Understanding information security issues

1. Open a new Microsoft® Word document, and enter your answer to one of the prompts below:
   • Select two articles that detail hacking tools. Summarize each article and include a concluding paragraph that explains how you as a cybersecurity professional would use these tools.
   • Scan the articles that appeared over the last month. Use those articles to create your own one-page summary of any trends you notice as well as any major events that have occurred.
   • Visit the “Popular Posts” tab and select one article that is of particular interest to you. Perform additional Internet research on that topic and then write a summary of that article. Include why you were interested in the article, what you learned from it, and how you could use it in your career as a cybersecurity professional.
2. Save the file on your computer with your last name in the file name. (Example: chapter_01_essay_Jones.doc)
3. Click the Browse button below to find and select your saved document, and then click Submit Assignment for Grading.

    How can we use market data to suggest hourly rates to Taskers that would

    maximize their opportunity to be hired?

    Please describe in detail, with code and formulas that support your model.

A factorial experiment was designed to test for any significant differences in the time needed to perform English to foreign language translations with two computerized language translators. Because the type of language translated was also considered a significant factor, translations were made with both systems for three different languages: Spanish, French, and German. Use the following data for translation time in hours.

Test for any significant differences due to language translator, type of language, and interaction. Use α = 0.05.

Find the value of the test statistic for language translator. (Round your answer to two decimal places.)

Find the p-value for language translator. (Round your answer to three decimal places.)

p-value =

State your conclusion about language translator.

Because the p-value > α = 0.05, language translator is not significant.Because the p-value ≤ α = 0.05, language translator is not significant.     Because the p-value ≤ α = 0.05, language translator is significant.Because the p-value > α = 0.05, language translator is significant.

Find the value of the test statistic for type of language. (Round your answer to two decimal places.)

Find the p-value for type of language. (Round your answer to three decimal places.)

p-value =

State your conclusion about type of language.

Because the p-value > α = 0.05, type of language is significant.Because the p-value ≤ α = 0.05, type of language is not significant.     Because the p-value > α = 0.05, type of language is not significant.Because the p-value ≤ α = 0.05, type of language is significant.

Find the value of the test statistic for interaction between language translator and type of language. (Round your answer to two decimal places.)

Find the p-value for interaction between language translator and type of language. (Round your answer to three decimal places.)

p-value =

State your conclusion about interaction between language translator and type of language.

Because the p-value > α = 0.05, interaction between language translator and type of language is not significant.Because the p-value ≤ α = 0.05, interaction between language translator and type of language is significant.     Because the p-value > α = 0.05, interaction between language translator and type of language is significant.Because the p-value ≤ α = 0.05, interaction between language translator and type of language is not significant.

A factorial experiment was designed to test for any significant differences in the time needed to perform English to foreign language translations with two computerized language translators. Because the type of language translated was also considered a significant factor, translations were made with both systems for three different languages: Spanish, French, and German. Use the following data for translation time in hours.

Test for any significant differences due to language translator, type of language, and interaction. Use α = 0.05.

Find the value of the test statistic for language translator. (Round your answer to two decimal places.)

Find the p-value for language translator. (Round your answer to three decimal places.)

p-value =

State your conclusion about language translator.

Because the p-value ≤ α = 0.05, language translator is not significant. Because the p-value > α = 0.05, language translator is not significant.     Because the p-value > α = 0.05, language translator is significant. Because the p-value ≤ α = 0.05, language translator is significant.

Find the value of the test statistic for type of language. (Round your answer to two decimal places.)

Find the p-value for type of language. (Round your answer to three decimal places.)

p-value =

State your conclusion about type of language.

Because the p-value ≤ α = 0.05, type of language is significant. Because the p-value > α = 0.05, type of language is not significant.     Because the p-value ≤ α = 0.05, type of language is not significant. Because the p-value > α = 0.05, type of language is significant.

Find the value of the test statistic for interaction between language translator and type of language. (Round your answer to two decimal places.)

Find the p-value for interaction between language translator and type of language. (Round your answer to three decimal places.)

p-value =

State your conclusion about interaction between language translator and type of language.

Because the p-value > α = 0.05, interaction between language translator and type of language is not significant. Because the p-value ≤ α = 0.05, interaction between language translator and type of language is significant.     Because the p-value > α = 0.05, interaction between language translator and type of language is significant. Because the p-value ≤ α = 0.05, interaction between language translator and type of language is not significant.

A mail-order catalog firm designed a factorial experiment to test the effect of the size of a magazine advertisement and the advertisement design on the number of catalog requests received (data in thousands). Three advertising designs and two different-size advertisements were considered. The data obtained follow.

