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[Solved] CO Data 3

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– ## COdata3.docx – credit_Dataset.arff.txt **## COdata3.docx** **DECISION TREES for Risk Assessment** One of the great advantages of decision trees is their _interpretability_. The rules learnt for classification are easy for a person to follow, unlike the opaque "black box" of many other methods,…

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– ## COdata3.docx

– credit_Dataset.arff.txt

**## COdata3.docx**

**DECISION TREES for Risk Assessment**

One of the great advantages of decision trees is their
_interpretability_. The rules learnt for classification are easy for a person to follow, unlike the opaque "black box" of many other methods, such as neural networks. We demonstrate the utility of this using a
[German credit](http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29) data set. You can read
[a description](http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29) of this dataset at the UCI site. The task is to predict whether a loan approval is good or bad credit risk based on 20 attributes. We've simplified the data set somewhat, particularly making attribute names and values more meaningful.

1. Download the credit_Dataset.arff dataset and load it to Weka.

2. (5 Points) When presented with a dataset, it is usually a good idea to visualise it first. Go to the
_Visualise_ tab. Click on any of the scatter plots to open a new window which shows the scatter plot for two selected attributes. Try visualising a scatter plot of
_age_ and
_duration_. Do you notice anything unusual? You can click on any data point to display all it's values.

3. (5 Points) In the previous point you should have found a data point, which seems to be corrupted, as some of its values are nonsensical. Even a single point like this can significantly affect the performance of a classifier. How do you think it would affect Decision trees? A good way to check this is to test the performance of each classifier before and after removing this datapoint.

4. (10 Points) To remove this instance from the dataset we will use a filter. We want to remove all instances, where the age of an applicant is lower than 0 years, as this suggests that the instance is corrupted. In the
_Preprocess_ tab click on
_Choose_ in the Filter pane. Select
_filters > unsupervised > instance > RemoveWithValues_. Click on the text of this filter to change the parameters. Set the attribute index to 13 (Age) and set the split point at 0. Click
_Ok_ to set the parameters and
_Apply_ to apply the filter to the data. Visualise the data again to verify that the invalid data point was removed.

5. (20 Points) On the
_Classify_ tab, select the
_Percentage split_ test option and change its value to 90%. This way, we will train the classifiers using 90% of the training data and evaluate their performance on the remaining 10%. First, train a decision tree classifier with default options. Select
_classifiers > trees > J48_ and click
_Start_.
_J48_ is the Weka implementation of the
_C4.5_ algorithm, which uses the normalized information gain criterion to build a decision tree for classification.

6. (20 Points) After training the classifier, the full decision tree is output for your perusal; you may need to scroll up for this. The tree may also be viewed in graphical form by right-clicking in the
_Result list_ and selecting
_Visualize tree_; unfortunately this format is very cluttered for large trees. Such a tree accentuates one of the strengths of decision tree algorithms: they produce classifiers which are understandable to humans. This can be an important asset in real life applications (people are seldom prepared to do what a computer program tells them if there is no clear explanation). Observe the output of the classifier and try to answer the following questions:

· How would you assess the performance of the classifier? Is the Percentage of
_Correctly Classified Instances_ a sufficient measure in this case? Why?
_Hint:_ check the number of good and bad cases in the test sample, using the confusion matrix. Each column of the matrix represents the instances in a predicted class, while each row represents the instances in an actual class. For example let us define an experiment from P positive instances and N negative instances. The four outcomes can be formulated in a 2 by 2 contingency table or confusion matrix. One benefit of a confusion matrix is that it is easy to see if the system is confusing two classes (i.e. commonly mislabeling one as another).

· Looking at the decision tree itself, are the rules it applies sensible? Are there any branches which appear absurd? At what depth of the tree? What does this suggest?

_Hint:_ Check the rules applied after following the paths: (a)
_CheckingAccount = <0, Foreign = yes, Duration >11, Job = skilled, OtherDebtors = none, Duration <= 30_ and (b)
_CheckingAccount = <0, Foreign = yes, Duration >11, Job = unskilled._

· How does the decision tree deal with classification in the case where there are zero instances in the training set corresponding to that particular path in the tree (e.g. those leaf nodes that have (0:0))?

7. (20 Points) Now, explore the effect of the
_confidenceFactor_ option. You can find this by clicking on the Classifer name (to the right of the
_Choose_ button on the Classify tab). On the
_Classifier options_ window, click on the
_More_ button to find out what the confidence factor controls. Try the values 0.1, 0.2, 0.3 and 0.5. What is the performance of the classifier at each case? Did you expect this given your observations in the previous questions? Why do you think this happens?

8. (20 Points) Suppose that it is worse to classify a customer as good when they are bad, than it is to classify a customer as bad when they are good. Which value would you pick for the confidence factor? Which performance measure would you base your decision on?

