prevalent in the for profit area than in the nonprofit area. The authors argue that tournament and fair wage/equity theories play a role in how each area impacts pay inequality. The authors used data from a study of workers in the nursing home industry. The data suggested that workers in nonprofit positions were more likely to be paid fairly or based on their skill level, as opposed to for profit positions and how similar positions are paid.
Finally, after reviewing wage inequality: A story of policy choices by L. Mishel, J. Schmitt, & H. Shierolz, the authors presented data to show how economic policies in the US has had a direct impact on wage and pay inequality on workers in the US. The authors focused on workers at four key points in the wage distribution: workers at the bottom (the 10th percentile, who earn more than 10 percent of workers, but less than 90 percent), the middle (the 50th percentile, or median, worker right in the middle of the wage distribution), the top (the 90th percentile, who earn more than 90 percent of workers, but less than the top 10 percent), and the very top (roughly, the top 1 percent). Based on wage and pay data, the authors discovered that wages for the top 1 percent increased by 156% between 1979 and 2007, and only 17% for the bottom 90 percent of wage earners during the same period.
Each study showed data that presented a steady decrease in non-skilled worker wages and an increase in skilled or executive wages. A difference would be the area of concentration on the study of the workers in the 90-10 study, and the study ...
Workforce diversity- A perspective of employees and students
Diversity can generally be defined as recognizing, understanding and accepting individual differences irrespective of their race, gender, age, class, ethnicity, physical ability, race, sexual orientation spiritual practice and so on. Diversity is growing in today’s organizations. Diversity can benefit the
organizations in enormous ways. Different values and perspectives shared by employees can make the
organization to compete in this turbulent environment. Globalization in this recent time has triggeredm more interaction amongst people from different cultures and backgrounds than before. People are now more open-minded in the marketplace worldwide with competition coming from almost everywhere in the continent. This study is an attempt to find out and compare the perception of people for workforce
diversity. In the present study the sample of 200 respondents was taken. Convenient sampling technique was used. The sample was drawn from Delhi. Interpretation of result was done on the basis of mean, standard deviation and ‘t’ test.
ScoreWeek 2Testing means - T-tests.docx
Score:
Week 2
Testing means - T-tests
Q3
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
Ho
Female
Male
Female
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
45
34
1.017
1.096
45
41
0.870
1.025
<1 point>
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
45
23
1.157
1.000
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
45
22
0.979
0.956
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
45
23
1.134
1.000
Males
Females
45
42
1.149
1.050
Ho: Mean salary = 45
Ho: Mean salary = 45
45
24
1.052
1.043
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
45
24
1.175
1.043
45
69
1.043
1.210
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
45
36
1.134
1.161
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
45
34
1.043
1.096
Male
Ho
Female
Ho
45
57
1.000
1.187
Mean
52
45
Mean
38
45
45
23
1.074
1.000
Variance
316
0
Variance
334.6666667
0
45
50
1.020
1.041
Observations
25
25
Observations
25
25
45
24
0.903
1.043
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
45
75
1.122
1.119
df
24
df
24
45
24
0.903
1.043
t Stat
1.968903827
t Stat
-1.913206357
45
24
0.982
1.043
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.033862118
45
23
1.086
1.000
t Critical one-tail
1.71088208
t Critical one-tail
1.71088208
45
22
1.075
0.956
P(T<=t) two-tail
0.060615701
P(T<=t) two-tail
0.067724237
45
35
1.052
1.129
t Critical two-tail
2.063898562
t Critical two-tail
2.063898562
45
24
1.140
1.043
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
45
77
1.087
1.149
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
45
55
1.052
1.145
Is P-value > 0.05?
Is P-value > 0.05?
45
65
1.157
1.140
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
<1 point>
2
Based on our sample data set, perform a 2-sample t-test to see if the pop.
Practice Problems Chapter 10For this lesson, we will be co.docx
Practice Problems: Chapter 10
For this lesson, we will be comparing two group population values (or two paired observations for those in the population). There are some subtle differences in the formulas used for computing the Standard Error used in both our Confidence Intervals of the Difference and in our Hypothesis Tests which are noted in the instructions below.
