Develop a Reading/Language Arts lesson plan designed around serving ELL students in your classroom.
Develop a Reading/Language Arts lesson plan designed around serving ELL students in your classroom.
Please follow EDTA LESSON PLAN METHODS
What seem to be the key focal points of the code of ethics?
Instructions
Project: Communication Ethics
This course has major project assignments due in Week 3 and Week 5. It will take more than a week’s effort to adequately complete them. Plan time to start the research and work on those assignments earlier than the week in which they are due.
Communication Ethics
In this assignment, you will learn to critically evaluate the code of ethics practiced in the corporate world.
Complete the following tasks:
· Find an example of an actual corporate code of ethics (you may also use the code of ethics from your current employment).
· Critique the code of ethics by addressing the following questions:
· What seem to be the key focal points of the code of ethics?
· Does the firm urge all its employees to uphold the code and perform only in ethically acceptable ways? How?
· Does the code discuss the firm’s values and reflect the corporate culture? How?
· Recommend at least three improvements the organization could make to its code of ethics.
Support your answers with examples and reasoning.
Submission Details:
· Submit your assignment as a 3 to 4 pages Microsoft Word document, using APA style.
· Support your answers with examples, reasoning, and research.
Identify an example from your professional career in which both highly favorable and unfavorable variances occurred.
Several tools are available to managers to assist them in evaluating organizational performance, one of them being variance analysis. Variance analysis compares actual costs and quantities with standard costs and quantities. The results from a variance analysis are important for helping managers control costs (price variances) and identify areas where organizational performance and efficiency can be improved (quantity variances). In this Discussion, you will use an example from your professional career to consider the role of variance analysis in the decision-making processes for an organization.
To prepare for this Discussion:
· Consider the role of variance analysis in decision making and how understanding variances, both favorable and unfavorable, might help contribute to improved organizational efficiency.
· Identify an example from your professional career in which both highly favorable and unfavorable variances occurred.
Post an explanation of the role of variance analysis in managerial decision making, to include the following: (the assignment should be 300 words or more)
· Describe an example from your professional career in which both highly favorable and unfavorable variances occurred.
· Identify what stakeholders were most affected by these variances.
· Explain how understanding these variances could have helped you as a manager in this situation to make better decisions.
References
Should the United States government impose a carbon tax, why or why not?
Chapter 10 explores Externalities, positive and negative. Climate change represents a negative externality. Read about NASA’s evidence relating to climate change. Then explore the World Bank’s overview of carbon pricing.
Should the United States government impose a carbon tax, why or why not?
Which features contribute mostly to the linear regression model? Is the model fitting the data well? How large is the model error?
We consider a regression problem for predicting the demand of bike-sharing services in Washington D.C.1 The prediction task is to predict the demand for the bikes (column cnt) given the other features: ignore the columns instant and dteday. Use the day.csv file from the data folder. (a) Write a Python file to load day.csv.2 Compute the correlation coefficient of each feature with the response (i.e., cnt). Include a table with the correlation coefficient of each feature with the response. Which features are positively correlated (i.e., have positive corre- lation coefficient) with the response? Which feature has the highest positive correlation with the response? (b) Were you able to find any features with a negative correlation coefficient with the response? If not, can you think of a feature that is not provided in the dataset but may have a negative correlation coefficient with the response? (c) Now, divide the data into training and test sets with the training set having about 70 percent of the data. Import train_test_split from sklearn to perform this operation. Use an existing package to train a multiple linear regression model on the training set using all the features (except the ones excluded above). Report the coefficients of the linear regression models and the following metrics on the training data: (1) RMSE metric; (2) R2 metric. [Hint: You may find the libraries sklearn.linear_model.LinearRegression useful.] (d) Next, use the test set that was generated in the earlier step. Evaluate the trained model in step (c) on the testing set. Report the RMSE and R2 metrics on the testing set. (e) Interpret the results in your own words. Which features contribute mostly to the linear regression model? Is the model fitting the data well? How large is the model error?
1https://www.kaggle.com/datasets/marklvl/bike-sharing-dataset?search=bike+demand+Washington& select=Readme.txt. You can also find a Readme.txt file that explains all the features in the dataset. 2Refer to https://docs.python.org/3/library/csv.html on how to load a csv file in Python.