9. (Bonus: 20 Points)Finally we will create a
_random decision forest_ and compare the performance of this classifier to that of the decision tree and the decision stump. The random decision forest is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. Again set the test option
_Percentage split_ to 90%. Select
_classifiers > trees > RandomForest_ and hit
_Start_. Again, observe the output. How high can you get the performance of the classifier by changing the number of trees (numTrees) parameter? How does the random decision forest compare performance wise to the decision tree and decision stump?

Deliverable:

· Your report including the screenshots of your implementation for each section and the results.

**credit_Dataset.arff.txt**

This file is too large to display. [View in new window](https://www.sweetstudy.com/files/credit-dataset-arff-txt)

**credit_Dataset.arff.txt**

This file is too large to display. [View in new window](https://www.sweetstudy.com/files/credit-dataset-arff-txt)

**## COdata3.docx**

**DECISION TREES for Risk Assessment**

One of the great advantages of decision trees is their
_interpretability_. The rules learnt for classification are easy for a person to follow, unlike the opaque "black box" of many other methods, such as neural networks. We demonstrate the utility of this using a
[German credit](http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29) data set. You can read
[a description](http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29) of this dataset at the UCI site. The task is to predict whether a loan approval is good or bad credit risk based on 20 attributes. We've simplified the data set somewhat, particularly making attribute names and values more meaningful.

1. Download the credit_Dataset.arff dataset and load it to Weka.

2. (5 Points) When presented with a dataset, it is usually a good idea to visualise it first. Go to the
_Visualise_ tab. Click on any of the scatter plots to open a new window which shows the scatter plot for two selected attributes. Try visualising a scatter plot of
_age_ and
_duration_. Do you notice anything unusual? You can click on any data point to display all it's values.

3. (5 Points) In the previous point you should have found a data point, which seems to be corrupted, as some of its values are nonsensical. Even a single point like this can significantly affect the performance of a classifier. How do you think it would affect Decision trees? A good way to check this is to test the performance of each classifier before and after removing this datapoint.

4. (10 Points) To remove this instance from the dataset we will use a filter. We want to remove all instances, where the age of an applicant is lower than 0 years, as this suggests that the instance is corrupted. In the
_Preprocess_ tab click on
_Choose_ in the Filter pane. Select
_filters > unsupervised > instance > RemoveWithValues_. Click on the text of this filter to change the parameters. Set the attribute index to 13 (Age) and set the split point at 0. Click
_Ok_ to set the parameters and
_Apply_ to apply the filter to the data. Visualise the data again to verify that the invalid data point was removed.

5. (20 Points) On the
_Classify_ tab, select the
_Percentage split_ test option and change its value to 90%. This way, we will train the classifiers using 90% of the training data and evaluate their performance on the remaining 10%. First, train a decision tree classifier with default options. Select
_classifiers > trees > J48_ and click
_Start_.
_J48_ is the Weka implementation of the
_C4.5_ algorithm, which uses the normalized information gain criterion to build a decision tree for classification.

6. (20 Points) After training the classifier, the full decision tree is output for your perusal; you may need to scroll up for this. The tree may also be viewed in graphical form by right-clicking in the
_Result list_ and selecting
_Visualize tree_; unfortunately this format is very cluttered for large trees. Such a tree accentuates one of the strengths of decision tree algorithms: they produce classifiers which are understandable to humans. This can be an important asset in real life applications (people are seldom prepared to do what a computer program tells them if there is no clear explanation). Observe the output of the classifier and try to answer the following questions:

· How would you assess the performance of the classifier? Is the Percentage of
_Correctly Classified Instances_ a sufficient measure in this case? Why?
_Hint:_ check the number of good and bad cases in the test sample, using the confusion matrix. Each column of the matrix represents the instances in a predicted class, while each row represents the instances in an actual class. For example let us define an experiment from P positive instances and N negative instances. The four outcomes can be formulated in a 2 by 2 contingency table or confusion matrix. One benefit of a confusion matrix is that it is easy to see if the system is confusing two classes (i.e. commonly mislabeling one as another).

· Looking at the decision tree itself, are the rules it applies sensible? Are there any branches which appear absurd? At what depth of the tree? What does this suggest?

_Hint:_ Check the rules applied after following the paths: (a)
_CheckingAccount = <0, Foreign = yes, Duration >11, Job = skilled, OtherDebtors = none, Duration <= 30_ and (b)
_CheckingAccount = <0, Foreign = yes, Duration >11, Job = unskilled._

· How does the decision tree deal with classification in the case where there are zero instances in the training set corresponding to that particular path in the tree (e.g. those leaf nodes that have (0:0))?

7. (20 Points) Now, explore the effect of the
_confidenceFactor_ option. You can find this by clicking on the Classifer name (to the right of the
_Choose_ button on the Classify tab). On the
_Classifier options_ window, click on the
_More_ button to find out what the confidence factor controls. Try the values 0.1, 0.2, 0.3 and 0.5. What is the performance of the classifier at each case? Did you expect this given your observations in the previous questions? Why do you think this happens?