1) We wish to compare the population proportion of those graduates who pass the Bar Exam on the first try for two small law schools. The researcher has randomly selected graduates from the previous 4 years graduating classes and asked those selected if they passed the Bar Exam on the first try. Law School A had 150 out of 200 sampled who indicated that they passed the Bar Exam on the first try. Law School B had 170 out of 250 sampled who indicated that they passed the Bar Exam on the first try. We wish to see if there is a difference between the population proportions of those who passed the Bar Exam on the first try for the two law schools (p1 is for School A, p2 is for School B).
A) First, we want to construct a 95% Confidence Interval of the Difference between the two population proportions. Here we use SE =
We use this SE formula for confidence intervals of the difference between population proportions.
i) Calculate your SE. Show all work
ii) What is your Z multiplier?
iii) Construct your 95% Confidence Interval of the Difference. Show all work and indicate the endpoints.
iv) Is there a difference in the two group population proportions? Why or why not?
B)Now, do a Hypothesis Test to see if there is a difference between the two population proportions.
i) Since we are testing to see if there is a difference, our alternative is that there is a difference and our null is that there is no difference:
Ho: p1-p2 = 0; Ha: p1 - p2 ≠ 0 Given that the null hypothesis is that there is no difference (ie: assuming equal population values), we use the pooled to calculate our SE
=
Calculate and . Show all work.
ii) Calculate our Z test statistic. Show all work.
iii) What is the p-value of our Z test statistic? Show all work.
iv) What is your conclusion and why?
2) We wish to test whether a program teaching social skills to elementary students has an effect on social skills in third graders. In one elementary school, we randomly assign three classes out of six classes of third graders to participate in the program. The three classes who do not participate in the program act as the control group. We are told that the composition of the six classes is similar in terms of gender composition, IQ, etc. Every third grader is evaluated by their teacher and given a social skills assessment score at the beginning of the study. Higher scores mean the student has a higher level of social skills. At the end of the eight week program all students are then evaluated again by their teacher and given a social skills assessment .
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The 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)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint
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, 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.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0Standard Deviation8.48.76.4Range28.030.021.03What 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?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that .
howryet- increwing their energetic reyquirements -11-- When saline lak.pdf
howryet, increwing their energetic reyquirements [11]. When saline lake water levels decline and
their salinities rise [12]. emote sensing dats to monitoe long term changpe in suirface ment
vertebrases, macrinvertobrates, and other organiems are, thenefore, unable to respond, leaving
ecoiystems comon thum [16,17]. worldwide [22]. Climate change and incruating anthropogenic
water usage have been the thain drivers of dedines in soline bke levels 115, 18L, bat other
anthropogenic factars may also be 2. Methods important. Fur inatance, in the 'Lithum Triangle'
of Bolivia, Argentina, and Chile (figure De increasing demand for: (a) Study spericis and study
arei lithium for use in electric cars, enengy storage, and celluiar Chivan, Andean and farmin'
Hlamingos are long -lived waterthirde. telephones has driven an eightfold increase in liatuium
prothoction over the past twe deeades and is potentially that beved and to varying degres remvin
reident in the atregion [ 19200 ) . These salares are not enly biodiverse [ 21 ] but range eatending
acres southem South America Pbi Andest provide considerable economic and health-related
benefits for local prople [ 22 , 23 ] The impacts of littium mining asay be particularly eatenname
indiniduabs spend the nontiredies sease in aline When sive in arid envirenments like the Uithium
Triangle [24] west of the Andes. All tlece youis breed during the Ausinal munnine, math
reponduction perking in lanuary [3P]. The Chiloar portion of the Lathium Triangle comprises firs
complaie of salars (silt plyyas) and Louras (weitands. the production of 1 ton of lithium require
approes - pontw-Husso, Surine, Rupal, Tars and Acicamu-located 400000 ] of water [25]. Some
alares within the pgion ane alneady a4 historically low water lewels [ 26 ] and gradient 6 20 man
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fauna have: already been nutely 1/2 of the global lathum noerves [45/ ard mutuction
documented, affecting local human popolations via decreases in the Alacuma is prodiked to
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ui0, Andean (Phixmicguimat audinus) and fames flamingos above 2018 lenels [20]
(Phenonoparmas jamesi. Two of these species are endemic to (b) Flamingo surveys cesses [33]
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How would the deletion of the Poly-A tail decrease protein expression-.pdf
How would the deletion of the Poly-A tail decrease protein expression?
