Sashi OB dis 3 – I need a discussion on the below topic Writing Assignment Help

This assignment has 4 purposes:

1. To make sure that your camera works.
2. To see if you can use the basic controls on your cameras.
3. To see if you can upload your images to a computer and process them properly.
4. To see how creative you can be illustrating ideas.

Your goal is to make photographs that illustrate 10 of the words on the scavenger hunt list.

The Process:

Set your camera to RAW + JPG mode [note: you will only have to use this mode for this assignment]

Set your camera to monochrome Picture Style/Picture Control/Film Simulation mode

This setting is different on each camera. If you google your camera name plus “black and white picture mode” you should find a tutorial that’s specific to your camera. Ask me if you need help! [Note: we’re only using this for the first assignment. This is not the best way to convert to black and white. But it does give you a black and white preview in the camera!]

Shoot in Manual mode (M). Use autofocus if you like, but control the exposure.

Upload your images using Bridge.

Rename your files Lastname_YYMMDD_0001 (ex: Hemmerle_200115_0001)

Select one image to illustrate each word. You can only turn in one image for each word!

Make a folder called Lastname_200203_SELF on the class google drive, and upload your JPG and writeup files into it.

Print each image at 8.5” x 11” on any printer you have access to.

NOTE- All the images you turn in for this assignment should be made AFTER 15 JAN 2020.

Checklist:

• Am I turning in exactly 10 images?
• Are the images properly named?
• Are the images black and white?
• Don’t forget to do a write up!

Write Up Guidelines

Name your document Lastname_200115_WriteUp

Did you find interesting ways to illustrate each word?

Does it look like you thought about each image and found the best perspective?

Are your images properly named and uploaded?

Are your images properly exposed? In focus (if necessary)?

Scavenger Hunt Word List

1 Small

2 Bright

3 Shadow

4 Confusing

5 Busy

6 Dull

7 Line

8 Texture

9 Contrast

10 Flat

Week03 HW

Attached Files:

• HW03.docx (24.773 KB)
• Advertising.csv (4.645 KB)
• Auto.csv (20.043 KB)
• Boston.csv (36.775 KB)
• Credit.csv (21.844 KB)

Initially summarize the questions attempted

Q1: ISLR Textbook chapter review questions

Chapter 3 – Linear Regression Review the following sections in Python

• Load Datasets
• 3.1 Simple Linear Regression
• 3.2 Multiple Linear Regression
• 3.3 Other Considerations in the Regression Model

Q2 Textbook Theory Questions 3.7 Exercises

1. Describe the null hypotheses to which the p-values given in Table 3.4 correspond. Explain what conclusions you can draw based on these p-values. Your explanation should be phrased in terms of sales, TV, radio, and newspaper, rather than in terms of the coefficients of the linear model.

Q3 Applied Textbook Questions with Python 3.7 Exercises

Hint – several github sites have the complete solution in python e.g.

https://github.com/mscaudill/IntroStatLearn

https://botlnec.github.io/islp/

8. This question involves the use of simple linear regression on the Auto data set. (a) Perform a simple linear regression with mpg as the response and horsepower as the predictor. Print the results. Comment on the output. For example:

i. Is there a relationship between the predictor and the response?

ii. How strong is the relationship between the predictor and the response?

iii. Is the relationship between the predictor and the response positive or negative?

iv. Predictions

1. What is the predicted mpg associated with a horsepower of 98? What are the associated 95 % confidence and prediction intervals?
2. Plot the response and the predictor, display the least squares regression line.
3. Produce diagnostic plots of the least squares regression fit. Comment on any problems you see with the fit.

9. This question involves the use of multiple linear regression on the Auto data set.

(a) Produce a scatterplot matrix which includes all of the variables in the data set.

(b) Compute the matrix of correlations between the variables.

(c) Perform a multiple linear regression with mpg as the response and all other variables except name as the predictors and print the results. Comment on the output. For instance:

i. Is there a relationship between the predictors and the response?

ii. Which predictors appear to have a statistically significant relationship to the response?

iii. What does the coefficient for the year variable suggest?

(d) Produce diagnostic plots of the linear regression fit. Comment on any problems you see with the fit. Do the residual plots suggest any unusually large outliers? Does the leverage plot identify any observations with unusually high leverage?

(e) Fit linear regression models with interaction effects. Do any interactions appear to be statistically significant?

(f) Try a few different transformations of the variables, such as log(X), √ X, X2. Comment on your findings.

Opional

15. This problem involves the Boston data set, which we saw in the lab for this chapter. We will now try to predict per capita crime rate using the other variables in this data set. In other words, per capita crime rate is the response, and the other variables are the predictors.

(a) For each predictor, fit a simple linear regression model to predict the response. Describe your results. In which of the models is there a statistically significant association between the predictor and the response? Create some plots to back up your assertions.

(b) Fit a multiple regression model to predict the response using all of the predictors. Describe your results. For which predictors can we reject the null hypothesis H0 : βj = 0?

(c) How do your results from (a) compare to your results from (b)? Create a plot displaying the univariate regression coefficients from (a) on the x-axis, and the multiple regression coefficients from (b) on the y-axis. That is, each predictor is displayed as a single point in the plot. Its coefficient in a simple linear regression model is shown on the x-axis, and its coefficient estimate in the multiple linear regression model is shown on the y-axis.