8. (20 Points) Suppose that it is worse to classify a customer as good when they are bad, than it is to classify a customer as bad when they are good. Which value would you pick for the confidence factor? Which performance measure would you base your decision on?

9. (Bonus: 20 Points)Finally we will create a
_random decision forest_ and compare the performance of this classifier to that of the decision tree and the decision stump. The random decision forest is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. Again set the test option
_Percentage split_ to 90%. Select
_classifiers > trees > RandomForest_ and hit
_Start_. Again, observe the output. How high can you get the performance of the classifier by changing the number of trees (numTrees) parameter? How does the random decision forest compare performance wise to the decision tree and decision stump?

Deliverable:

· Your report including the screenshots of your implementation for each section and the results.

**credit_Dataset.arff.txt**

This file is too large to display. [View in new window](https://www.sweetstudy.com/files/credit-dataset-arff-txt)

**## COdata3.docx**

**DECISION TREES for Risk Assessment**

One of the great advantages of decision trees is their
_interpretability_. The rules learnt for classification are easy for a person to follow, unlike the opaque "black box" of many other methods, such as neural networks. We demonstrate the utility of this using a
[German credit](http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29) data set. You can read
[a description](http://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29) of this dataset at the UCI site. The task is to predict whether a loan approval is good or bad credit risk based on 20 attributes. We've simplified the data set somewhat, particularly making attribute names and values more meaningful.

1. Download the credit_Dataset.arff dataset and load it to Weka.

2. (5 Points) When presented with a dataset, it is usually a good idea to visualise it first. Go to the
_Visualise_ tab. Click on any of the scatter plots to open a new window which shows the scatter plot for two selected attributes. Try visualising a scatter plot of
_age_ and
_duration_. Do you notice anything unusual? You can click on any data point to display all it's values.

3. (5 Points) In the previous point you should have found a data point, which seems to be corrupted, as some of its values are nonsensical. Even a single point like this can significantly affect the performance of a classifier. How do you think it would affect Decision trees? A good way to check this is to test the performance of each classifier before and after removing this datapoint.

4. (10 Points) To remove this instance from the dataset we will use a filter. We want to remove all instances, where the age of an applicant is lower than 0 years, as this suggests that the instance is corrupted. In the
_Preprocess_ tab click on
_Choose_ in the Filter pane. Select
_filters > unsupervised > instance > RemoveWithValues_. Click on the text of this filter to change the parameters. Set the attribute index to 13 (Age) and set the split point at 0. Click
_Ok_ to set the parameters and
_Apply_ to apply the filter to the data. Visualise the data again to verify that the invalid data point was removed.

5. (20 Points) On the
_Classify_ tab, select the
_Percentage split_ test option and change its value to 90%. This way, we will train the classifiers using 90% of the training data and evaluate their performance on the remaining 10%. First, train a decision tree classifier with default options. Select
_classifiers > trees > J48_ and click
_Start_.
_J48_ is the Weka implementation of the
_C4.5_ algorithm, which uses the normalized information gain criterion to build a decision tree for classification.

6. (20 Points) After training the classifier, the full decision tree is output for your perusal; you may need to scroll up for this. The tree may also be viewed in graphical form by right-clicking in the
_Result list_ and selecting
_Visualize tree_; unfortunately this format is very cluttered for large trees. Such a tree accentuates one of the strengths of decision tree algorithms: they produce classifiers which are understandable to humans. This can be an important asset in real life applications (people are seldom prepared to do what a computer program tells them if there is no clear explanation). Observe the output of the classifier and try to answer the following questions:

· How would you assess the performance of the classifier? Is the Percentage of
_Correctly Classified Instances_ a sufficient measure in this case? Why?
_Hint:_ check the number of good and bad cases in the test sample, using the confusion matrix. Each column of the matrix represents the instances in a predicted class, while each row represents the instances in an actual class. For example let us define an experiment from P positive instances and N negative instances. The four outcomes can be formulated in a 2 by 2 contingency table or confusion matrix. One benefit of a confusion matrix is that it is easy to see if the system is confusing two classes (i.e. commonly mislabeling one as another).

· Looking at the decision tree itself, are the rules it applies sensible? Are there any branches which appear absurd? At what depth of the tree? What does this suggest?

_Hint:_ Check the rules applied after following the paths: (a)
_CheckingAccount = <0, Foreign = yes, Duration >11, Job = skilled, OtherDebtors = none, Duration <= 30_ and (b)
_CheckingAccount = <0, Foreign = yes, Duration >11, Job = unskilled._

· How does the decision tree deal with classification in the case where there are zero instances in the training set corresponding to that particular path in the tree (e.g. those leaf nodes that have (0:0))?

7. (20 Points) Now, explore the effect of the
_confidenceFactor_ option. You can find this by clicking on the Classifer name (to the right of the
_Choose_ button on the Classify tab). On the
_Classifier options_ window, click on the
_More_ button to find out what the confidence factor controls. Try the values 0.1, 0.2, 0.3 and 0.5. What is the performance of the classifier at each case? Did you expect this given your observations in the previous questions? Why do you think this happens?