A.) The spliceosome would incorrectly process the pre-mRNA
B.) The stability of the mRNA would be compromised in the cytoplasm
C.) Transcription factors wouldn't be able to bind to the DNA and recruit RNA polymerase for
transcription
D.) Initiation factors would not be able to bind to the DNA and recruit DNA polymerase for
transcription.
How to calculate the NPV- Benefit to Cost Ratio- IRR- and Payback Peri.pdf
How to calculate the NPV, Benefit to Cost Ratio, IRR, and Payback Period in Excel.
How to calculate the NPV- Benefit to Cost Ratio- IRR- and Payback Peri (1).pdf
How to calculate the NPV, Benefit to Cost Ratio, IRR, and Payback Period in a simple
formula.
How many different 10-letter words (real or imaginary) can be formed f.pdf
How many different 10-letter words (real or imaginary) can be formed from the following
letters? U , E , N , Z , Y , G , E , F , K , X ten-letter words (real or imaginary) can be formed with
the given letters. (Type a whole number.).
How does MRP ensure lower inventory levels- A- By reducing the number.pdf
How does MRP ensure lower inventory levels?
A. By reducing the number of suppliers to an organization
B. By identifying redundant warehousing facilities
C. By identifying outsourced manufacturing units
D. By ascertaining the right amount of inventory needed for production
Which is true about scheduling for services?
A. It can be inventoried.
B. It deals with the identification of suppliers.
C. It requires capacity control.
D. lt is purely concerned with sequencing..
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ScoreWeek 3ANOVA and Paired T-test.docx
Score:
Week 3
ANOVA and Paired T-test
At this point we know the following about male and female salaries.
a.
Male and female overall average salaries are not equal in the population.
b.
Male and female overall average compas are equal in the population, but males are a bit more spread out.
c.
The male and female salary range are almost the same, as is their age and service.
d.
Average performance ratings per gender are equal.
Let's look at some other factors that might influence pay - education(degree) and performance ratings.
<1 point>
1
Last week, we found that average performance ratings do not differ between males and females in the population.
Now we need to see if they differ among the grades. Is the average performace rating the same for all grades?
(Assume variances are equal across the grades for this ANOVA.)
You can use these columns to place grade Perf Ratings if desired.
A
B
C
D
E
F
Null Hypothesis:
Alt. Hypothesis:
Place
B17 in Outcome range box.
Interpretation:
What is the p-value:
Is P-value < 0.05?
Do we REJ or Not reject the null?
If
the null hypothesis was rejected, what is the effect size value (eta squared):
Meaning of effect size measure:
What does that decision mean in terms of our equal pay question:
<1 point>
2
While it appears that average salaries per each grade differ, we need to test this assumption.
Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.)
Use the input table to the right to list salaries under each grade level.
Null Hypothesis:
If desired, place salaries per grade in these columns
Alt. Hypothesis:
A
B
C
D
E
F
.
Week 2Testing means - T-testsIn qu.docx
Week 2
Testing means - T-tests
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
Males
Females
Ho: Mean salary = 45
Ho: Mean salary = 45
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
Male
Ho
Female
Ho
Mean
52
45
Mean
38
45
Variance
316
0
Variance
334.667
0
Observations
25
25
Observations
25
25
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
df
24
df
24
t Stat
1.96890383
t Stat
-1.9132
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.03386
t Critical one-tail
1.71088208
t Critical one-tail
1.71088
P(T<=t) two-tail
0.0606157
P(T<=t) two-tail
0.06772
t Critical two-tail
2.06389856
t Critical two-tail
2.0639
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
Is P-value > 0.05?
