COMMUNITY CORRECTIONS
Prepared By:
Date:
PROBATION
Description:
Purpose(s) served:
Advantages:
1.
2.
3.
Drawbacks:
1.
2.
3.
INTERMEDIATE SANCTIONS
Name of punishment: COMMUNITY SERVICE
Description:
Purpose(s) served:
Advantages:
1.
2.
3.
Drawbacks:
1.
2.
3.
Name of punishment: RESTITUTION
Description:
Purpose(s) served:
Advantages:
1.
2.
3.
Drawbacks:
1.
2.
3.
Name of punishment: HOUSE ARREST
Description:
Purpose(s) served:
Advantages:
1.
2.
3.
Drawbacks:
1.
2.
3.
REFERENCES
1
Day 08 ActivityFisher & HughesSeptember 21, 2018Study
A study was conducted to determine the effects of alcohol on human reaction times. Fifty-seven adult individuals within two-age groups were recruited for this study and were randomly allocated into one of three alcohol treatment groups – a control where the subjects remain sober during the entire study, a moderate group were the subject is supplied alcohol but is limited in such a way that their blood alcohol content (BAC) remains under the legal limit to drive (BAC of 0.08) and a group that received a high amount of alcohol to which their BAC may exceed the legal limit for driving. Each subject was trained on a video game system and their reaction time (in milliseconds) to a visual stimulus was recorded at 7 time points 30 minutes apart (labeled T0=0, T1=30, T2=60 and so on). At time point T0, all subjects were sober and those in one of the alcohol consumption groups began drinking after the first measured reaction time (controlled within the specifications outlined). The researcher is interested in determining the influence alcohol and age (namely, is reaction time different for those in the 20s versus 30s) has on reaction times.
The task for today is to do a complete analysis for this study and dig into the effects of alcohol, age and time have on reaction times.Data input and wrangling
First read in the data:alcohol <- read.csv("alcoholReaction.csv")
head(alcohol)## Subject Age Alcohol T0 T1 T2 T3 T4 T5 T6
## 1 1 24 Control 255.3 254.8 256.4 255.1 257.0 256.1 257.0
## 2 2 34 Control 250.1 249.2 249.0 248.0 248.0 248.9 248.1
## 3 3 31 Control 248.2 247.1 246.9 246.7 246.0 246.0 247.0
## 4 4 24 Control 253.9 253.8 254.9 254.1 253.2 254.1 255.0
## 5 5 38 Control 250.0 251.0 250.0 249.9 248.8 249.1 249.9
## 6 6 38 Control 246.0 248.0 247.0 248.1 248.1 246.9 244.0
Note, the Age variable is recorded as an actual age in years, not the category of 20s or 30s like we want – we need to dichotomize this variable. Also note the data is in wide format – the reaction times (the response variables) are spread over multiple columns. We need a way to gather these columns into a single column. So we need to do some data processing.
First consider the below code:head(alcohol %>%
mutate(Age = case_when(Age<31 ~ "20s",
Age %in% 31:40 ~ "30s")))## Subject Age Alcohol .
Day 08 ActivityFisher & HughesSeptember 21, 2018StudyA study was c.docxedwardmarivel
Day 08 ActivityFisher & HughesSeptember 21, 2018Study
A study was conducted to determine the effects of alcohol on human reaction times. Fifty-seven adult individuals within two-age groups were recruited for this study and were randomly allocated into one of three alcohol treatment groups – a control where the subjects remain sober during the entire study, a moderate group were the subject is supplied alcohol but is limited in such a way that their blood alcohol content (BAC) remains under the legal limit to drive (BAC of 0.08) and a group that received a high amount of alcohol to which their BAC may exceed the legal limit for driving. Each subject was trained on a video game system and their reaction time (in milliseconds) to a visual stimulus was recorded at 7 time points 30 minutes apart (labeled T0=0, T1=30, T2=60 and so on). At time point T0, all subjects were sober and those in one of the alcohol consumption groups began drinking after the first measured reaction time (controlled within the specifications outlined). The researcher is interested in determining the influence alcohol and age (namely, is reaction time different for those in the 20s versus 30s) has on reaction times.
The task for today is to do a complete analysis for this study and dig into the effects of alcohol, age and time have on reaction times.Data input and wrangling
First read in the data:alcohol <- read.csv("alcoholReaction.csv")
head(alcohol)## Subject Age Alcohol T0 T1 T2 T3 T4 T5 T6
## 1 1 24 Control 255.3 254.8 256.4 255.1 257.0 256.1 257.0
## 2 2 34 Control 250.1 249.2 249.0 248.0 248.0 248.9 248.1
## 3 3 31 Control 248.2 247.1 246.9 246.7 246.0 246.0 247.0
## 4 4 24 Control 253.9 253.8 254.9 254.1 253.2 254.1 255.0
## 5 5 38 Control 250.0 251.0 250.0 249.9 248.8 249.1 249.9
## 6 6 38 Control 246.0 248.0 247.0 248.1 248.1 246.9 244.0
Note, the Age variable is recorded as an actual age in years, not the category of 20s or 30s like we want – we need to dichotomize this variable. Also note the data is in wide format – the reaction times (the response variables) are spread over multiple columns. We need a way to gather these columns into a single column. So we need to do some data processing.
First consider the below code:head(alcohol %>%
mutate(Age = case_when(Age<31 ~ "20s",
Age %in% 31:40 ~ "30s")))## Subject Age Alcohol T0 T1 T2 T3 T4 T5 T6
## 1 1 20s Control 255.3 254.8 256.4 255.1 257.0 256.1 257.0
## 2 2 30s Control 250.1 249.2 249.0 248.0 248.0 248.9 248.1
## 3 3 30s Control 248.2 247.1 246.9 246.7 246.0 246.0 247.0
## 4 4 20s Control 253.9 253.8 254.9 254.1 253.2 254.1 255.0
## 5 5 30s Control 250.0 251.0 250.0 249.9 248.8 249.1 249.9
## 6 6 30s Control 246.0 248.0 247.0 248.1 248.1 246.9 244.0
case_when is essentially a piece-wise comparison. When Age is less than 31, you overwrite Age variable .
Chapter 16 Inference for RegressionClimate ChangeThe .docxketurahhazelhurst
Chapter 16: Inference for Regression
Climate Change
The earth has been getting warmer. Most climate scientists agree that one important cause of the warming is
the increase in atmospheric levels of carbon dioxide (CO2), a green house gas. Here is part of a regression
analysis of the mean annual CO2 concentration (CO2) in the atmosphere, measured in parts per thousand
(ppt), at the top of Mauna Loa in Hawaii and the mean annual air temperature (Temp) over both land and
sea across the globe, in degrees Celsius.
Let’s first read the dataset into R
climate <- read.table('Climate_Change.txt', sep = '\t', header = TRUE)
and take a look at the data structure:
str(climate)
## 'data.frame': 29 obs. of 3 variables:
## $ year: int 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ...
## $ Temp: num 14.2 14.3 14.1 14.3 14.1 ...
## $ CO2 : num 339 340 341 342 344 ...
We see three variables, which are year, Temp (mean annual air temperature) and CO2 (mean annual CO2
concentration), and there are 29 observations in each variable.