8. (20 Points) Suppose that it is worse to classify a customer as good when they are bad, than it is to classify a customer as bad when they are good. Which value would you pick for the confidence factor? Which performance measure would you base your decision on?

9. (Bonus: 20 Points)Finally we will create a
_random decision forest_ and compare the performance of this classifier to that of the decision tree and the decision stump. The random decision forest is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. Again set the test option
_Percentage split_ to 90%. Select
_classifiers > trees > RandomForest_ and hit
_Start_. Again, observe the output. How high can you get the performance of the classifier by changing the number of trees (numTrees) parameter? How does the random decision forest compare performance wise to the decision tree and decision stump?

Deliverable:

· Your report including the screenshots of your implementation for each section and the results.

**credit_Dataset.arff.txt**

This file is too large to display. [View in new window](https://www.sweetstudy.com/files/credit-dataset-arff-txt)

– 1
– 2

November 5, 2026
[Solved] 1

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In-depth study guidance and analytical solution covering 1.

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November 5, 2026
[Solved] PE Week 2 Assignment 1

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Connect with your partners in the team’s discussion. Begin to get to know one another. – Review Chapter 2 of your text and other relevant resources listed for this week. Verify that everyone has viewed the Audiopedia and Wedell videos in this week’s Learning Resources. – Verify that everyone has rev…

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Connect with your partners in the team’s discussion. Begin to get to know one another.
– Review Chapter 2 of your text and other relevant resources listed for this week. Verify that everyone has viewed the Audiopedia and Wedell videos in this week’s Learning Resources.
– Verify that everyone has reviewed the Program Evaluation Proposal Template and the Team Project Planning Worksheet and noted which sections are due and when during the course. Consider how this and every week’s activities fit into completing sections of your Team Project Planning Worksheet and Program Evaluation Proposal Template.
– Review your RFP and the documents identified in this week’s Learning Resources, especially the requirements for your first team proposal work product, to be submitted in Week 4.
– **Post in your Project Team Space Discussion forum by Day 3** the initial information you have from the RFP for your team’s evaluation project. Describe your individual observations and thoughts about the following issues for planning and preparing:
– Who wants an evaluation?
– What should be the focus of the evaluation?
– Why is an evaluation wanted?
– When is an evaluation wanted?
– What resources are available to support an evaluation?
– Explain what you know from the RFP. Explain what you need to discuss further as part of planning the proposal for the program evaluation. Continue to refine your ideas with your team members throughout the rest of the week.
– Set your team plans and goals for this week:
– Establish group communications (what to communicate, how often, what means to use). You may wish to set up a live conference using Big Blue Button in Canvas or a free account with a third-party tool such as Zoom, Google Docs, or Slack.
– Review the first team assignment, due in Week 4, including the documents listed in this week’s Learning Resources. Using the Team Project Planning Worksheet, begin to identify tasks to be completed for the first team assignment. Make team assignments (what task, to whom). Establish due dates (what is due and when, how it will be submitted). Post these on the Team Project Planning Worksheet.
– Use the Team Project Planning Worksheet to plan and track the team’s progress to date and set your team’s goals and assignments for this week to move toward your Week 4 proposal submission.
– Use your group’s discussion forum and Wiki areas for continued team discussions. You also may wish to set up a live video conference using Big Blue Button in the classroom or a free account with a third-party tool such as Zoom, Google Docs, or Slack. If you have conversations outside of the classroom, document who attended and what was discussed/completed in the meeting in the team discussion forum and also note the meeting on your Team Project Planning Worksheet.
– Complete all team work that you planned for the week in order to stay on task for completing the team assignment, due in Week 4.
– Submit your Team Project Planning Worksheet.
_Be sure to support your work with specific references to the Learning Resources. Use proper APA format and citations._
Work as a team for mutual sharing of information and responsibilities. Each member of the team should contribute equitably. You may decide through team discussions who may have specific interests and/or skills for certain components of the agenda to complete the assignment as a team.
\
RFP5-SexualHarassmentHO.docx](https://www.sweetstudy.com/files/rfp5-sexualharassmentho-docx "RFP5-SexualHarassmentHO.docx")
\
TeamProjPlanningWkstHO.docx](https://www.sweetstudy.com/files/teamprojplanningwkstho-docx "TeamProjPlanningWkstHO.docx")

November 5, 2026
[Solved] Week 1 discussion

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**Application of Course Knowledge**: Answer all questions/criteria with explanations and detail. – Reflect upon a leader or manager who positively or negatively impacted your practice. Do not identify the leader by name. Describe the impact on your nursing practice. – Discuss the impact on the pract…

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Assignment Prompt & Instructions

**Application of Course Knowledge**: Answer all questions/criteria with explanations and detail.
– Reflect upon a leader or manager who positively or negatively impacted your practice. Do not identify the leader by name. Describe the impact on your nursing practice.
– Discuss the impact on the practice environment.
– Analyze the individual’s leadership skills using either the American Association of Colleges of Nursing (AACN) or the American Organization for Nursing Leadership (AONL) leadership competencies provided.
– Include a minimum of two different scholarly sources per week. Cite all references and provide references for all citations.