(d) Is there evidence of non-linear association between any of the predictors and the response? To answer this question, for each predictor X, fit a model of the form Y = β0 + β1X + β2X2 + β3X3 + .

thorough legal review by the company’s attorneys. Upon final approval by the Corporate Governance Board, the policies will be adopted and placed into the Employee Handbook.

Research:

• Review the table of contents and relevant chapters in the Certified Information Privacy Professional textbook to find information about legal and regulatory drivers.
• Review the remaining course readings.
• Review the sample policies and procedures provided in Week 3.
• Find additional sources which provide information about the policy statements which should be covered in three policies for the Employee Handbook.

Write:

• Prepare briefing package with approval drafts of the three IT related policies for the Employee Handbook. Yourbriefing package must contain the following:
• Use a professional format for your policy documents and briefing package. A recommended format is provided in the assignment template file (see the recommended template under Course Resources).
• You must include a cover page with the assignment title, your name, and the due date. Your reference list must be on a separate page at the end of your file. These pages do not count towards the assignment’s page count.
• Common phrases do not require citations. If there is doubt as to whether or not information requires attribution, provide a footnote with publication information or use APA format citations and references.
• You are expected to write grammatically correct English in every assignment that you submit for grading. Do not turn in any work without (a) using spell check, (b) using grammar check, (c) verifying that your punctuation is correct and (d) reviewing your work for correct word usage and correctly structured sentences and paragraphs.

• Executive Summary
• “Approval Drafts” for
  • Acceptable Use Policy for Information Technology
  • Bring Your Own Device Policy
  • Digital Media Sanitization, Reuse, & Destruction Policy

As you write your policies, make sure that you address security issues using standard cybersecurity terminology (e.g. 5 Pillars of IA, 5 Pillars of Information Security). See the resources listed under Course Resources > Cybersecurity Concepts Review for definitions and terminology.

Week03 HW

Attached Files:

• HW03.docx (24.773 KB)
• Advertising.csv (4.645 KB)
• Auto.csv (20.043 KB)
• Boston.csv (36.775 KB)
• Credit.csv (21.844 KB)

Initially summarize the questions attempted

Q1: ISLR Textbook chapter review questions

Chapter 3 – Linear Regression Review the following sections in Python

• Load Datasets
• 3.1 Simple Linear Regression
• 3.2 Multiple Linear Regression
• 3.3 Other Considerations in the Regression Model

Q2 Textbook Theory Questions 3.7 Exercises

1. Describe the null hypotheses to which the p-values given in Table 3.4 correspond. Explain what conclusions you can draw based on these p-values. Your explanation should be phrased in terms of sales, TV, radio, and newspaper, rather than in terms of the coefficients of the linear model.

Q3 Applied Textbook Questions with Python 3.7 Exercises

Hint – several github sites have the complete solution in python e.g.

https://github.com/mscaudill/IntroStatLearn

https://botlnec.github.io/islp/

8. This question involves the use of simple linear regression on the Auto data set. (a) Perform a simple linear regression with mpg as the response and horsepower as the predictor. Print the results. Comment on the output. For example:

i. Is there a relationship between the predictor and the response?

ii. How strong is the relationship between the predictor and the response?

iii. Is the relationship between the predictor and the response positive or negative?

iv. Predictions

1. What is the predicted mpg associated with a horsepower of 98? What are the associated 95 % confidence and prediction intervals?
2. Plot the response and the predictor, display the least squares regression line.
3. Produce diagnostic plots of the least squares regression fit. Comment on any problems you see with the fit.

9. This question involves the use of multiple linear regression on the Auto data set.

(a) Produce a scatterplot matrix which includes all of the variables in the data set.

(b) Compute the matrix of correlations between the variables.

(c) Perform a multiple linear regression with mpg as the response and all other variables except name as the predictors and print the results. Comment on the output. For instance:

i. Is there a relationship between the predictors and the response?

ii. Which predictors appear to have a statistically significant relationship to the response?

iii. What does the coefficient for the year variable suggest?

(d) Produce diagnostic plots of the linear regression fit. Comment on any problems you see with the fit. Do the residual plots suggest any unusually large outliers? Does the leverage plot identify any observations with unusually high leverage?

(e) Fit linear regression models with interaction effects. Do any interactions appear to be statistically significant?

(f) Try a few different transformations of the variables, such as log(X), √ X, X2. Comment on your findings.

Opional

15. This problem involves the Boston data set, which we saw in the lab for this chapter. We will now try to predict per capita crime rate using the other variables in this data set. In other words, per capita crime rate is the response, and the other variables are the predictors.

(a) For each predictor, fit a simple linear regression model to predict the response. Describe your results. In which of the models is there a statistically significant association between the predictor and the response? Create some plots to back up your assertions.

(b) Fit a multiple regression model to predict the response using all of the predictors. Describe your results. For which predictors can we reject the null hypothesis H0 : βj = 0?

(c) How do your results from (a) compare to your results from (b)? Create a plot displaying the univariate regression coefficients from (a) on the x-axis, and the multiple regression coefficients from (b) on the y-axis. That is, each predictor is displayed as a single point in the plot. Its coefficient in a simple linear regression model is shown on the x-axis, and its coefficient estimate in the multiple linear regression model is shown on the y-axis.

(d) Is there evidence of non-linear association between any of the predictors and the response? To answer this question, for each predictor X, fit a model of the form Y = β0 + β1X + β2X2 + β3X3 +