Analyze the impact that laws and regulations have on ethical decisions made in conducting a business.
Assignment #3
Chapter 4
Social Responsibility Real World Case
Competency:
#4 Discussing the nature of the corporation and its relationship to society.
#8 Analyzing the impact that laws and regulations have on ethical decisions made in conducting a business.
#15 Analyzing specific ethical decisions made by businesses in the areas of human resources, marketing, accounting/finance, and production.
Having read Chapter 4, answer the following:
Read on pages 115-116 the “Cases From the Real World” regarding Corporate and Personal Choices Regarding the Environment of the Future and address the three Critical Thinking questions, applying the lessons from the chapter. Demonstrate your understanding of the competencies by conveying the chapter material, applying the lessons to the Case.
Assignment 3 will be graded on conveying a clear understanding of the topic, providing a clear discussion of such, as well as utilizing correct grammar.
What parallels do you see between events in our times and the events and/or practices described in this reading?
What new things do we learn in this reading, and why are they important to the story that Orwell is telling?
- What parallels do you see between events in our times and the events and/or practices described in this reading?
- In what specific ways do you see this book coming true now, and in what ways did Orwell get it wrong?
- What questions do you still have about the reading, and what would you like to ask your peers about this material?
Find the expected value of the sum obtained when n fair coin flips are rolled independently.
Supervised Machine Learning and Learning Theory Fall 2022
Problem Set 1
Instructor: Hongyang Ryan Zhang Due: September 30, 2022, 11:59pm
Instructions:
• You are expected to write up the solution on your own. Discussions and collaborations are
encouraged; remember to mention any fellow students you discussed with when you turn in
the solution.
• There are up to three late days for all the problem sets and project submissions. Use them
wisely. After that, the grade depreciates by 20% for every extra day. Late submissions are
considered case by case. Please reach out to the instructor if you cannot meet the deadline.
• Submit your written solutions to Gradescope and upload your code to Canvas. You are
recommended to write up the solution in LaTeX.
Problem 1 (20 points)
(a) (1 point) Calculate V ar(X) when X represents the outcome when a fair coin flip (i.e., E[X] =
1/2).
(b) (1 point) Find the expected value of the sum obtained when n fair coin flips are rolled
independently.
(c) (2 point) For three events A, B, and C, we know that: A and C are independent, B and C are
independent, A and B are disjoint, P (A∪C) = 2/3, P (B ∪C) = 3/4, P (A∪B ∪C) = 11/12.
Find P (A), P (B) and P (C).
(d) (2 points) Consider a test to detect a disease (e.g., COVID-19), assuming that 0.6% of the
population has it. The test is 97% effective in detecting an infected person. However, the
test gives a false-positive result in 1% of cases (meaning that it shows a positive result if the
person is not infected). What is the probability that a person gets a negative test result?
(e) (2 points) If a person tests positive for the disease, what is the probability that they actually
have COVID?
(f) (2 points) If a person tests negative for the disease, what is the probability that they are
infected with COVID?iPhoneiPhoneiPhoneiPhoneiPhone
Along with the tests, data regarding the number of symptoms shown by the patients was also
recorded and is given below. The data was collected from 2 different sources.
No. of Symptoms Patients
1 20
2 20
3 20
4 20
No. of Symptoms Patients
1 70
2 15
3 10
4 5
(g) (2 points) Suppose you pick one patient from each of the above 2 sources independently.
What would be the expected number of symptoms detected in each of them?
(h) (2 points) Prove that V ar(X) = E[X]2−(E[X])2. Explain the interpretation of this derivation.
(i) (2 points) Let Y1 and Y2 denote the number of symptoms detected in each of the above two
patients respectively, where Y1, Y2 ∈ [1, 2, 3, 4]. Then calculate the following probabilities: (i)
E[Y1Y2]; (ii) V ar[Y1 − Y2].
(j) (2 points) Among a population of n people, let X be the number of people that test positive.
What is the expectation of X, E[X]? What is the variance of X, V ar[X]? Make sure to
include all the steps in the calculation.