November 5, 2026
[Solved] PE Week 2 Assignment 2

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**To prepare for this Assignment:** – Reflect on this week’s experiences with your team. **Assignment** Complete your journal entry, reflecting on your experiences with the team process for this week. – How is it going? – What are you learning about yourself? – What are you learning about the team p…

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Assignment Prompt & Instructions

**To prepare for this Assignment:**
– Reflect on this week’s experiences with your team.
**Assignment**
Complete your journal entry, reflecting on your experiences with the team process for this week.
– How is it going?
– What are you learning about yourself?
– What are you learning about the team process?
– What seems to be working well? Not so well?
– How might you continue to support your team’s development and ongoing progress?
– What more may be needed to enhance the team process?
– Share any other thoughts you may have.

November 5, 2026
[Solved] Equity & Diversity Awareness Quiz – All Correct 100%

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**Equity & Diversity Awareness Quiz** **All Correct** **Question 1** According to the National Center for Education Statistics, what is the percentage of U.S. schools with no teachers of color on staff? **Question 2** Compared with White women, how likely are African American women in the U.S. to di…

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**Equity & Diversity Awareness Quiz**
**All Correct**
**Question 1**
According to the National Center for Education Statistics, what is the percentage of U.S. schools with no teachers of color on staff?
**Question 2**
Compared with White women, how likely are African American women in the U.S. to die during childbirth due to a lack of access to prenatal care, according to the Agency for Healthcare Research and Quality?
**Question 3**
According to a study by the American Association of Physicians for Human Rights, what percentage of physicians report witnessing a colleague giving reduced care or refusing care to lesbian, gay, or bisexual patients?
**Question 4**
In a 2007 study, UNICEF rated the treatment of children in the 23 wealthiest countries in the world based on 40 indicators of child well-being. Which two countries received the lowest ratings?
**Question 5**
According to a 2006 report from the American Civil Liberties Union, African Americans comprise more than 37% of people arrested for drug use, 59% of those convicted for drug use, and 74% of those sentenced to prison for drug use. African Americans comprise what percentage of U.S. drug users?
**Question 6**
The U.S. military budget is by far the highest of any country in the world. How much higher is the U.S. military budget than that of China, the world’s second biggest military spender?
**Question 7**
Compared with their U.S.-born peers, how likely are immigrant men in the U.S., ages 18-39, to be in jail or prison, according to a 2008 report from the Immigration Policy Center?
**Question 8**
According to the U.S. Census Bureau, the median annual income for U.S. white men, 25 years or older, who have earned graduate degrees, is $80,000. What are the median annual incomes for Latina and Native American women, 25 years or older, who have earned graduate degrees?
**Question 9**
According to UNICEF, the wealth of the three richest people in the world is roughly equal to the combined Gross Domestic Product of:
**Question 10**
Powder cocaine (largely used by wealthy people) and crack cocaine (largely used by economically disadvantaged people) contain roughly the same amount of the drug per gram. Under federal law, how much of these substances must an individual be convicted of possessing to be sentenced to a mandatory minimum of five years in prison?
**Question 11**
What percentage of gay, lesbian, bisexual, and transgender high school students report that their teachers “never” or “rarely” respond to homophobic remarks made by other students, according to a national study by GLSEN?
**Question 12**
Based on a 2007 report from the Economic Policy Institute, the annual earnings of the average full-time U.S. worker is roughly equal to:
**Question 13**
What portion of the U.S. Government budget goes to welfare and Social Security?
**Question 14**
A Princeton study of elite universities in the U.S. found that legacy applicants—people, usually white and wealthy, with a parent or grandparent who attended the institution—are far more privileged by legacy status than applicants of color are by affirmative action policies. The study determined that legacy status was roughly equivalent to how much of a boost to an applicant’s SAT score?
**Question 15**
In 1978 corporate CEOs in the United States earned, on average, 35 times more than the average worker. Today, they earn __ times more than the average worker.1 1. The State of Working America 2004/2005 by Lawrence Mishel, Jared Bernstein, and Sylvia Allegretto, 2005, p. 214.
**Question 16**
The majority of poor people in the U.S. live in
**Question 17**
According to the Bureau of Labor Statistics, approximately 33% of the U.S. workforce was unionized in 1962. What percentage is unionized today?
**Question 18**
Which of the following variables most closely predicts how high someone will score on the SAT test?
**Question 19**
How many children around the world die each day from hunger-related causes? 2 2. “Where and Why Are 10 Million Children Dying Every Year” (2003) by Robert Black, Saul Morris, and Jennifer Bryce in The Lancet, 361.
**Question 20**
The U.S. military budget is by far the highest of any country in the world. By 2003, the U.S. military budget was roughly equal to that of:
**Question 21**
What percentage of people around the world lives on less than $750 per year?
**Question 22**
How many people in the U.S. live in poverty according to the U.S. Census Bureau?
**Question 23**
According to the U.S. Census Bureau, how much more likely are African American and Latino mortgage applicants to be turned down for a loan, even after controlling for employment, financial, and neighborhood factors?
**Question 24**
According to a Catalyst study, what percentage of Fortune 500 CEOs are women?
**Question 25**
According to the U.S. Census Bureau, how many millionaires are there in the U.S.?
**Question 26**
Which of the following statements is consistent with research on poverty and education in the U.S.?
**Question 27**
According to United for a Fair Economy, how much money did the U.S. government spend in 2005 on corporate welfare (economic incentives, often provided to support corporations’ expansions into overseas markets)?
**Equity & Diversity Awareness Quiz – All Correct 100%**
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cathrine – ## TEMPLATE6030CaseStudy1.docx – TeachingChildrentoReadSectionY01Fall2025CO-8302025-805PM.zip **## TEMPLATE6030CaseStudy1.docx** **SERD 6030 Case Study in Reading** Please use this as a template for the case study. Remove all information in red font as you complete the section. Save your …