We now take Temp as the response variable and CO2 the predictor variable, to study their relationship. To see
if linear regression is appropriate, we make a scatterplot of Temp against CO2
plot(climate$CO2, climate$Temp, xlab = 'CO2 Concentration', ylab = 'Temperature')
340 350 360 370 380
1
4
.1
1
4
.3
1
4
.5
CO2 Concentration
Te
m
p
e
ra
tu
re
It seems reasonable to fit a linear model to the dataset, because both variables are quantitative, the data
points show a linear pattern, and there is no outlier. So, let’s fit the model:
imod <- lm(Temp ~ CO2, data = climate)
1
The summary of the fitted model is given by
summary(imod)
##
## Call:
## lm(formula = Temp ~ CO2, data = climate)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16809 -0.07972 0.00194 0.07013 0.18532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.707076 0.481006 22.260 < 2e-16 ***
## CO2 0.010062 0.001336 7.534 4.19e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09847 on 27 degrees of freedom
## Multiple R-squared: 0.6776, Adjusted R-squared: 0.6657
## F-statistic: 56.76 on 1 and 27 DF, p-value: 4.192e-08
which contains a lot of information. We see that R2 = 0.6776 and the SD of residuals se = 0.09847 (the
estimator of population standard deviation σ) with 27 degrees of freedom. In Coefficients section we
see the intercept b0 = 10.71 and the slope b1 = 0.01. Their standard errors are SE(b0) = 0.481 and
SE(b1) = 0.00134. Their t-test statistics are t0 = b0/SE(b0) = 22.26 and t1 = b1/SE(b1) = 7.534. Their
corresponding (two-tailed) p-values are very small (<2e-16 and 4.19e-08). As a result, we reject H0 : β1 = 0
and conclude there is a positive correlation between Temp and CO2. The b1 = 0.01 can be interpreted as
follows: The air temperature will increase by 0.01 degrees Celsius on average if the CO2 concentration in the
atmosphere increases by 1 p ...
After reading this module, you should be able to . . .
1.01 Identify the base quantities in the SI system.
1.02 Name the most frequently used prefixes for
SI units.
1.03 Change units (here for length, area, and volume) by
using chain-link conversions.
1.04 Explain that the meter is defined in terms of the speed of
light in vacuum.
Shortcut Design Method for Multistage Binary Distillation via MS-ExceIJERA Editor
Multistage distillation is most widely used industrial method for separating chemical mixtures with high energy consumptions especially when relative volatility of key components is lower than 1.5. The McCabe Thiele is considered to be the simplest and perhaps most instructive method for the conceptual design of binary distillation column which is still widely used, mainly for quick preliminary calculations. In this present work, we provide a numerical solution to a McCabe-Thiele method to find out theoretical number of stages for ideal and non-ideal binary system, reflux ratio, condenser duty, reboiler duty, each plate composition inside the column. Each and every point related to McCabe-Thiele in MS-Excel to give quick column dimensions are discussed in details
Day 08 ActivityFisher & HughesSeptember 21, 2018StudyA study was c.docxedwardmarivel
Day 08 ActivityFisher & HughesSeptember 21, 2018Study
A study was conducted to determine the effects of alcohol on human reaction times. Fifty-seven adult individuals within two-age groups were recruited for this study and were randomly allocated into one of three alcohol treatment groups – a control where the subjects remain sober during the entire study, a moderate group were the subject is supplied alcohol but is limited in such a way that their blood alcohol content (BAC) remains under the legal limit to drive (BAC of 0.08) and a group that received a high amount of alcohol to which their BAC may exceed the legal limit for driving. Each subject was trained on a video game system and their reaction time (in milliseconds) to a visual stimulus was recorded at 7 time points 30 minutes apart (labeled T0=0, T1=30, T2=60 and so on). At time point T0, all subjects were sober and those in one of the alcohol consumption groups began drinking after the first measured reaction time (controlled within the specifications outlined). The researcher is interested in determining the influence alcohol and age (namely, is reaction time different for those in the 20s versus 30s) has on reaction times.
The task for today is to do a complete analysis for this study and dig into the effects of alcohol, age and time have on reaction times.Data input and wrangling
First read in the data:alcohol <- read.csv("alcoholReaction.csv")
head(alcohol)## Subject Age Alcohol T0 T1 T2 T3 T4 T5 T6
## 1 1 24 Control 255.3 254.8 256.4 255.1 257.0 256.1 257.0
## 2 2 34 Control 250.1 249.2 249.0 248.0 248.0 248.9 248.1
## 3 3 31 Control 248.2 247.1 246.9 246.7 246.0 246.0 247.0
## 4 4 24 Control 253.9 253.8 254.9 254.1 253.2 254.1 255.0
## 5 5 38 Control 250.0 251.0 250.0 249.9 248.8 249.1 249.9
## 6 6 38 Control 246.0 248.0 247.0 248.1 248.1 246.9 244.0
Note, the Age variable is recorded as an actual age in years, not the category of 20s or 30s like we want – we need to dichotomize this variable. Also note the data is in wide format – the reaction times (the response variables) are spread over multiple columns. We need a way to gather these columns into a single column. So we need to do some data processing.
First consider the below code:head(alcohol %>%
mutate(Age = case_when(Age<31 ~ "20s",
Age %in% 31:40 ~ "30s")))## Subject Age Alcohol T0 T1 T2 T3 T4 T5 T6
## 1 1 20s Control 255.3 254.8 256.4 255.1 257.0 256.1 257.0
## 2 2 30s Control 250.1 249.2 249.0 248.0 248.0 248.9 248.1
## 3 3 30s Control 248.2 247.1 246.9 246.7 246.0 246.0 247.0
## 4 4 20s Control 253.9 253.8 254.9 254.1 253.2 254.1 255.0
## 5 5 30s Control 250.0 251.0 250.0 249.9 248.8 249.1 249.9
## 6 6 30s Control 246.0 248.0 247.0 248.1 248.1 246.9 244.0
case_when is essentially a piece-wise comparison. When Age is less than 31, you overwrite Age variable .
Chapter 16 Inference for RegressionClimate ChangeThe .docxketurahhazelhurst
Chapter 16: Inference for Regression
Climate Change
The earth has been getting warmer. Most climate scientists agree that one important cause of the warming is
the increase in atmospheric levels of carbon dioxide (CO2), a green house gas. Here is part of a regression
analysis of the mean annual CO2 concentration (CO2) in the atmosphere, measured in parts per thousand
(ppt), at the top of Mauna Loa in Hawaii and the mean annual air temperature (Temp) over both land and
sea across the globe, in degrees Celsius.
Let’s first read the dataset into R
climate <- read.table('Climate_Change.txt', sep = '\t', header = TRUE)
and take a look at the data structure:
str(climate)
## 'data.frame': 29 obs. of 3 variables:
## $ year: int 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ...
## $ Temp: num 14.2 14.3 14.1 14.3 14.1 ...
## $ CO2 : num 339 340 341 342 344 ...
We see three variables, which are year, Temp (mean annual air temperature) and CO2 (mean annual CO2
concentration), and there are 29 observations in each variable.
We now take Temp as the response variable and CO2 the predictor variable, to study their relationship. To see
if linear regression is appropriate, we make a scatterplot of Temp against CO2
plot(climate$CO2, climate$Temp, xlab = 'CO2 Concentration', ylab = 'Temperature')
340 350 360 370 380
1
4
.1
1
4
.3
1
4
.5
CO2 Concentration
Te
m
p
e
ra
tu
re
It seems reasonable to fit a linear model to the dataset, because both variables are quantitative, the data
points show a linear pattern, and there is no outlier. So, let’s fit the model:
imod <- lm(Temp ~ CO2, data = climate)
1
The summary of the fitted model is given by
summary(imod)
##
## Call:
## lm(formula = Temp ~ CO2, data = climate)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16809 -0.07972 0.00194 0.07013 0.18532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.707076 0.481006 22.260 < 2e-16 ***
## CO2 0.010062 0.001336 7.534 4.19e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09847 on 27 degrees of freedom
## Multiple R-squared: 0.6776, Adjusted R-squared: 0.6657
## F-statistic: 56.76 on 1 and 27 DF, p-value: 4.192e-08
which contains a lot of information. We see that R2 = 0.6776 and the SD of residuals se = 0.09847 (the
estimator of population standard deviation σ) with 27 degrees of freedom. In Coefficients section we
see the intercept b0 = 10.71 and the slope b1 = 0.01. Their standard errors are SE(b0) = 0.481 and
SE(b1) = 0.00134. Their t-test statistics are t0 = b0/SE(b0) = 22.26 and t1 = b1/SE(b1) = 7.534. Their
corresponding (two-tailed) p-values are very small (<2e-16 and 4.19e-08). As a result, we reject H0 : β1 = 0
and conclude there is a positive correlation between Temp and CO2. The b1 = 0.01 can be interpreted as
follows: The air temperature will increase by 0.01 degrees Celsius on average if the CO2 concentration in the
atmosphere increases by 1 p ...