(k) (2 points) Define bias error and variance error. What do you understand by Bias-Variance
trade-off?
Problem 2 (20 points)
(a) (2 points) Show that for any arbitrary matrix X ∈ Rm×n, the matrix XX⊤ is always positive
semi-definite.
(b) Recall that the SVD of a rank-r matrix M has the form
M =
r∑ i=1
σiuiv T i ,
where {ui}ri=1 denote the left singular vectors, {vi}ri=1 denote the right singular vectors, and
{σi}ri=1 denote the singular values.
i) (2 points) Let
A =
1 1
1 1
1 −1
.
Calculate the left and right singular vectors {ui}ri=1 and {vi}ri=1 of A. Then show that
{ui}ri=1 and {vi}ri=1 are the eigenvectors of AA⊤ and A⊤A.
ii) (5 points) Let M ∈ Rm×n be an arbitrary real-valued rank-r matrix, show that the
eigenvectors of MMT and MTM are {ui}ri=1 and {vi}ri=1 respectively.
(c) Recall that the best rank-k approximation of M in Frobenius norm is attained by
B =
k∑ i=1
σiuiv T i .
i) (2 points) For the matrix A defined above, calculate the best rank-1 approximation of
A in Frobenius norm. Then find out the approximation error ||M −B||F .
ii) (5 points) Let M ∈ Rm×n be an arbitrary real-valued rank-r matrix. Show that
||M −B||F =
√√√√ r∑ i=k+1
σ2 i .
(d) (4 points) Write a Python file to verify your calculation in (b-i) and (c-i). You may find the
library numpy.linalg.svd and numpy.linalg.eig useful.
Problem 3 (15 points)
(a) (6 points) For vectors x ∈ Rn, a ∈ Rn and matrices X ∈ Rn×n,A ∈ Rn×n, show the following:
(i) ∂aTx
∂x = a.
(ii) ∂xTAx
∂x = (A+AT )x.
(iii) ∂||y −Ax||22
∂x = 2AT (Ax− y).
(b) (4 points) You are given a training set {(x1, y1), . . . , (xn, yn)}, where xi ∈ Rd and yi ∈ R. Consider the regression problem
min θ∈Rd
1
n
n∑ i=1
(yi − θTxi)) 2.
What is the minimizer of the above regression problem? Provide all steps of your derivation.
Feel free to assume that the rank of {xi}ni=1 is equal to d.]
(c) (5 points) Let the cost function to minimize is:
J(w) =
n∑ i=1
(yi − θTxi)) 2 + λ
d∑ j=0
θj 2
Prove that the vector w⋆ that minimizes J(w) is:
w⋆ = (X⊤X + λI)−1X⊤y,
where X is the n by d design matrix, whose i-th row is xi, and y = (y1, …, yn) ⊤.
Problem 4 (15 points)
We consider a regression problem for predicting the demand of bike-sharing services in Washington
D.C.1 The prediction task is to predict the demand for the bikes (column cnt) given the other
features: ignore the columns instant and dteday. Use the day.csv file from the data folder.
(a) (4 points) Write a Python file to load day.csv.2 Compute the correlation coefficient of each
feature with the response (i.e., cnt). Include a table with the correlation coefficient of each
feature with the response. Which features are positively correlated (i.e., have positive corre-
lation coefficient) with the response? Which feature has the highest positive correlation with
the response?
(b) (2 points) Were you able to find any features with a negative correlation coefficient with the
response? If not, can you think of a feature that is not provided in the dataset but may have
a negative correlation coefficient with the response?
(c) (5 points) Now, divide the data into training and test sets with the training set having about
70 percent of the data. Import train_test_split from sklearn to perform this operation.
Use an existing package to train a multiple linear regression model on the training set using all
the features (except the ones excluded above). Report the coefficients of the linear regression
models and the following metrics on the training data: (1) RMSE metric; (2) R2 metric.
[Hint: You may find the libraries sklearn.linear_model.LinearRegression useful.]
(d) (2 points) Next, use the test set that was generated in the earlier step. Evaluate the trained
model in step (c) on the testing set. Report the RMSE and R2 metrics on the testing set.