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cathrine

– ## TEMPLATE6030CaseStudy1.docx

– TeachingChildrentoReadSectionY01Fall2025CO-8302025-805PM.zip

**## TEMPLATE6030CaseStudy1.docx**

**SERD 6030 Case Study in Reading**

Please use this as a template for the case study. Remove all information in red font as you complete the section. Save your case study as [Yourlastname]readingcase.

**Part 1: Background Information (20 pts)**

**_Home History_**

Pseudonym, age, grade, and educational placement/ setting. Include information about the child’s family (who lives in the home, how many siblings, etc.) and any other factors that you believe are relevant to literacy outcomes.

**_School History_**

Information about the student’s grades, overall academic performance, retentions, special education placement category, and any related services received.

**_Literacy-Related Assessments_**

Using the chart below, provide current (within a year) assessment results (formal and/or informal) related to reading and writing. You should include the name of the reading and writing related assessment, purpose of the assessment (formal or diagnostic measure; used for placement or instructional leveling, etc.), and overall scores.

Then, explain the meaning of the data presented in your chart by discussing the strengths, weaknesses and a comparison of same-age peers.

| | | |
| — | — | — |
| Name of Literacy/ Writing Related Assessment | Purpose of the assessment | Scores |
| | | |
| | | |
| | | |
| | | |

Summarize any current IEP goals related to reading and writing found on the student’s IEP.

Once you have completed Part 1, upload your narrative to the Case Study Part 1 GoView assignment page.

**Part 2: Review of Affective Factors (20 pts)**

**_Analysis of Results_**

Analyze the results of the five assessments to describe the student’s affective factors in terms of attitudes, interests (reading & writing), self-concept, and value.

**_Connections_**

Make connections between what you learned while administering the assessments to Duke & Cartwright’s Active View of Reading.

**_Instruction_**

Write about some of the instructional recommendations that you may have for this student using the instructional strategies found in the course modules. Indicate the source of the information like this: (Module 2, Duke & Cartwright, p. 2).

Once you have completed Part 2,
**upload your narrative AND the completed affective assessments** to the Case Study Part 2 GoView assignment page. You may leave Part 1 above Part 2 when you submit.

**Part 3: Literacy & Writing Assessments (40 pts)**

**_Assessment Performance_**

Describe and analyze the student’s performance on
each of these assessments. Clearly identify strengths and areas for growth and justify these claims with specific assessment data. You may separate by paragraphs:

i. Phonics Assessment

ii. Spelling Assessment

iii. Fluency Assessment

iv. Comprehension Assessment

v. Writing Assessment

**_Connections_**

Make connections between what you learned while administering the above literacy and writing assessments to Scarborough’s reading rope, Duke & Cartwright’s Active View of Reading, & Sedita’s Writing Rope.

**_Instruction_**

Utilizing materials from the modules, justify specific instructional strategies that address areas for growth, based on the patterns from the assessment data. Consider
all data collected in Part 3 of the case study. You may leave Part 1 and Part 2 on your template when you submit.

Once you have completed Part 3, upload your
**narrative AND the completed literacy and writing assessments** to the Case Study Part 3 GoView assignment page.

**Part 4: Implementation Phase (50 pts)**

**_Instructional Strategies_ (20 pts)**

Paragraphs 1 & 2: Name the strategy, where you found it (i.e., Module 2 in the Temple, et al. article), and explain how the strategy is implemented. Create one paragraph per strategy.

**_Implementation_(20 pts)**

Paragraphs 3 & 4: Write a detailed paragraph for each strategy, including the following:

(a) how/when the strategies were implemented;

(b) the student’s reaction/ progress with the use of the strategies;

(c) your reaction to the strategies (i.e., Were you well prepared and understood the steps of the strategy well? Do you need to revisit the overall strategy?);

(d) lessons learned from the implementation (i.e., What needs to be changed/ adjusted in the future).