After reading this module, you should be able to . . .
1.01 Identify the base quantities in the SI system.
1.02 Name the most frequently used prefixes for
SI units.
1.03 Change units (here for length, area, and volume) by
using chain-link conversions.
1.04 Explain that the meter is defined in terms of the speed of
light in vacuum.
Shortcut Design Method for Multistage Binary Distillation via MS-ExceIJERA Editor
Multistage distillation is most widely used industrial method for separating chemical mixtures with high energy consumptions especially when relative volatility of key components is lower than 1.5. The McCabe Thiele is considered to be the simplest and perhaps most instructive method for the conceptual design of binary distillation column which is still widely used, mainly for quick preliminary calculations. In this present work, we provide a numerical solution to a McCabe-Thiele method to find out theoretical number of stages for ideal and non-ideal binary system, reflux ratio, condenser duty, reboiler duty, each plate composition inside the column. Each and every point related to McCabe-Thiele in MS-Excel to give quick column dimensions are discussed in details
These notes are of chemistry class 11th first chapter which are strictly according to CBSE & state Board. This notes covers Some basics concepts of chemistry i.e. Branches of chemistry, classification of matter & many more..
COMM 166 Final Research Proposal GuidelinesThe proposal should.docxdrandy1
COMM 166 Final Research Proposal Guidelines
The proposal should contain well-developed sections (Put clear titles on the top of each section) of your outline that you submitted earlier. The proposal should have seven (7) major sections:
1. Introduction: A brief overview of all your sections. Approx. one page
2. A summary of the literature review. In this section you would summarize the previous research (summarize at least 8-10 scholarly research articles), and also your field data collection results (if it was connected to your proposal topic). Also indicate the gaps in the previous research, including your pilot study, and the need for your research study. Please devote around three pages in reviewing the previous research and finding the gaps.
3. Arising from the literature review, write the Purpose Statement of your research (purpose statement should have all its parts clearly written. Follow the examples from textbook).
4. Identify two to three main hypotheses or research questions (based on the quantitative/qualitative research design). Also give some of your supporting research questions. Follow the examples from textbook.
5. Describe the research strategy of inquiry and methods that you would use and why. The method part should be the substantial part of your paper, around three pages. Define your knowledge claims, strategies, and methods from the textbook (and cite), why you chose them, and how you will conduct the research in detail.
6. A page on the significance of your study.
7. A complete reference list of your sources in APA style.
The total length of the paper should be between 8-10 pages (excluding the reference and cover pages).
If you have further questions, please do not hesitate to contact me.
Best wishes
Dev
mportant notes about grading:
1. Compiler errors: All code you submit must compile. Programs that do not compile will receive an automatic zero. If you run out of time, it is better to comment out the parts that do not compile, than hand in a more complete file that does not compile.
2. Late assignments: You must submit your code before the deadline. Verify on Sakai that you have submitted the correct version. If you submit the incorrect version before the deadline and realize that you have done so after the deadline, we will only grade the version received before the deadline.
A Prolog interpreter
In this project, you will implement a Prolog interpreter in OCaml.
If you want to implement the project in Python, download the source code and follow the README file. Parsing functions and test-cases are provided.
Pseudocode
Your main task is to implement the non-deterministic abstract interpreter covered in the lecture Control in Prolog. The pseudocode of the abstract interpreter is in the lecture note.
Bonus
There is also a bonus task for implementing a deterministic Prolog interpreter with support for backtracking (recover from bad choices) and choice points (produce multiple results). Please refer to th.
COMMENTS You wrote an interesting essay; however, it is lacking t.docxdrandy1
COMMENTS: You wrote an interesting essay; however, it is lacking the introduction and conclusion paragraphs (1/3 deduction.) Remove the notations from the Reference List. Not all of your sources came from the UOP library and are peer reviewed, so you need to locate additional. You need a minimum of three peer reviewed sources from the UOP library. Prove your arguments using academic sources. Some paragraphs are too short, every paragraph should be five to eight sentences. You received a five point deduction for not including the introduction or conclusion.
SCORE: 10/15 Points (Deduction for not including an introduction or conclusion.)
The Inappropriateness of the Death Sentence
Add an introduction paragraph. Comment by Darlene Bennett: The introduction needs a topic sentence that describes the main idea of the paragraph, then provide background information and finally, include the thesis statement. The introduction should be five to eight sentences in length.
Thesis Statement Comment by Darlene Bennett: The thesis statement cannot be isolated by itself. You need to insert it as the last sentence of the introductory paragraph.
The death penalty, as practiced in some societies in the world, has had its positive aspects and negative aspects and my stand are that it should be abolished in any democratic state that seeks to ensure justice for both the victims of crime and the offenders.
The death sentence is against the bible and other religious guidelines Comment by Darlene Bennett: Capitalize the word “Bible.”
Death sentences against convicted criminals in the society go against the spirit and guidelines provided by religious faiths regarding our stay here in the universe. Comment by Darlene Bennett: Casual tone, stay formal and do not use first person pronouns.
Religious laws quoted in religious books prohibited the execution of man whatsoever. According to these religious laws, there is no compromise or a reason big enough to necessitate the punishment of an offender through death. The ten commandments developed by God himself in the book of Deuteronomy, under commandment five, prohibits the killing of a man. Universal religious laws concur with the Christian teaching regarding the execution of man as a way of punishment (Goldman, 2017). Comment by Darlene Bennett: This is a generalization – do all religious books state this? By Old Testament law, people were stoned for certain infractions. Be specific and provide a source for your fact. Comment by Darlene Bennett:
Religious teachings in all religion term human life as sacred and one that is not subject to limitation, in all circumstance. According to the various religions, it is God only who can terminate the life of a human being. They recommend the use of other means of justice for offenders to reform and revert back to their normal lives in the society (Goldman, 2017).
Further, no method of executing criminals is humane, all the methods are painful, inhumane and disrespectful.
More Related Content
Similar to COMMUNITY CORRECTIONSPrepared ByDatePROBATIONDescr.docx
These notes are of chemistry class 11th first chapter which are strictly according to CBSE & state Board. This notes covers Some basics concepts of chemistry i.e. Branches of chemistry, classification of matter & many more..
COMM 166 Final Research Proposal GuidelinesThe proposal should.docxdrandy1
COMM 166 Final Research Proposal Guidelines
The proposal should contain well-developed sections (Put clear titles on the top of each section) of your outline that you submitted earlier. The proposal should have seven (7) major sections:
1. Introduction: A brief overview of all your sections. Approx. one page
2. A summary of the literature review. In this section you would summarize the previous research (summarize at least 8-10 scholarly research articles), and also your field data collection results (if it was connected to your proposal topic). Also indicate the gaps in the previous research, including your pilot study, and the need for your research study. Please devote around three pages in reviewing the previous research and finding the gaps.
3. Arising from the literature review, write the Purpose Statement of your research (purpose statement should have all its parts clearly written. Follow the examples from textbook).
4. Identify two to three main hypotheses or research questions (based on the quantitative/qualitative research design). Also give some of your supporting research questions. Follow the examples from textbook.
5. Describe the research strategy of inquiry and methods that you would use and why. The method part should be the substantial part of your paper, around three pages. Define your knowledge claims, strategies, and methods from the textbook (and cite), why you chose them, and how you will conduct the research in detail.
6. A page on the significance of your study.
7. A complete reference list of your sources in APA style.
The total length of the paper should be between 8-10 pages (excluding the reference and cover pages).
