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18/05/2023

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24/02/2023

23/02/2023

Week 5 - Data science and tools
Exploratory Data Analysis
Write a short paragraph (< 100 words) on what you understand by exploratory data analysis.
Requirements for Discussion Assignments
Compose a well-developed post (< 100 words) that is comprehensive in answering questions posed on the discussion board
Complete the post by Thursday at 11:59 p.m. ET in the assigned week
Demonstrate integration of the required reading, other course materials, critical thinking, scholarly or peer-reviewed sources (as applicable), using either APA or MLA style, depending on the instructor/assignment specifications

Save text often when writing lengthy discussion board posts; work can be lost if the Internet connection drops or times out.
Write posts offline in a word-processing software first so that the text can be saved. Then copy and paste the text into the discussion thread. Be aware that the format may change when copy and paste is used.
Plagiarism: According to the Council of Writing Program Administrators, “Plagiarism occurs when a writer deliberately uses someone else’s language, ideas, or other original (not common-knowledge) material without acknowledging its source.”[1] Any of these activities constitutes plagiarism: directly copying and pasting from a source without citation; paraphrasing from a source or sources without citation; turning in a paper, or sections of a paper, known to be written by someone other than the student; unauthorized multiple submissions of the same work in more than one course; and turning in a purchased paper.
[1] Council of Writing Program Administrators. 2003. Defining and Avoiding Plagiarism: The WPA Statement on Best Practices.http://wpacouncil.org/files/wpa-plagiarism-statement.pdf

23/02/2023

week Discussion
Data Visualization
Write a short paragraph (< 100 words) on the importance of data visualization in analytics. Describe your most favorite feature of the ggplot2 library.
Requirements for Discussion Assignments
Compose a well-developed post (< 100 words) that is comprehensive in answering questions posed on the discussion board
Complete the post by Thursday at 11:59 p.m. ET in the assigned week
Demonstrate integration of the required reading, other course materials, critical thinking, scholarly or peer-reviewed sources (as applicable), using either APA or MLA style, depending on the instructor/assignment specifications

Save text often when writing lengthy discussion board posts; work can be lost if the Internet connection drops or times out.
Write posts offline in a word-processing software first so that the text can be saved. Then copy and paste the text into the discussion thread. Be aware that the format may change when copy and paste is used.
Plagiarism: According to the Council of Writing Program Administrators, “Plagiarism occurs when a writer deliberately uses someone else’s language, ideas, or other original (not common-knowledge) material without acknowledging its source.”[1] Any of these activities constitutes plagiarism: directly copying and pasting from a source without citation; paraphrasing from a source or sources without citation; turning in a paper, or sections of a paper, known to be written by someone other than the student; unauthorized multiple submissions of the same work in more than one course; and turning in a purchased paper.
[1] Council of Writing Program Administrators. 2003. Defining and Avoiding Plagiarism: The WPA Statement on Best Practices.http://wpacouncil.org/files/wpa-plagiarism-statement.pdf

23/02/2023

Week 4 - Data science and tools
Write a program using R-Markdown answering questions listed below under Exercises immediately after each section. For clarity, make sure to give an appropriate title to each section.
Sections: Introduction, Prerequisites, First Steps, The mpg Data Frame, Creating a ggplot, A Graphing Template
Exercises: 1, 2 (Read it as mpg and not mtcars), 3,4, 5
Sections: Aesthetic Mappings
Exercises: 1, 2, 3, 5, 6
Sections: Common Problems, Facets
Exercises: 3,4, 5
Sections: Geometric Objects
Exercises: 2, 5, 6
Sections: Statistical Transformations
Exercises: 1, 2, 5
Sections: Position Adjustments
Exercises: 1, 2, 3
Sections: Coordinate Systems
Exercises: 1, 3, 4
Replicate and run every line of code listed in the above sections in R studio on your own machine
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04/12/2022

Week 3 - Data science and tools
Data Transformation Techniques
Write a program using R-Markdown solving and answering questions listed below under Exercises immediately after each section. For clarity, make sure to give an appropriate title to each section.
Sections: Introduction, Prerequisites, nycflights13, dplyr Basics, Filter Rows with filter(), Comparisons, Logical Operators, Missing Values.
• Exercises: 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 3
Sections: Arrange Rows with Rows()
• Exercises: 1 and 2
Sections: Select columns with select()
• Exercises: 3
Sections: Add new variables with mutate(), Useful Creation Functions
• Exercises: 2, 4, 5
Sections: Grouped summaries with summarize(), Combining multiple operations with the Pipe, Missing Values, Counts, Useful Summary Functions, Grouping by Multiple Variables, Ungrouping
• Exercises: 5, 6
Sections: Grouped Mutates (and Filters)
• Exercises: 2, 4, 7