**_Limitations/Considerations_ (10 pts)**

Write one detailed paragraph evaluating the potential limitations and considerations impacting the literacy progress of this student.

Once you have completed Part 4, upload your
**completed template (without attached assessments)** to two places: the
**Part 4 Case Study GoView assignment page AND Anthology.**

**TeachingChildrentoReadSectionY01Fall2025CO-8302025-805PM.zip**

This file is too large to display. [View in new window](https://www.sweetstudy.com/files/teachingchildrentoreadsectiony01fall2025co-8302025-805pm-zip)

**TeachingChildrentoReadSectionY01Fall2025CO-8302025-805PM.zip**

This file is too large to display. [View in new window](https://www.sweetstudy.com/files/teachingchildrentoreadsectiony01fall2025co-8302025-805pm-zip)

**## TEMPLATE6030CaseStudy1.docx**

**SERD 6030 Case Study in Reading**

Please use this as a template for the case study. Remove all information in red font as you complete the section. Save your case study as [Yourlastname]readingcase.

**Part 1: Background Information (20 pts)**

**_Home History_**

Pseudonym, age, grade, and educational placement/ setting. Include information about the child’s family (who lives in the home, how many siblings, etc.) and any other factors that you believe are relevant to literacy outcomes.

**_School History_**

Information about the student’s grades, overall academic performance, retentions, special education placement category, and any related services received.

**_Literacy-Related Assessments_**

Using the chart below, provide current (within a year) assessment results (formal and/or informal) related to reading and writing. You should include the name of the reading and writing related assessment, purpose of the assessment (formal or diagnostic measure; used for placement or instructional leveling, etc.), and overall scores.

Then, explain the meaning of the data presented in your chart by discussing the strengths, weaknesses and a comparison of same-age peers.

| | | |
| — | — | — |
| Name of Literacy/ Writing Related Assessment | Purpose of the assessment | Scores |
| | | |
| | | |
| | | |
| | | |

Summarize any current IEP goals related to reading and writing found on the student’s IEP.

Once you have completed Part 1, upload your narrative to the Case Study Part 1 GoView assignment page.

**Part 2: Review of Affective Factors (20 pts)**

**_Analysis of Results_**

Analyze the results of the five assessments to describe the student’s affective factors in terms of attitudes, interests (reading & writing), self-concept, and value.

**_Connections_**

Make connections between what you learned while administering the assessments to Duke & Cartwright’s Active View of Reading.

**_Instruction_**

Write about some of the instructional recommendations that you may have for this student using the instructional strategies found in the course modules. Indicate the source of the information like this: (Module 2, Duke & Cartwright, p. 2).

Once you have completed Part 2,
**upload your narrative AND the completed affective assessments** to the Case Study Part 2 GoView assignment page. You may leave Part 1 above Part 2 when you submit.

**Part 3: Literacy & Writing Assessments (40 pts)**

**_Assessment Performance_**

Describe and analyze the student’s performance on
each of these assessments. Clearly identify strengths and areas for growth and justify these claims with specific assessment data. You may separate by paragraphs:

i. Phonics Assessment

ii. Spelling Assessment

iii. Fluency Assessment

iv. Comprehension Assessment

v. Writing Assessment

**_Connections_**

Make connections between what you learned while administering the above literacy and writing assessments to Scarborough’s reading rope, Duke & Cartwright’s Active View of Reading, & Sedita’s Writing Rope.

**_Instruction_**

Utilizing materials from the modules, justify specific instructional strategies that address areas for growth, based on the patterns from the assessment data. Consider
all data collected in Part 3 of the case study. You may leave Part 1 and Part 2 on your template when you submit.

Once you have completed Part 3, upload your
**narrative AND the completed literacy and writing assessments** to the Case Study Part 3 GoView assignment page.

**Part 4: Implementation Phase (50 pts)**

**_Instructional Strategies_ (20 pts)**

Paragraphs 1 & 2: Name the strategy, where you found it (i.e., Module 2 in the Temple, et al. article), and explain how the strategy is implemented. Create one paragraph per strategy.

**_Implementation_(20 pts)**

Paragraphs 3 & 4: Write a detailed paragraph for each strategy, including the following:

(a) how/when the strategies were implemented;

(b) the student’s reaction/ progress with the use of the strategies;

(c) your reaction to the strategies (i.e., Were you well prepared and understood the steps of the strategy well? Do you need to revisit the overall strategy?);

(d) lessons learned from the implementation (i.e., What needs to be changed/ adjusted in the future).

**_Limitations/Considerations_ (10 pts)**

Write one detailed paragraph evaluating the potential limitations and considerations impacting the literacy progress of this student.