If you have further questions, please do not hesitate to contact me.
Best wishes
Dev
mportant notes about grading:
1. Compiler errors: All code you submit must compile. Programs that do not compile will receive an automatic zero. If you run out of time, it is better to comment out the parts that do not compile, than hand in a more complete file that does not compile.
2. Late assignments: You must submit your code before the deadline. Verify on Sakai that you have submitted the correct version. If you submit the incorrect version before the deadline and realize that you have done so after the deadline, we will only grade the version received before the deadline.
A Prolog interpreter
In this project, you will implement a Prolog interpreter in OCaml.
If you want to implement the project in Python, download the source code and follow the README file. Parsing functions and test-cases are provided.
Pseudocode
Your main task is to implement the non-deterministic abstract interpreter covered in the lecture Control in Prolog. The pseudocode of the abstract interpreter is in the lecture note.
Bonus
There is also a bonus task for implementing a deterministic Prolog interpreter with support for backtracking (recover from bad choices) and choice points (produce multiple results). Please refer to th.
COMMENTS You wrote an interesting essay; however, it is lacking t.docxdrandy1
COMMENTS: You wrote an interesting essay; however, it is lacking the introduction and conclusion paragraphs (1/3 deduction.) Remove the notations from the Reference List. Not all of your sources came from the UOP library and are peer reviewed, so you need to locate additional. You need a minimum of three peer reviewed sources from the UOP library. Prove your arguments using academic sources. Some paragraphs are too short, every paragraph should be five to eight sentences. You received a five point deduction for not including the introduction or conclusion.
SCORE: 10/15 Points (Deduction for not including an introduction or conclusion.)
The Inappropriateness of the Death Sentence
Add an introduction paragraph. Comment by Darlene Bennett: The introduction needs a topic sentence that describes the main idea of the paragraph, then provide background information and finally, include the thesis statement. The introduction should be five to eight sentences in length.
Thesis Statement Comment by Darlene Bennett: The thesis statement cannot be isolated by itself. You need to insert it as the last sentence of the introductory paragraph.
The death penalty, as practiced in some societies in the world, has had its positive aspects and negative aspects and my stand are that it should be abolished in any democratic state that seeks to ensure justice for both the victims of crime and the offenders.
The death sentence is against the bible and other religious guidelines Comment by Darlene Bennett: Capitalize the word “Bible.”
Death sentences against convicted criminals in the society go against the spirit and guidelines provided by religious faiths regarding our stay here in the universe. Comment by Darlene Bennett: Casual tone, stay formal and do not use first person pronouns.
Religious laws quoted in religious books prohibited the execution of man whatsoever. According to these religious laws, there is no compromise or a reason big enough to necessitate the punishment of an offender through death. The ten commandments developed by God himself in the book of Deuteronomy, under commandment five, prohibits the killing of a man. Universal religious laws concur with the Christian teaching regarding the execution of man as a way of punishment (Goldman, 2017). Comment by Darlene Bennett: This is a generalization – do all religious books state this? By Old Testament law, people were stoned for certain infractions. Be specific and provide a source for your fact. Comment by Darlene Bennett:
Religious teachings in all religion term human life as sacred and one that is not subject to limitation, in all circumstance. According to the various religions, it is God only who can terminate the life of a human being. They recommend the use of other means of justice for offenders to reform and revert back to their normal lives in the society (Goldman, 2017).
Further, no method of executing criminals is humane, all the methods are painful, inhumane and disrespectful.
Commercial Space TravelThere are about a half dozen commercial s.docxdrandy1
Commercial Space Travel
There are about a half dozen commercial space entrepreneurs globally today. Pick one of those companies, and then provide a short history of their company, outline their current projects, and describe their future plans for space travel. Describe the biggest obstacles that they will have to overcome to achieve their goals.
Your initial discussion post should be succinct (only about 200–300 words) and include references to your sources.
.
CommentsPrice is the easiest way to make profit – all you.docxdrandy1
Comments:
Price is the easiest way to make profit – all you do is raise the price – it costs nothing and you have to do no work – just send out a new price sheet.
Distribution is the next easiest – sell the same stuff in different places – with minor changes.
Questions
1.
Define/explain:
A.
Supply chain
B.
Value delivery
C.
What/who are the distribution chain members:
D.
How does a distribution chain member add value to the consumer
E.
Vertical marketing system
F.
Horizontal marketing system
J.
Mutlichannel system
G.
Marketing logistics
H.
Supply chain management
I.
Major logistical functions
J.
Specialty stores
K. Department stores
L.
Supermarkets
M.
Convenience stores
N.
Discount stores
O.
Off price stores
P. Superstores
Q.
Corporate chain stores
R.
Voluntary chain
S.
Retailer Cooperative
T
Franchise organization
U.
Merchandising conglomerate
v.
Wholesalers
w.
Brokers
X.
Agents
2.
Read (or look up if needed)
“Zara – the technology giant of the fashion world”
-- explain how technology drives this company – remember women’s fashion has 4 to 5 seasons.
3.
What marketing mix decisions must retailers make:
A.
B.
C.
D.
E.
F.
G.
4.
Describe 4 distribution ideas/innovations you have witnessed:
A.
B.
C.
D.
.
COMM 1110 Library Research Assignment Objective To ensu.docxdrandy1
COMM 1110 Library Research Assignment
Objective:
To ensure students begin library research in a timely manner, selecting worthwhile sources and justifying
their inclusion.
Assignment:
Select five credible sources that can be used for your speech. For each source, provide a full APA
citation, an explanation of where/how you found the source, a summary of the information the source
contains, and an explanation of why it is relevant to your speech. Credible sources contain worthwhile
and trustworthy information from reliable sources.
Make sure you number each source and separate each component: citation, how source was discovered,
summary, and relevance.
Pay attention to all of the requirements in order to complete the assignment to the Satisfactory level.
Specifications for Satisfactory Completion
1. Format: Submitted paper is/has:
a. Double-spaced, with no extra spaces before or after paragraphs.
b. Times New Roman font.
c. 1-inch margins.
d. 12-point font.
e. Document is submitted with only the student’s name placed in the header and nothing in
the footer, with NO date, class, or professor information on the document (this is tracked
by Georgia View).
f. Document is submitted in .docx format.
g. Document is submitted by the due date in Georgia View.
h. Citations are in proper APA format.
2. Content:
a. At least five sources are presented, with each source containing all the required
components listed above.
b. Fewer than 5 grammar, punctuation, or spelling errors.
c. All sources come from credible outlets, including and especially GALILEO.
d. No more than two sources are in common with any group members.
.
COMM 1110 Persuasive Speech Evaluation Objective To lea.docxdrandy1
COMM 1110 Persuasive Speech Evaluation
Objective:
To learn how to identify areas for improvement in public speaking and evaluate observations, inferences,
and relationships in a speech.
Assignment:
Watch Dan Pink’s The Puzzle of Motivation at https://www.ted.com/talks/dan_pink_on_motivation/.
Prepare a paper that answers the following questions: How would you rate the speaker’s delivery? What
things did the speaker do well? What things need to be improved? What was the speaker trying to
accomplish? How many steps of Monroe’s Motivated Sequence were covered by the speech, and were
they in the correct order? When in the speech was each step of Monroe’s Motivated Sequence covered?
Pay attention to all of the requirements in order to complete the assignment to the Satisfactory level.
Specifications for Satisfactory Completion
1. Format: Submitted paper is/has:
a. Double-spaced, with no extra spaces before or after paragraphs.
b. Times New Roman font.
c. 1-inch margins.
d. 12-point font.
e. Document is submitted with only the student’s name placed in the header and nothing in
the footer, with NO date, class, or professor information on the document (this is tracked
by Georgia View).
f. Document is submitted in .docx format.
g. Document is submitted by the due date in Georgia View.