Use the ANOVA procedure for factorial designs to test for any significant effects due to type of design, size of advertisement, or interaction. Use α = 0.05.

Find the value of the test statistic for type of design. (Round your answer to two decimal places.)

Find the p-value for type of design. (Round your answer to three decimal places.)

p-value =

State your conclusion about type of design.

Because the p-value ≤ α = 0.05, type of design is not significant.

Because the p-value ≤ α = 0.05, type of design is significant.    

Because the p-value > α = 0.05, type of design is significant

.Because the p-value > α = 0.05, type of design is not significant.

Find the value of the test statistic for size of advertisement. (Round your answer to two decimal places.)

Find the p-value for size of advertisement. (Round your answer to three decimal places.)

p-value =

State your conclusion about size of advertisement.

Because the p-value ≤ α = 0.05, size of advertisement is not significant.

Because the p-value ≤ α = 0.05, size of advertisement is significant.   

Because the p-value > α = 0.05, size of advertisement is significant.

Because the p-value > α = 0.05, size of advertisement is not significant.

Find the value of the test statistic for interaction between type of design and size of advertisement. (Round your answer to two decimal places.)

Find the p-value for interaction between type of design and size of advertisement. (Round your answer to three decimal places.)

p-value =

State your conclusion about interaction between type of design and size of advertisement.

Because the p-value ≤ α = 0.05, interaction between type of design and size of advertisement is not significant.

Because the p-value ≤ α = 0.05, interaction between type of design and size of advertisement is significant.    

Because the p-value > α = 0.05, interaction between type of design and size of advertisement is significant.

Because the p-value > α = 0.05, interaction between type of design and size of advertisement is not significant.

A factorial experiment was designed to test for any significant differences in the time needed to perform English to foreign language translations with two computerized language translators. Because the type of language translated was also considered a significant factor, translations were made with both systems for three different languages: Spanish, French, and German. Use the following data for translation time in hours.

Test for any significant differences due to language translator, type of language, and interaction. Use α = 0.05.

Find the value of the test statistic for language translator. (Round your answer to two decimal places.)

Find the p-value for language translator. (Round your answer to three decimal places.)

p-value =

State your conclusion about language translator.

Because the p-value ≤ α = 0.05, language translator is significant.Because the p-value ≤ α = 0.05, language translator is not significant.    Because the p-value > α = 0.05, language translator is not significant.Because the p-value > α = 0.05, language translator is significant.

Find the value of the test statistic for type of language. (Round your answer to two decimal places.)

Find the p-value for type of language. (Round your answer to three decimal places.)

p-value =

State your conclusion about type of language.

Because the p-value > α = 0.05, type of language is not significant.Because the p-value ≤ α = 0.05, type of language is not significant.    Because the p-value > α = 0.05, type of language is significant.Because the p-value ≤ α = 0.05, type of language is significant.

Find the value of the test statistic for interaction between language translator and type of language. (Round your answer to two decimal places.)

Find the p-value for interaction between language translator and type of language. (Round your answer to three decimal places.)

p-value =

State your conclusion about interaction between language translator and type of language.

Because the p-value > α = 0.05, interaction between language translator and type of language is not significant.Because the p-value ≤ α = 0.05, interaction between language translator and type of language is not significant.    Because the p-value ≤ α = 0.05, interaction between language translator and type of language is significant.Because the p-value > α = 0.05, interaction between language translator and type of language is significant.

A mail-order catalog firm designed a factorial experiment to test the effect of the size of a magazine advertisement and the advertisement design on the number of catalog requests received (data in thousands). Three advertising designs and two different-size advertisements were considered. The data obtained follow.

Use the ANOVA procedure for factorial designs to test for any significant effects due to type of design, size of advertisement, or interaction. Use α = 0.05.

Find the value of the test statistic for type of design. (Round your answer to two decimal places.)

Find the p-value for type of design. (Round your answer to three decimal places.)

p-value =

State your conclusion about type of design.

Because the p-value ≤ α = 0.05, type of design is not significant.Because the p-value > α = 0.05, type of design is not significant.    Because the p-value > α = 0.05, type of design is significant.Because the p-value ≤ α = 0.05, type of design is significant.

Find the value of the test statistic for size of advertisement. (Round your answer to two decimal places.)

Find the p-value for size of advertisement. (Round your answer to three decimal places.)

p-value =

State your conclusion about size of advertisement.