Once you have completed Part 4, upload your
**completed template (without attached assessments)** to two places: the
**Part 4 Case Study GoView assignment page AND Anthology.**

**TeachingChildrentoReadSectionY01Fall2025CO-8302025-805PM.zip**

This file is too large to display. [View in new window](https://www.sweetstudy.com/files/teachingchildrentoreadsectiony01fall2025co-8302025-805pm-zip)

**## TEMPLATE6030CaseStudy1.docx**

**SERD 6030 Case Study in Reading**

Please use this as a template for the case study. Remove all information in red font as you complete the section. Save your case study as [Yourlastname]readingcase.

**Part 1: Background Information (20 pts)**

**_Home History_**

Pseudonym, age, grade, and educational placement/ setting. Include information about the child’s family (who lives in the home, how many siblings, etc.) and any other factors that you believe are relevant to literacy outcomes.

**_School History_**

Information about the student’s grades, overall academic performance, retentions, special education placement category, and any related services received.

**_Literacy-Related Assessments_**

Using the chart below, provide current (within a year) assessment results (formal and/or informal) related to reading and writing. You should include the name of the reading and writing related assessment, purpose of the assessment (formal or diagnostic measure; used for placement or instructional leveling, etc.), and overall scores.

Then, explain the meaning of the data presented in your chart by discussing the strengths, weaknesses and a comparison of same-age peers.

| | | |
| — | — | — |
| Name of Literacy/ Writing Related Assessment | Purpose of the assessment | Scores |
| | | |
| | | |
| | | |
| | | |

Summarize any current IEP goals related to reading and writing found on the student’s IEP.

Once you have completed Part 1, upload your narrative to the Case Study Part 1 GoView assignment page.

**Part 2: Review of Affective Factors (20 pts)**

**_Analysis of Results_**

Analyze the results of the five assessments to describe the student’s affective factors in terms of attitudes, interests (reading & writing), self-concept, and value.

**_Connections_**

Make connections between what you learned while administering the assessments to Duke & Cartwright’s Active View of Reading.

**_Instruction_**

Write about some of the instructional recommendations that you may have for this student using the instructional strategies found in the course modules. Indicate the source of the information like this: (Module 2, Duke & Cartwright, p. 2).

Once you have completed Part 2,
**upload your narrative AND the completed affective assessments** to the Case Study Part 2 GoView assignment page. You may leave Part 1 above Part 2 when you submit.

**Part 3: Literacy & Writing Assessments (40 pts)**

**_Assessment Performance_**

Describe and analyze the student’s performance on
each of these assessments. Clearly identify strengths and areas for growth and justify these claims with specific assessment data. You may separate by paragraphs:

i. Phonics Assessment

ii. Spelling Assessment

iii. Fluency Assessment

iv. Comprehension Assessment

v. Writing Assessment

**_Connections_**

Make connections between what you learned while administering the above literacy and writing assessments to Scarborough’s reading rope, Duke & Cartwright’s Active View of Reading, & Sedita’s Writing Rope.

**_Instruction_**

Utilizing materials from the modules, justify specific instructional strategies that address areas for growth, based on the patterns from the assessment data. Consider
all data collected in Part 3 of the case study. You may leave Part 1 and Part 2 on your template when you submit.

Once you have completed Part 3, upload your
**narrative AND the completed literacy and writing assessments** to the Case Study Part 3 GoView assignment page.

**Part 4: Implementation Phase (50 pts)**

**_Instructional Strategies_ (20 pts)**

Paragraphs 1 & 2: Name the strategy, where you found it (i.e., Module 2 in the Temple, et al. article), and explain how the strategy is implemented. Create one paragraph per strategy.

**_Implementation_(20 pts)**

Paragraphs 3 & 4: Write a detailed paragraph for each strategy, including the following:

(a) how/when the strategies were implemented;

(b) the student’s reaction/ progress with the use of the strategies;

(c) your reaction to the strategies (i.e., Were you well prepared and understood the steps of the strategy well? Do you need to revisit the overall strategy?);

(d) lessons learned from the implementation (i.e., What needs to be changed/ adjusted in the future).

**_Limitations/Considerations_ (10 pts)**

Write one detailed paragraph evaluating the potential limitations and considerations impacting the literacy progress of this student.

Once you have completed Part 4, upload your
**completed template (without attached assessments)** to two places: the
**Part 4 Case Study GoView assignment page AND Anthology.**

**TeachingChildrentoReadSectionY01Fall2025CO-8302025-805PM.zip**

This file is too large to display. [View in new window](https://www.sweetstudy.com/files/teachingchildrentoreadsectiony01fall2025co-8302025-805pm-zip)

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November 5, 2026
[Solved] For isabella

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November 5, 2026
[Solved] 1.3 discussion

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November 5, 2026
[Solved] Chapter 3 questions

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Each reply has to be 4 sentences. \ IMG_2015.png](https://www.sweetstudy.com/files/img-2015-png-10386633 "IMG_2015.png")

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Each reply has to be 4 sentences.
\
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November 5, 2026