Is P-value > 0.05?
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
2
Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.
(Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)
Ho:
Ha:
Test to use:
Place
B43 in Outcome range box.
P-value is:
Is P-value < 0.05?
Reject or do not reject Ho:
If
the null hypothesis was rejected, what is the effect size value:
Meaning of effect size measure:
Interpretation:
b.
Since the one and two sample t-test results provided different outcomes, which is the proper/correct apporach to comparing salary equality?
Why?
3
Based on our sample data set, can the male and female compas in the population be e.
Synthesizing the LiteratureChristopher.docx
Synthesizing the Literature
Christopher Walters
BTM7300: Scholarly Literature Review
Dr. Eva Philpot
December 6, 2015
Analyzing the increase in pay inequality over the last several years.
After reading J.C. Cunningham’s measuring wage inequality within and across US metropolitan areas, he presented how wages in the US have steadily declined for the unskilled worker and steadily increased for the skilled worker and or top 1 percent in US metropolitan area. The author also presented data showing how wage inequality is either negatively or positively affected within private and public sectors based on employee performance. The author made an interesting statement concerning US economic policies, and how they have impacted wage and pay inequality within the US from the 1970’s until today.
The author used one method or measure to acquire the data needed—the ratio of the 90th wage percentile to the 10th wage percentile, sometimes called the “90–10” ratio, inequality increased by 7 percent in the United States between 2003 and 2013. The study included data from the Occupational Employment Statistics program. Workers within 4 key areas of the wage distribution graph were sampled.
After reading D. J. Hammann, and T. Ren’s wage inequality and performance in nonprofit and for-profit organizations, the authors presented data to show how pay inequality is more prevalent in the for profit area than in the nonprofit area. The authors argue that tournament and fair wage/equity theories play a role in how each area impacts pay inequality. The authors used data from a study of workers in the nursing home industry. The data suggested that workers in nonprofit positions were more likely to be paid fairly or based on their skill level, as opposed to for profit positions and how similar positions are paid.
Finally, after reviewing wage inequality: A story of policy choices by L. Mishel, J. Schmitt, & H. Shierolz, the authors presented data to show how economic policies in the US has had a direct impact on wage and pay inequality on workers in the US. The authors focused on workers at four key points in the wage distribution: workers at the bottom (the 10th percentile, who earn more than 10 percent of workers, but less than 90 percent), the middle (the 50th percentile, or median, worker right in the middle of the wage distribution), the top (the 90th percentile, who earn more than 90 percent of workers, but less than the top 10 percent), and the very top (roughly, the top 1 percent). Based on wage and pay data, the authors discovered that wages for the top 1 percent increased by 156% between 1979 and 2007, and only 17% for the bottom 90 percent of wage earners during the same period.
Each study showed data that presented a steady decrease in non-skilled worker wages and an increase in skilled or executive wages. A difference would be the area of concentration on the study of the workers in the 90-10 study, and the study ...
Workforce diversity- A perspective of employees and students
Diversity can generally be defined as recognizing, understanding and accepting individual differences irrespective of their race, gender, age, class, ethnicity, physical ability, race, sexual orientation spiritual practice and so on. Diversity is growing in today’s organizations. Diversity can benefit the
organizations in enormous ways. Different values and perspectives shared by employees can make the
organization to compete in this turbulent environment. Globalization in this recent time has triggeredm more interaction amongst people from different cultures and backgrounds than before. People are now more open-minded in the marketplace worldwide with competition coming from almost everywhere in the continent. This study is an attempt to find out and compare the perception of people for workforce
diversity. In the present study the sample of 200 respondents was taken. Convenient sampling technique was used. The sample was drawn from Delhi. Interpretation of result was done on the basis of mean, standard deviation and ‘t’ test.
ScoreWeek 2Testing means - T-tests.docx
Score:
Week 2
Testing means - T-tests
Q3
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing.