Because the p-value ≤ α = 0.05, size of advertisement is significant.Because the p-value > α = 0.05, size of advertisement is not significant.    Because the p-value > α = 0.05, size of advertisement is significant.Because the p-value ≤ α = 0.05, size of advertisement is not significant.

Find the value of the test statistic for interaction between type of design and size of advertisement. (Round your answer to two decimal places.)

Find the p-value for interaction between type of design and size of advertisement. (Round your answer to three decimal places.)

p-value =

State your conclusion about interaction between type of design and size of advertisement.

Because the p-value > α = 0.05, interaction between type of design and size of advertisement is not significant.Because the p-value ≤ α = 0.05, interaction between type of design and size of advertisement is significant.    Because the p-value > α = 0.05, interaction between type of design and size of advertisement is significant.Because the p-value ≤ α = 0.05, interaction between type of design and size of advertisement is not significant

A mail-order catalog firm designed a factorial experiment to test the effect of the size of a magazine advertisement and the advertisement design on the number of catalog requests received (data in thousands). Three advertising designs and two different-size advertisements were considered. The data obtained follow.

Use the ANOVA procedure for factorial designs to test for any significant effects due to type of design, size of advertisement, or interaction. Use α = 0.05.

Find the value of the test statistic for type of design. (Round your answer to two decimal places.)

Find the p-value for type of design. (Round your answer to three decimal places.)

p-value =

State your conclusion about type of design.

Because the p-value ≤ α = 0.05, type of design is not significant.Because the p-value > α = 0.05, type of design is significant.     Because the p-value > α = 0.05, type of design is not significant.Because the p-value ≤ α = 0.05, type of design is significant.

Find the value of the test statistic for size of advertisement. (Round your answer to two decimal places.)

Find the p-value for size of advertisement. (Round your answer to three decimal places.)

p-value =

State your conclusion about size of advertisement.

Because the p-value ≤ α = 0.05, size of advertisement is not significant.

Because the p-value ≤ α = 0.05, size of advertisement is significant.    

Because the p-value > α = 0.05, size of advertisement is not significant.

Because the p-value > α = 0.05, size of advertisement is significant.

Find the value of the test statistic for interaction between type of design and size of advertisement. (Round your answer to two decimal places.)

Find the p-value for interaction between type of design and size of advertisement. (Round your answer to three decimal places.)

p-value =

State your conclusion about interaction between type of design and size of advertisement.

Because the p-value > α = 0.05, interaction between type of design and size of advertisement is significant

.Because the p-value > α = 0.05, interaction between type of design and size of advertisement is not significant.     

Because the p-value ≤ α = 0.05, interaction between type of design and size of advertisement is not significant

.Because the p-value ≤ α = 0.05, interaction between type of design and size of advertisement is significant.

The scattering amplitude can be expressed in terms of the partial wave amplitude.

When  ,

(a) (5 points) What is scattering amplitude?

(b) (5 points) What is differential cross-section?

(c) (5 points) What is total cross-section?

When ,

(d) (5 points) What is scattering amplitude?

(e) (5 points) What is differential cross-section?

(f) (5 points) What is total cross-section?

The objective of a study was to see whether a recorded phone would be more effective than a mailed flyer in getting voters to support a certain candidate. The study assumes a significance level of α = 0.05.

The hypotheses are:

H0: p(voted to support candidate with flyer) – p(voted to support candidate with recorded phone call) = 0, and

HA: p(voted to support candidate with flyer) – p(voted to support candidate with recorded phone call) > 0.

(a) Explain what the p-value (0.027) indicates with respect to the observed sample statistic (and other, more extreme values of that statistic). Name the sample statistic involved as well as the p-value, and use the appropriate mathematical notation. (1-2 sentences.)

(b) Write a specific statement about your interpretation of the null hypothesis, given the p-value and the specified level of significance. Be sure to cite the p-value. Does the sample evidence available support the idea that phone calls are more effective than flyers? Explain.

(c) In the conclusion for (a) & (b), which type of error are we possibly making: Type I or Type II? Explain what this error means in this context.

(d) What if the p-value for the statistical test were actually 0.18 (and not 0.027)? Explain what the p-value (0.18) indicates with respect to the observed sample statistic (and other, more extreme values of that statistic). Name the sample statistic involved, report the p-value, and use the appropriate mathematical notation. (1-2 sentences.)

(e) Write a specific statement about your interpretation of the null hypothesis, given the p-value from (d), 0.18, and the specified level of significance. Be sure to cite the p-value and α. Does the sample evidence available support the idea that phone calls are more effective than flyers? Explain.

(f) In the conclusion from (e), which type of error are we possibly making: Type I or Type II? Describe what this error means in this context.