2. Content:
a. All questions are answered thoroughly.
b. Fewer than 5 grammar, punctuation, or spelling errors.
c. 300-600 words.
COMM 1110 Persuasive Speech EvaluationObjective:Assignment:Specifications for Satisfactory Completion
.
Comment The ANA is such an astonishing association. They help .docxdrandy1
Comment
The ANA is such an astonishing association. They help with new enactment, state-of-the-art data on nursing issues, confirmations and proceeding with training, thus a lot increasingly significant nursing subjects. I turned into a part as an understudy, yet I didn't comprehend the significance of being associated with these associations. In the present changing social insurance framework, it is so imperative to be taught and included on the present issues. The ANA has been a promoter for profession improvement and improving the wellbeing for all Americans for more than 100 years. I need to turn into an individual from this long-standing association to keep awake to-date on issues, proceed with my training, and have any kind of effect in the nursing field.
Comment
Being an advocate means a lot, at many different levels. For instance, as LVN being an advocate is hands on, RN would be collaborating with many different discipling, BSN would be all the combination and take it to a management level. As working for hospice being a patient advocate is so important at the end of life. Working with dying patients and educating families about the medications needed for end of life comfort. For instance, Morphine 20mg/ml give 1 ml Po/SL q 2 hours PRN pain. (severe pain 7-10). With out this education on medication regimen patient would suffer in pain.
.
Comments Excellent paper. It’s obvious that you put quite a bit of .docxdrandy1
Comments: Excellent paper. It’s obvious that you put quite a bit of work into this. Unfortunately, your paper needs adequate citations in the body of the text to meet our standards on plagiarism. You need to cite each textbook from your bibliography whenever you quote or use some information from the textbook or other resource. For example, writing (Jones 285) after the quote or information used means that you got it from the book whose author was Jones and the info came from page 285.
Laparoscopic cholecystectomy is a procedure in which laparoscopic techniques remove the gallbladder. It is the standard of care for symptomatic gallbladder disease, of which most are performed for symptomatic cholelithiasis. Other indications include acute cholecystitis, biliary dyskinesia, and gallstone pancreatitis.
Describe the reasons a patient might have the selected surgical procedure
The typical reason a cholecystectomy is a treatment of choice is inflammatory changes of gallbladder or blockage of bile flow by gallstones. Symptomatic cholelithiasis is the most common reason where gallstones in the gallbladder are blocking the bile flow and cause inflammation. The patient usually complains of episodic epigastric pain and right upper quadrant pain that radiates to the right shoulder. This pain is found to occur several hours after heavy meals and the patient experiences nausea, vomiting, bloating, fever, and right upper quadrant tenderness. Another condition is acute cholecystitis, where inflammation and symptoms are more prominent. The patient may have a fever, constant pain, positive Murphy's sign, or leukocytosis. Acute cholecystitis may be caused by calculous biliary tract disease with confirmed gallstones in the abdominal US. Acute acalculous cholecystitis usually occurs in critically ill patients, those with prolonged total parenteral nutrition, and some immunosuppressed patients. Patients with episodes of right upper quadrant pain (which are ‘classic' for biliary pain without evidence of cholelithiasis of US or ERCP) may also be referred for laparoscopic cholecystectomy. Gallstone pancreatitis (when small stones pass through the cystic duct) confirmed by cholangiography is another indication for laparoscopic cholecystectomy.
Describe the reasons a patient might be disqualified for this surgery and the options for the patient if any
A patient might be excluded for laparoscopic cholecystectomy due to acute general conditions that are a contraindication for any surgery such as an acute cardiac failure, uncontrolled hypertension, acute renal failure, pneumonia, etc. The condition should be treated by a primary care provider or specialist and the patient should be stable prior surgery. Additional contraindications may include the inability to tolerate general anesthesia, significant portal hypertension, uncorrectable coagulopathy, and multiple prior operations.
List the diagnostic tests and lab work that an attending surgeon might order and desc.
Community Assessment and Analysis PresentationThis assignment co.docxdrandy1
Community Assessment and Analysis Presentation
This assignment consists of both an interview and a PowerPoint (PPT) presentation.
Assessment/Interview
Select a community of interest in your region. Perform a physical assessment of the community.
1. Perform a direct assessment of a community of interest using the "Functional Health Patterns Community Assessment Guide."
2. Interview a community health and public health provider regarding that person's role and experiences within the community.
Interview Guidelines
Interviews can take place in-person, by phone, or by Skype.
Develop interview questions to gather information about the role of the provider in the community and the health issues faced by the chosen community.
Complete the "Provider Interview Acknowledgement Form" prior to conducting the interview. Submit this document separately in its respective drop box.
Compile key findings from the interview, including the interview questions used, and submit these with the presentation.
PowerPoint Presentation
Create a PowerPoint presentation of 15-20 slides (slide count does not include title and references slide) describing the chosen community interest.
Include the following in your presentation:
1. Description of community and community boundaries: the people and the geographic, geopolitical, financial, educational level; ethnic and phenomenological features of the community, as well as types of social interactions; common goals and interests; and barriers, and challenges, including any identified social determinates of health.
2. Summary of community assessment: (a) funding sources and (b) partnerships.
3. Summary of interview with community health/public health provider.
4. Identification of an issue that is lacking or an opportunity for health promotion.
5. A conclusion summarizing your key findings and a discussion of your impressions of the general health of the community.
While APA style, and thesis is required for the body of this assignment, solid academic writing is expected, and documentation of sources should be presented using APA format ting guidelines.
Functional Health Patterns Community Assessment Guide
Functional Health Pattern (FHP) Template Directions:
This FHP template is to be used for organizing community assessment data in preparation for completion of the topic assignment. Address every bulleted statement in each section with data or rationale for deferral. You may also add additional bullet points if applicable to your community.
Value/Belief Pattern
Predominant ethnic and cultural groups along with beliefs related to health.
Predominant spiritual beliefs in the community that may influence health.
Availability of spiritual resources within or near the community (churches/chapels, synagogues, chaplains, Bible studies, sacraments, self-help groups, support groups, etc.).
Do the community members value health promotion measures? What is the evidence that they do or do not (e.g., involvement in education, fundrai.
Comment Commentonat least 3 Classmates’Posts (approximately 150.docxdrandy1
Comment
Commentonat least 3 Classmates’Posts (approximately 150 -300 words each)§
- comment must address the R2R prompt and your classmate’s response substantively; if you agree or disagree, provide reasoning and rational evidence from the readings to support your position
- build on the ideas of what your classmate has written and dig deeper into the ideas
- support your views through research you have read or through your personal and/or professional experiences§demonstrate a logical progression of ideas
- comments need to be thoughtful and substantive; not gratuitous comments like “this was a good post” or simply that “you agree”. Simply congratulating the writer on their astute insights is insufficient.
- cite the readings in your response by using proper APA Style format and conventions.
classmate 1
Pragmatism is defined as a philosophical approach in which experience is the fundamental concept. Radu explains that in pragmatism, each experience is based on the interaction between subject and object, between self and its world and represents only the result of the integration of human beings into the environment (Radu, 2011). All in all, pragmatism promotes activity based learning. Pragmatism relates to Dewey’s work in many ways. The most significant being its rejection of traditional learning, and its emphasis on solving problems in a sensible way that suits conditions that really exist rather than obeying fixed theories, ideas, or rules (Cambridge, 2016).
Progressivism is a philosophical concept belonging to ‘new education’, is ‘a Copernican revolution’ in pedagogy, promoting ‘a child-centered school’ (Radu, 2011). Radu states that Dewey’s pedagogic view is not based on his philosophical concept, but al on the social, economic and cultural realities of American society (pg. 87). Progressivism is featured around the learning capacity continuing into adulthood; Dewey called this “permanent education”. Learning is done by doing; this is because Dewey believed authentic knowledge is achieved only through direct experience. Although Dewey though some target methods were necessary when teaching, he did not believe in teachers being forced to stick to routines (Radu 2011). This idea leads to the problem-problem solving method which in short states that in order to solve problem, an individual must: define the problem, analyze the problem, determine possible solutions, propose solutions, evaluate and select a solution, and determine strategies to implement solution. The progressive theory encourages learning through discovery, this allows the learner to acquire knowledge through interest, rather than effort.