Ho
Female
Male
Female
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis.
45
34
1.017
1.096
45
41
0.870
1.025
<1 point>
1
Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.
45
23
1.157
1.000
(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)
45
22
0.979
0.956
Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?
45
23
1.134
1.000
Males
Females
45
42
1.149
1.050
Ho: Mean salary = 45
Ho: Mean salary = 45
45
24
1.052
1.043
Ha: Mean salary =/= 45
Ha: Mean salary =/= 45
45
24
1.175
1.043
45
69
1.043
1.210
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,
45
36
1.134
1.161
having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.
45
34
1.043
1.096
Male
Ho
Female
Ho
45
57
1.000
1.187
Mean
52
45
Mean
38
45
45
23
1.074
1.000
Variance
316
0
Variance
334.6666667
0
45
50
1.020
1.041
Observations
25
25
Observations
25
25
45
24
0.903
1.043
Hypothesized Mean Difference
0
Hypothesized Mean Difference
0
45
75
1.122
1.119
df
24
df
24
45
24
0.903
1.043
t Stat
1.968903827
t Stat
-1.913206357
45
24
0.982
1.043
P(T<=t) one-tail
0.03030785
P(T<=t) one-tail
0.033862118
45
23
1.086
1.000
t Critical one-tail
1.71088208
t Critical one-tail
1.71088208
45
22
1.075
0.956
P(T<=t) two-tail
0.060615701
P(T<=t) two-tail
0.067724237
45
35
1.052
1.129
t Critical two-tail
2.063898562
t Critical two-tail
2.063898562
45
24
1.140
1.043
Conclusion: Do not reject Ho; mean equals 45
Conclusion: Do not reject Ho; mean equals 45
45
77
1.087
1.149
Is this a 1 or 2 tail test?
Is this a 1 or 2 tail test?
- why?
- why?
P-value is:
P-value is:
45
55
1.052
1.145
Is P-value > 0.05?
Is P-value > 0.05?
45
65
1.157
1.140
Why do we not reject Ho?
Why do we not reject Ho?
Interpretation:
<1 point>
2
Based on our sample data set, perform a 2-sample t-test to see if the pop.
Practice Problems Chapter 10For this lesson, we will be co.docx
Practice Problems: Chapter 10
For this lesson, we will be comparing two group population values (or two paired observations for those in the population). There are some subtle differences in the formulas used for computing the Standard Error used in both our Confidence Intervals of the Difference and in our Hypothesis Tests which are noted in the instructions below.
1) We wish to compare the population proportion of those graduates who pass the Bar Exam on the first try for two small law schools. The researcher has randomly selected graduates from the previous 4 years graduating classes and asked those selected if they passed the Bar Exam on the first try. Law School A had 150 out of 200 sampled who indicated that they passed the Bar Exam on the first try. Law School B had 170 out of 250 sampled who indicated that they passed the Bar Exam on the first try. We wish to see if there is a difference between the population proportions of those who passed the Bar Exam on the first try for the two law schools (p1 is for School A, p2 is for School B).
A) First, we want to construct a 95% Confidence Interval of the Difference between the two population proportions. Here we use SE =
We use this SE formula for confidence intervals of the difference between population proportions.
i) Calculate your SE. Show all work
ii) What is your Z multiplier?
iii) Construct your 95% Confidence Interval of the Difference. Show all work and indicate the endpoints.
iv) Is there a difference in the two group population proportions? Why or why not?
B)Now, do a Hypothesis Test to see if there is a difference between the two population proportions.
i) Since we are testing to see if there is a difference, our alternative is that there is a difference and our null is that there is no difference:
Ho: p1-p2 = 0; Ha: p1 - p2 ≠ 0 Given that the null hypothesis is that there is no difference (ie: assuming equal population values), we use the pooled to calculate our SE
=
Calculate and . Show all work.
ii) Calculate our Z test statistic. Show all work.
iii) What is the p-value of our Z test statistic? Show all work.
iv) What is your conclusion and why?