(e) (2 points) Interpret the results in your own words. Which features contribute mostly to the
linear regression model? Is the model fitting the data well? How large is the model error?
1https://www.kaggle.com/datasets/marklvl/bike-sharing-dataset?search=bike+demand+Washington&
select=Readme.txt. You can also find a Readme.txt file that explains all the features in the dataset. 2Refer to https://docs.python.org/3/library/csv.html on how to load a csv file in Python.https://www.kaggle.com/datasets/marklvl/bike-sharing-dataset?search=bike+demand+Washington&select=Readme.txthttps://www.kaggle.com/datasets/marklvl/bike-sharing-dataset?search=bike+demand+Washington&select=Readme.txthttps://docs.python.org/3/library/csv.html
Problem 5 (10 points)
This question should be answered using the Diabetes data set that is readily available in the
Scikit-learn library.3 This data set has information about 442 patients and whether they have
suffered from diabetes or not.
(a) (2 points) Fit a multiple regression model to predict Diabetes using Age, Sex, BMI, and BP.
(b) (4 points) Provide an interpretation of each coefficient in the model. Be careful—some of the
variables in the model are qualitative!
(c) (2 points) Write out the model in equation form, being careful to handle the qualitative
variables properly.
(d) (2 points) Using the model from (c), obtain 95% confidence intervals for the coefficient(s).
Problem 6 (20 points)
We will now perform cross-validation on a simulated data set.
(a) (2 points) Generate a simulated data set as follows:
numpy.random.seed(12345)
x = numpy.random.normal(0, 1, (200))
y = x + 2 * x**2 – 2 * x**3 + numpy.random.normal(0, 1, (200))
In this data set, what is n and what is p? Write out the model used to generate the data in
equation form.
(b) (2 points) Create a scatterplot of X against Y . Comment on what you find. (Hint: You may
find matplotlib.pyplot.plot() helpful)
(c) (9 points) Set a random seed 123, and then compute the leave-one-out cross validation errors
that result from fitting the following five models using least squares:
(i) Y = β0 + β1X + ε
(ii) Y = β0 + β1X + β2X 2 + ε
(iii) Y = β0 + β1X + β2X 2 + β3X
3 + ε
(iv) Y = β0 + β1X + β2X 2 + β3X
3 + β4X 4 + ε
3https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html. You can
find the description of this data set at https://scikit-learn.org/stable/datasets/toy_dataset.html.https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.htmlhttps://scikit-learn.org/stable/datasets/toy_dataset.html
(v) Y = β0 + β1X + β2X 2 + β3X
3 + β4X 4 + β5X
5 + ε
[Hint: You may find LeaveOneOut() and cross_val_score() in sklearn.model_selection
helpful.]
(d) (2 points) Repeat (c) using another random seed 12345, and report your results. Are your
results the same as what you got in (c)? Why?
(e) (5 points) Which of the models in (c) had the smallest leave-one-out cross validation error?
Is this what you expected? Explain your answer.
In your own words, how would you define that theory?
Chapter 5 of the textbook mentions the self-presentation theory. In your own words, how would you define that theory? Then, based on the various types of online communities outlined in the textbook, from the list below, what online community would you identify yourself with and why?
- Communities of practice
- Communities of interest
- Communities of transaction
- Communities of relationship
- Communities of fantasy
- Brand communities
- Social Networks
Does employment-at-will have better protections for employees or employers? Why or why not?
Primary Task Response: Within the Discussion Board area, write 400-600 words that respond to the following questions with your thoughts, ideas, and comments. This will be the foundation for future discussions by your classmates. Be substantive and clear, and use examples to reinforce your ideas.
Over the years, there has been much debate over the classification of employment-at-will employees. Employment-at-will is a term that refers to the protection that is applied to the employment relationship, such that the employer or the employee has the right to terminate the employment relationship at any time. There are different modifications to employment-at-will that vary at the state level. With your classmates, please discuss the following:
- Does employment-at-will have better protections for employees or employers? Why or why not?
- Choose a state and describe its modifications to employment-at-will. Do you agree with these modifications? Why or why not?