Ragu also states that there are reactions against Dewey’s progressive education. Perennialism says that permanence is the fundamental feature of the world; not change. School is intended to promote the permanent values of the past and present. Essentialists believed the main purpose of school was to prepare th.
Communication permeates all that we do, no matter who we are. In thi.docxdrandy1
Communication permeates all that we do, no matter who we are. In this discussion forum, we are going to explore this concept by looking at the changes in how we communicate through written and spoken formats with the introduction of new technologies.
Begin by reading the following:
Mobile telephony and democracy in Ghana: Interrogating the changing ecology of citizen engagement and political communication
.
Towards the Egyptian Revolution: Activists' Perceptions of Social Media for Mobilization
Peacebuilding in a Networked World
Clay Shirky interview:
Social Media Acts as Catalyst for Policy Change
Technologies enable people to connect by shared beliefs and social movements, rather than by just national or ethnic identification. There is no longer a location-bound or time element in global communication. We seek out those who share our beliefs, and this allows us to harness the power of ideas across borders. Conduct some research into the power of social media to effect political change and consider the following questions, sharing one recent example:
Has the advent of “technology assisted communication” contributed to an expansion of the democratic process? If so, in what way(s)? Is this approach to democratic interaction workable for the future or just a unique event?
How has social media contributed to political change? Examine this question using the example from your research.
.
Combating BriberyIn May 2011, the Commission for Eradication of .docxdrandy1
Combating Bribery
In May 2011, the Commission for Eradication of Corruption in Indonesia (K.P.K.) and the Organization for Economic Cooperation and Development (O.E.C.D.) met to devise a treaty against international bribery practices. First, read the Conference Conclusions document. Then discuss how the twelve conclusions from the conference will help the international anti-corruption community forge ahead in fighting foreign bribery with a mutual understanding of how to achieve its goals. Respond to at least two of your classmates’ posts.
Shell’s Values
Review the Shell: Our Values page on Shell’s corporate website. To what major issues does Shell highlight its commitment? Do you think the organization’s statements are useful as a guide to ethical and socially responsible decision making? Why or why not? Respond to at least two of your classmates’ posts.
.
Comment using your own words but please provide at least one referen.docxdrandy1
Comment using your own words but please provide at least one reference for each comment.
Do a half page for discussion #1, half page for discussion #2, half page for discussion #3 and half page for discussion #4 for a total of two pages.
Provide the comment for each discussion separate.
.
Communicating and Collaborating Family InvolvementIn this uni.docxdrandy1
Communicating and Collaborating: Family Involvement
In this unit you will read about the importance of developing partnerships with families in the preschool classroom. You will learn about rights and responsibilities of parents of children with disabilities as well as how to act as an advocate for children with special needs. You will discuss challenges of being sensitive and responsive to children and families from a variety of cultural backgrounds. You will also explore strategies to help empower a family of a child with special needs
.
Community Health Assessment and Health Promotion-1000 words-due .docxdrandy1
Community Health Assessment and Health Promotion-1000 words-due 9/23/2020
In 1000 words respond to each question below. Use the textbook and source to support statements
1. Elaborate on the effectiveness of children immunization program as a primary community health diseases prevention method within the Peoria Illinois community.
2. Identify at least 2 immunization health promotion program and initiatives within the Peoria Illinois community.
3. What are current population trends and attitudes regarding immunization?
4. Elaborate on the obesity epidemic and its public health impact.
5. Speak on at least two programs or initiative/programs that community and public health officials have taken to reduce the prevalence of obesity within the Peoria Illinois community.
Cite all source with credible scholarly articles. Use at least 3 reference. Sources must be 5 years old or less. Use APA format 7th edition. Use statistical data to support each question.
.
COMMUNITY HEALTH ASSESSMENTWINSHIELD SURVEYGUIDELINES1. C.docxdrandy1
COMMUNITY HEALTH ASSESSMENT/WINSHIELD SURVEY
GUIDELINES:
1. Community description.
2. Community health status (can be obtain from the department of health).
3. The role of the community as a client.
4. Healthy people 2020, leading health indictors in your community.
5. Conclusion.
Also, you must present a table as an appendix with the following topics and description;
Housing
Transportation
Race and ethnicity
Open space
Service centers
Religion and politics
Requirements:
APA style ( includes references, no less than 3 references not older than 2016 and intent citation).
.
Community Concerns Please respond to the followingIn your.docxdrandy1
Community Concerns"
Please respond to the following:
In your opinion, what are the most pressing and significant concerns facing communities today? Why do you think so? Respond to at least one of your classmates. How would a business' community relations department address the concern that your classmate has posted? Support your reasoning with at least one quality reference.
.
Community Engagement InstructionsPart I PlanStudents wi.docxdrandy1
Community Engagement Instructions
Part I: Plan
Students will submit the Community Engagement Plan Form that includes a paragraph informing the instructor of the plan for the required 10-hour volunteer service in a community setting, including the supervising organization’s name and other pertinent information.
Submit Part I: The Plan for Community Engagement by 11:59 p.m. (ET) on Sunday of Module/Week 3.
.
Community Career DevelopmentRead the following case study an.docxdrandy1
Community Career Development
Read the following case study and in 700- to 1050-words (2-3 pages) answer the questions posed after the case study. Use headings to separate the responses to each question. Use at least two (2) resources.
Frank is a 25-year-old veteran who has served two tours of duty in Iraq. While there, he lost his right arm while removing wounded soldiers from the combat zone. He is suffering from posttraumatic stress disorder. His military specialty is artillery maintenance specialist. Now he faces the need to get housing and a civilian job that provides enough income to support himself, his wife, and their two children. While he has been away, his wife and children have lived with her parents, but now Frank and his wife would like to have their own home.
Frank has a high school diploma but has not pursued any education beyond that. Before entering the military, he drove a florist delivery truck. Linda, his wife, has completed an associate degree in paralegal studies at the local community college while Frank has been away. She is willing to work if they could find a way to acquire good child care services.
Questions:
What kinds of next steps would you investigate with Frank as you work with him on an action plan?
To what agencies and resources might you refer Frank?
What kinds of support services does this family need?
.
Community College Initiative Paper 5-7 pages. Must be SUBMITTED BY 2.docxdrandy1
Community College Initiative Paper 5-7 pages. Must be SUBMITTED BY 2pm Central Time 06/15/20.
Students can choose one of six topics to present a paper thoroughly explaining the assigned initiative, stakeholders involved, expected results, attached legislation, if any, and information on supporters and critics.
The six topics are: community college academic achievement gap, student equity, 4 year transfer, workforce development, online education, and
GUIDED PATHWAYS (Highlighted)
. • Paper should be in APA format, typed, double spaced. • Paper must be written from third person point of view.
.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
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
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
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.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
2. 2.
3.
Drawbacks:
1.
2.
3.
Name of punishment: RESTITUTION
Description:
Purpose(s) served:
Advantages:
1.
2.
3.
Drawbacks:
1.
2.
3.
Name of punishment: HOUSE ARREST
Description:
Purpose(s) served:
Advantages:
1.
2.
3.
Drawbacks:
3. 1.
2.
3.