2) We wish to test whether a program teaching social skills to elementary students has an effect on social skills in third graders. In one elementary school, we randomly assign three classes out of six classes of third graders to participate in the program. The three classes who do not participate in the program act as the control group. We are told that the composition of the six classes is similar in terms of gender composition, IQ, etc. Every third grader is evaluated by their teacher and given a social skills assessment score at the beginning of the study. Higher scores mean the student has a higher level of social skills. At the end of the eight week program all students are then evaluated again by their teacher and given a social skills assessment .
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseDegreeGender1GrStudents: Copy the Student Data file data values into this sheet to assist in doing your weekly assignments.The 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)? Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.The column labels in the table mean:ID – Employee sample number Salary – Salary in thousands Age – Age in yearsPerformance Rating - Appraisal rating (employee evaluation score)Service – Years of service (rounded)Gender – 0 = male, 1 = female Midpoint – salary grade midpoint Raise – percent of last raiseGrade – job/pay gradeDegree (0= BS\BA 1 = MS)Gender1 (Male or Female)Compa - salary divided by midpoint
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, 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.Some of the values are completed for you - please finish the table.SalaryCompaAgePerf. Rat.ServiceOverallMean35.785.99.0Standard Deviation8.251311.41475.7177Note - data is a sample from the larger company populationRange304521FemaleMean32.584.27.9Standard Deviation6.913.64.9Range26.045.018.0MaleMean38.987.610.0Standard Deviation8.48.76.4Range28.030.021.03What 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?4A key issue in comparing data sets is to see if they are distributed/shaped the same. We can do this by looking at some measures of wheresome selected values are within each data set - that .
Similar to help The accompanying data provides the results of a survey of 27 empl.pdf (16)
Workforce diversity- A perspective of employees and students
Workforce diversity- A perspective of employees and students
Practice Problems Chapter 10For this lesson, we will be co.docx
Practice Problems Chapter 10For this lesson, we will be co.docx
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DataIDSalaryCompaMidpoint AgePerformance RatingServiceGenderRaiseD.docx
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Private 12837
Private 12699
Private 17978
Private 18381
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Private 17798
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Private 15632
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Private 24089
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Private 22938
Private 27649
Private 23251
Private 24491
Private 24080
Private 21327
Private 15674
Private 24907
Private 33138
Private 35757
Private 28419
Private 30023
Private 22848
Private 14238
Private 35862
Private 31300
Private 28698
Private 32101
Private 28358
Private 32501
Private 38437
Private 28976
Private 35439
Private 31161
Private 23776
Private 27839
Private 30539
Private 30960
Private 24624
Private 34912
Private 34573
Private 26817
Private 37847
Private 32477
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Private 16759
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Private 29130
Private 28670
Private 31756
Private 31698
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Private 36575
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Private 34725
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Private 33619
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Public 20997
Public 27421
Public 24309
Public 26058
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Public 18709
Public 17252
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Public 27314
Public 19649
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Public 24735
Public 23178
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Public 22920
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Public 33028
Public 20091
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Public 27059
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Public 24574
Public 33906
Public 20058
Public 22271
Public 27703
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Jose-Rizal-and-Philippine-Nationalism-National-Symbol-2.pptx
To provide details in philippine national symbols
Digital Tools and AI for Teaching Learning and Research
This Presentation in details discusses on Digital Tools and AI for Teaching Learning and Research
Supporting (UKRI) OA monographs at Salford.pptx
How libraries can support authors with open access requirements for UKRI funded books
Wednesday 22 May 2024, 14:00-15:00.
Unit 8 - Information and Communication Technology (Paper I).pdf
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
How libraries can support authors with open access requirements for UKRI fund...
How libraries can support authors with open access requirements for UKRI funded books
Wednesday 22 May 2024, 14:00-15:00.
Chapter 3 - Islamic Banking Products and Services.pptx
Chapter 3 - Islamic Banking Products and Services.pptxMohd Adib Abd Muin, Senior Lecturer at Universiti Utara Malaysia
This slide is prepared for master's students (MIFB & MIBS) UUM. May it be useful to all.Basic Civil Engineering Notes of Chapter-6, Topic- Ecosystem, Biodiversity G...
Basic Civil Engineering Notes of Chapter-6, Topic- Ecosystem, Biodiversity Green house effect & Hydrological cycle
Types of Ecosystem
(1) Natural Ecosystem
(2) Artificial Ecosystem
component of ecosystem
Biotic Components
Abiotic Components
Producers
Consumers
Decomposers
Functions of Ecosystem
Types of Biodiversity
Genetic Biodiversity
Species Biodiversity
Ecological Biodiversity
Importance of Biodiversity
Hydrological Cycle
Green House Effect
MARUTI SUZUKI- A Successful Joint Venture in India.pptx
Let us know about Maruti Suzuki, a successful Joint venture in India.
1.4 modern child centered education - mahatma gandhi-2.pptx
Child centred education is an educational approach that priorities the interest, needs and abilities of the child in the learning process.
Home assignment II on Spectroscopy 2024 Answers.pdf
Answers to Home assignment on UV-Visible spectroscopy: Calculation of wavelength of UV-Visible absorption
2024.06.01 Introducing a competency framework for languag learning materials ...
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Basic_QTL_Marker-assisted_Selection_Sourabh.ppt
This presentation provides an introduction to quantitative trait loci (QTL) analysis and marker-assisted selection (MAS) in plant breeding. The presentation begins by explaining the type of quantitative traits. The process of QTL analysis, including the use of molecular genetic markers and statistical methods, is discussed. Practical examples demonstrating the power of MAS are provided, such as its use in improving crop traits in plant breeding programs. Overall, this presentation offers a comprehensive overview of these important genomics-based approaches that are transforming modern agriculture.
Sectors of the Indian Economy - Class 10 Study Notes pdf
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Synthetic Fiber Construction in lab .pptx
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
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Instructions for Submissions thorugh G- Classroom.pptx
Instructions for Submissions thorugh G- Classroom.pptx
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Jose-Rizal-and-Philippine-Nationalism-National-Symbol-2.pptx
Digital Tools and AI for Teaching Learning and Research
Digital Tools and AI for Teaching Learning and Research
Unit 8 - Information and Communication Technology (Paper I).pdf
Unit 8 - Information and Communication Technology (Paper I).pdf
How libraries can support authors with open access requirements for UKRI fund...
How libraries can support authors with open access requirements for UKRI fund...
Chapter 3 - Islamic Banking Products and Services.pptx
Chapter 3 - Islamic Banking Products and Services.pptx
Basic Civil Engineering Notes of Chapter-6, Topic- Ecosystem, Biodiversity G...
Basic Civil Engineering Notes of Chapter-6, Topic- Ecosystem, Biodiversity G...
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1.4 modern child centered education - mahatma gandhi-2.pptx
Home assignment II on Spectroscopy 2024 Answers.pdf
Home assignment II on Spectroscopy 2024 Answers.pdf
2024.06.01 Introducing a competency framework for languag learning materials ...
2024.06.01 Introducing a competency framework for languag learning materials ...
Sectors of the Indian Economy - Class 10 Study Notes pdf
Sectors of the Indian Economy - Class 10 Study Notes pdf
help The accompanying data provides the results of a survey of 27 empl.pdf
- 1. help The accompanying data provides the results of a survey of 27 employees in a tax division of a Fortune 100 company. a. Test the null hypothesis that the average number of years of service is the same for males and females. Assume that the population variances are unequal. b. Test the null hypothesis that the average years of undergraduate study is the same for males and females Assume that the populion variances are Click the icon to view the survey data. a. Is there sufficient evidence at the 0.05 level of significance that the average number of years of service is the same for males and females? Determine the null hypothesis, H 0 , and the alternative hypothesis, H 1 . Let females be population 1 and males be population 2 H 0 1 2 = 0 H 1 1 2 = 0 (Type integers or decimals. Do not round.) Compute the test statistic. (Round to two decimal places as needed.) Survey data