REFERENCES
1
Day 08 ActivityFisher & HughesSeptember 21, 2018Study
A study was conducted to determine the effects of alcohol on
human reaction times. Fifty-seven adult individuals within two-
age groups were recruited for this study and were randomly
allocated into one of three alcohol treatment groups – a control
where the subjects remain sober during the entire study, a
moderate group were the subject is supplied alcohol but is
limited in such a way that their blood alcohol content (BAC)
remains under the legal limit to drive (BAC of 0.08) and a
group that received a high amount of alcohol to which their
BAC may exceed the legal limit for driving. Each subject was
trained on a video game system and their reaction time (in
milliseconds) to a visual stimulus was recorded at 7 time points
30 minutes apart (labeled T0=0, T1=30, T2=60 and so on). At
time point T0, all subjects were sober and those in one of the
alcohol consumption groups began drinking after the first
measured reaction time (controlled within the specifications
outlined). The researcher is interested in determining the
influence alcohol and age (namely, is reaction time different for
those in the 20s versus 30s) has on reaction times.
The task for today is to do a complete analysis for this study
and dig into the effects of alcohol, age and time have on
reaction times.Data input and wrangling
First read in the data:alcohol <- read.csv("alcoholReaction.csv")
head(alcohol)## Subject Age Alcohol T0 T1 T2 T3
T4 T5 T6
## 1 1 24 Control 255.3 254.8 256.4 255.1 257.0 256.1
257.0
## 2 2 34 Control 250.1 249.2 249.0 248.0 248.0 248.9
248.1
4. ## 3 3 31 Control 248.2 247.1 246.9 246.7 246.0 246.0
247.0
## 4 4 24 Control 253.9 253.8 254.9 254.1 253.2 254.1
255.0
## 5 5 38 Control 250.0 251.0 250.0 249.9 248.8 249.1
249.9
## 6 6 38 Control 246.0 248.0 247.0 248.1 248.1 246.9
244.0
Note, the Age variable is recorded as an actual age in years, not
the category of 20s or 30s like we want – we need to
dichotomize this variable. Also note the data is in wide format –
the reaction times (the response variables) are spread over
multiple columns. We need a way to gather these columns into a
single column. So we need to do some data processing.
First consider the below code:head(alcohol %>%
mutate(Age = case_when(Age<31 ~ "20s",
Age %in% 31:40 ~ "30s")))## Subject Age
Alcohol T0 T1 T2 T3 T4 T5 T6
## 1 1 20s Control 255.3 254.8 256.4 255.1 257.0 256.1
257.0
## 2 2 30s Control 250.1 249.2 249.0 248.0 248.0 248.9
248.1
## 3 3 30s Control 248.2 247.1 246.9 246.7 246.0 246.0
247.0
## 4 4 20s Control 253.9 253.8 254.9 254.1 253.2 254.1
255.0
## 5 5 30s Control 250.0 251.0 250.0 249.9 248.8 249.1
249.9
## 6 6 30s Control 246.0 248.0 247.0 248.1 248.1 246.9
244.0
case_when is essentially a piece-wise comparison. When Age is
less than 31, you overwrite Age variable with “20s”. If the Age
is greater than 30, you replace it with “30s”. In this example we
used both a < comparison and the %in% statement we’ve seen
before just to show multiple functionality. Also note we include
30 in the 20s group and 40 in the 30s group so they are each of
5. size 10.alcohol <- alcohol %>%
mutate(Age = case_when(Age<31 ~ "20s",
Age %in% 31:40 ~ "30s") )
So the Age variable has been categorized. Now we need to
convert the data from wide to tall format. We do this with the
gather() function included in tidyverse.alcohol.tall <- alcohol
%>%
gather(key=Time, value=Reaction, c(T0, T1, T2, T3, T4, T5,
T6))
A blurb about gather There are essentially three inputs into the
gather() functions. Firstkey - Essentially provides the name of
the new variable we are going to create that consist of the
column namesvalue - Is the name for the new variable that will
house the values originally stored in the columns of interestThe
final part is a list of all the columns we want to gather, in this
case, T0, T1, T2, T3, T4, T5 and T6.head(alcohol.tall, n=10)##
Subject Age Alcohol Time Reaction
## 1 1 20s Control T0 255.3
## 2 2 30s Control T0 250.1
## 3 3 30s Control T0 248.2
## 4 4 20s Control T0 253.9
## 5 5 30s Control T0 250.0
## 6 6 30s Control T0 246.0
## 7 7 20s Control T0 248.8
## 8 8 30s Control T0 245.9
## 9 9 20s Control T0 246.9
## 10 10 30s Control T0 249.1
You will now note the data is a in a tall format, which is good
for analysis.
Lastly, so R doesn’t try and treat it as a number, we tell it that
the Subject variable is a factor or categorical variable. I also put
the Alcohol variables in the order we think…alcohol.tall <-
alcohol.tall %>%
mutate(Subject = as.factor(Subject),
Alcohol = factor(Alcohol, levels=c("Control",
"Moderate", "High")))Exploratory Data Analysis
6. There are 2 categories for age, 3 categories for alcohol use and
then 7 time points to consider. Essentially (2times 3times 7 =
42) combinations to consider. Rather than look numerically we
will consider things graphically.
First we consider a plot of the Reaction times in Time based on
Alcohol treatment with Age determining the
linetype.ggplot(alcohol.tall) +
geom_line(aes(x=Time, y=Reaction, group=Subject,
color=Alcohol, linetype=Age))
Not only is this plot noisy, it is hard to determine anything.
Let’s facet based on Ageggplot(alcohol.tall) +
geom_line(aes(x=Time, y=Reaction, group=Subject,
color=Alcohol)) +
facet_wrap(~Age)
This second plot is improved but still quite noisy. Let’s plot
average profiles rather than the raw data.ggplot(alcohol.tall,
aes(x=Time, y=Reaction, group=Alcohol, color=Alcohol)) +
stat_summary(fun.y=mean, geom="line") +
facet_wrap(~Age)
These average profiles are fairly telling and maybe even a little
surprising. Overall you see the High aclohol group (blue line)
shows an increase in reaction time over the time of the study.
The Control group shows a near decrease in the 30s group but
also note the spead is only about a half a unit decrease.Model
fitting and analysis
We fit a 2 factor repeated measure model and look at the
output.fit <- aov(Reaction ~ Age*Alcohol*Time +
Error(Subject/Time), data=alcohol.tall)
summary(fit)##
## Error: Subject
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 18 17.72 0.254 0.616
## Alcohol 2 143 71.47 1.026 0.366
7. ## Age:Alcohol 2 93 46.31 0.665 0.519
## Residuals 51 3553 69.66
##
## Error: Subject:Time
## Df Sum Sq Mean Sq F value Pr(>F)
## Time 6 50.3 8.386 6.929 6.45e-07 ***
## Age:Time 6 10.3 1.714 1.416 0.20786
## Alcohol:Time 12 40.0 3.330 2.752 0.00145 **
## Age:Alcohol:Time 12 13.8 1.150 0.950 0.49702
## Residuals 306 370.4 1.210
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
First we look at the most complicated interaction term, in this
case Age:Alcohol:Time and it is NOT significant. So we follow
up by considering the two-way interaction terms. We see
Age:Alcohol and Age:Time are not significant but
Alcohol:Time is. There is an interaction between Alcohol group
and Time. Given the interactions involving Age are not
significnat, we can also consider the Age main effect, but see it
is also insignificant (F-stat 0.252 on 1 and 51 degrees of
freedom, (p)-value=0.616). Age appears to have no influence
on the reaction times. We follow up with conditional multiple
comparisons.Multiple Comparison Follow ups
Note: We have two levels of control in this study, there is an
explicit Control group and at time point T0 no subjects had been
given a treatment, so it also operates as a control. Dunnett’s
method for multiple comparison is most appropriate (see chapter
2.7 of the text).
We see that Alcohol and Time both matter, but perhaps in
different ways. We consider both conditional comparisons. First
we run the emmeans() codemc.alc <- emmeans(fit, ~ Alcohol |
Time)## Warning in emm_basis.aovlist(object, ...): Some
predictors are correlated with the intercept - results are biased.
## May help to re-fit with different contrasts, e.g. 'contr.sum'##
NOTE: Results may be misleading due to involvement in
interactionsmc.time <- emmeans(fit, ~ Time | Alcohol)##
8. Warning in emm_basis.aovlist(object, ...): Some predictors are
correlated with the intercept - results are biased.
## May help to re-fit with different contrasts, e.g. 'contr.sum'##
NOTE: Results may be misleading due to involvement in
interactions
First we consider the effects of alcohol conditioning at different
time points.contrast(mc.alc, "trt.vs.ctrl", ref=1)## Time = T0:
## contrast estimate SE df t.ratio p.value
## Moderate - Control 1.077143 1.0774800 51.00 1.000
0.5097
## High - Control 1.400260 0.9861516 51.00 1.420 0.2799
##
## Time = T1:
## contrast estimate SE df t.ratio p.value
## Moderate - Control 1.753810 1.2014014 78.06 1.460
0.2590
## High - Control 1.816169 1.0995693 78.06 1.652 0.1841
##
## Time = T2:
## contrast estimate SE df t.ratio p.value
## Moderate - Control 1.947143 1.2014014 78.06 1.621
0.1950
## High - Control 2.023896 1.0995693 78.06 1.841 0.1274
##
## Time = T3:
## contrast estimate SE df t.ratio p.value
## Moderate - Control 2.133810 1.2014014 78.06 1.776
0.1450
## High - Control 2.613442 1.0995693 78.06 2.377 0.0380
##
## Time = T4:
## contrast estimate SE df t.ratio p.value
## Moderate - Control 2.405476 1.2014014 78.06 2.002
0.0907
## High - Control 2.814351 1.0995693 78.06 2.560 0.0239
##
9. ## Time = T5:
## contrast estimate SE df t.ratio p.value
## Moderate - Control 2.365476 1.2014014 78.06 1.969
0.0975
## High - Control 3.206623 1.0995693 78.06 2.916 0.0090
##
## Time = T6:
## contrast estimate SE df t.ratio p.value
## Moderate - Control 2.487143 1.2014014 78.06 2.070
0.0781
## High - Control 3.517532 1.0995693 78.06 3.199 0.0039
##
## Results are averaged over the levels of: Age
## P value adjustment: dunnettx method for 2
testsplot(contrast(mc.alc, "trt.vs.ctrl", ref=1))
First note, that in all seven comparisons, the Moderate group is
never different than the Control group (this is true for all time,
smallest adjusted (p)-value is 0.0781). Thus, the profiles of
the Moderate group and the Control group are statistically the
same.
We can see that in the early time points, there was no difference
between the treatment groups receiving alcohol and those not
but as time progressed the “High” alcohol group had higher
reaction times than the control (starting at T3, it always
significant with adjusted (p)-value of 0.0380).
Next we compare the effects of time conditioning on the alcohol
group.contrast(mc.time, "trt.vs.ctrl", ref=1)## Alcohol =
Control:
## contrast estimate SE df t.ratio p.value
## T1 - T0 0.1700000 0.3478929 306 0.489 0.9675
## T2 - T0 0.1750000 0.3478929 306 0.503 0.9647
## T3 - T0 0.2600000 0.3478929 306 0.747 0.8938
## T4 - T0 0.0700000 0.3478929 306 0.201 0.9976
## T5 - T0 -0.1750000 0.3478929 306 -0.503 0.9647
## T6 - T0 -0.1600000 0.3478929 306 -0.460 0.9727
10. ##
## Alcohol = Moderate:
## contrast estimate SE df t.ratio p.value
## T1 - T0 0.8466667 0.4017122 306 2.108 0.1603
## T2 - T0 1.0450000 0.4017122 306 2.601 0.0492
## T3 - T0 1.3166667 0.4017122 306 3.278 0.0065
## T4 - T0 1.3983333 0.4017122 306 3.481 0.0032
## T5 - T0 1.1133333 0.4017122 306 2.771 0.0309
## T6 - T0 1.2500000 0.4017122 306 3.112 0.0111
##
## Alcohol = High:
## contrast estimate SE df t.ratio p.value
## T1 - T0 0.5859091 0.3398943 306 1.724 0.3302
## T2 - T0 0.7986364 0.3398943 306 2.350 0.0929
## T3 - T0 1.4731818 0.3398943 306 4.334 0.0001
## T4 - T0 1.4840909 0.3398943 306 4.366 0.0001
## T5 - T0 1.6313636 0.3398943 306 4.800 <.0001
## T6 - T0 1.9572727 0.3398943 306 5.758 <.0001
##
## Results are averaged over the levels of: Age
## P value adjustment: dunnettx method for 6
testsplot(contrast(mc.time, "trt.vs.ctrl", ref=1))
We see that the Control group never deviates from the control
time point (T0). This should not be surprising given they
remained sober for the entire study. In both of the other
treatments we see the influence of Time (and thus alcohol
consumption) on reaction times.
Even though the profile of the Moderate group was not
significantly different than the Control group, they did
experience an increase in reaction times with the consumption
of alcohol (just not enough to deviate overall from the Control
group). We see that the High consumption did deviate from the
Control group sometime around time point T3 (90
minutes).Conclusions
We established above that the key finding is that those with a
11. high dosage of alcohol had a longer reaction time compared to
the the control group as time progressed. We also find that
those receiving a moderate amount of alcohol performed
similarly to the control group. We close by building a profile
plot to summarize the findings (remember, Age was not
important).
First we plot the profiles of the three alcohol treatments
summarizing over all ages.alcohol.summary <- alcohol.tall
%>%
group_by(Alcohol, Time) %>%
summarize(Mean=mean(Reaction),
SE= sd(Reaction)/sqrt(n()))
ggplot(alcohol.summary, aes(x=Time, y=Mean, color=Alcohol))
+
geom_errorbar(aes(ymin=Mean-SE, ymax=Mean+SE),
width=0.1, position=position_dodge(0.3)) +
geom_line(aes(group=Alcohol), position=position_dodge(0.3))
+
geom_point(position=position_dodge(0.3))
Note this plot is a bit misleading since we have plotted the
moderate group even though it is statistically similar to the
control group (note the SE bars overlap for all time points for
the moderate and contrl groups). To link the control and
moderate groups, we have to do a bit more data processing. In
the below code we recast the Alcohol variable to only two
groups.alcohol.summary2 <- alcohol.tall %>%
mutate(Alcohol = case_when(Alcohol=="High" ~ "Legally
Drunk",
TRUE ~ "Legally Sober")) %>% # `TRUE
~` is everything else
group_by(Alcohol, Time) %>%
summarize(Mean=mean(Reaction),
SE= sd(Reaction)/sqrt(n()))
The TRUE ~ "Legally Sober" line essentially tells R that in any
other case (TRUE is always True) to mark it as Legally Sober.
12. In the first line of the case_when statement we use the ==
notation to compare for equality.
Now we make an overall plot summarizing the findings of our
study. To demonstrate the level of sophistication we can include
in a plot, I do quite a bit with axes, labeling and color choices.
Note this is sort of thing covered in detail in STA404. Here we
demonstrate the functionality.ggplot(alcohol.summary2,
aes(x=Time, y=Mean, color=Alcohol)) +
geom_errorbar(aes(ymin=Mean-SE, ymax=Mean+SE),
width=0.1, position=position_dodge(0.3)) +
geom_line(aes(group=Alcohol), position=position_dodge(0.3))
+
geom_point(position=position_dodge(0.3)) +
scale_x_discrete(name="Minutes since start of study",
labels=c("0","30","60","90", "120", "150", "180")) +
scale_color_manual(name="Alcohol level",
values=c("darkgreen", "cyan")) +
labs(y="Mean Reaction Time (ms)") +
theme_bw() +
ggtitle("Alcohol effects on Reaction time to Visual Stimulus")
+
theme(legend.position=c(0.125,0.85)) # 0.125 (ie 12.5%) from
the left edge and 0.85 from the bottom edge