Replicate and run every line of code listed in the above sections in R studio on your own machine
or this assignment solution needed please contact us at University Solutions Hub or WhatsApp: +91 86888 96472
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04/12/2022

Week 2 assignment – Data science and tools
5.3
1. 1. How could you use arrange() to sort all missing values to the start? (Hint: use is.na()).
2. Sort flights to find the most delayed flights. Find the flights that left earliest.
3. Sort flights to find the fastest (highest speed) flights.
4. Which flights travelled the farthest? Which travelled the shortest?
5.4
1. Brainstorm as many ways as possible to select dep_time, dep_delay, arr_time, and arr_delay from flights.
2. What happens if you include the name of a variable multiple times in a select() call?
3. What does the any_of() function do? Why might it be helpful in conjunction with this vector?
5.7
1. Refer back to the lists of useful mutate and filtering functions. Describe how each operation changes when you combine it with grouping.
2. Which plane (tailnum) has the worst on-time record?
3. What time of day should you fly if you want to avoid delays as much as possible?
4. For each destination, compute the total minutes of delay. For each flight, compute the proportion of the total delay for its destination.
5. Delays are typically temporally correlated: even once the problem that caused the initial delay has been resolved, later flights are delayed to allow earlier flights to leave. Using lag(), explore how the delay of a flight is related to the delay of the immediately preceding flight.
6. Look at each destination. Can you find flights that are suspiciously fast? (i.e. flights that represent a potential data entry error). Compute the air time of a flight relative to the shortest flight to that destination. Which flights were most delayed in the air?
7. Find all destinations that are flown by at least two carriers. Use that Linformation to rank the carriers.
8. For each plane, count the number of flights before the first delay of greater than 1 hour.
9. Brainstorm at least 5 different ways to assess the typical delay characteristics of a group of flights. Consider the following scenarios:
1. A flight is 15 minutes early 50% of the time, and 15 minutes late 50% of the time.
2. A flight is always 10 minutes late.
3. A flight is 30 minutes early 50% of the time, and 30 minutes late 50% of the time.
4. 99% of the time a flight is on time. 1% of the time it’s 2 hours late.
Which is more important: arrival delay or departure delay?
10. Come up with another approach that will give you the same output as not_cancelled %>% count(dest) and not_cancelled %>% count(tailnum, wt = distance) (without using count()).
11. Our definition of cancelled flights (is.na(dep_delay) | is.na(arr_delay) ) is slightly suboptimal. Why? Which is the most important column?
12. Look at the number of cancelled flights per day. Is there a pattern? Is the proportion of cancelled flights related to the average delay?
13. Which carrier has the worst delays? Challenge: can you disentangle the effects of bad airports vs. bad carriers? Why/why not? (Hint: think about flights %>% group_by(carrier, dest) %>% summarise(n()))
14. What does the sort argument to count() do. When might you use it?

Replicate and run every line of code listed in the above sections in R studio on your own machine
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16/11/2022

Week 2 Assignment

Wk 2 Assignment: Chapter 5 from the Hands-on Programming with R
Submit a fully documented and executed R-Markdown ("knit" as pdf / word or html) file R program replicating the code from the following sections of chapter 5:
5.1, 5.3, 5.4, 5.7, 5.8
Replicate and run every line of code listed in the above sections in R studio on your own machine
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16/11/2022

test

16/11/2022

Week 1 Assignment – Data Science Tools

Introduction to R on DataQuest.com
There are two parts to this assignment:
1) Finish the first course Introduction to R on DataQuest.com. The main objective of this short course is to introduce the foundational concepts and the data structures in R. The course is divided into six missions, you are required to finish the below (which are free of cost)
as follows:
• Introduction to programming in R
• Working with Vectors

The assignment is to take screenshots of the completion of the three missions, paste them in a word document and submit.
2) Open R-Studio on your computer and start a new script. Now type getwd() in the script and run it. Take the screenshot including the console screen that contains the version number and the working directory. Paste the screenshot in a word document and submit it. Here is the expected output for this task:

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15/11/2022

Module 2, Week 4, gretl problem set 4 - McDaniel
Solution Id: USHMD00011 & Price: $50 USD
Use gretl to answer the following questions.
Module 2, Week 4, gretl problem set 4

Use gretl and the dataset “Children of Immigrants Survey” to answer the following questions. Use a 5% level of significance for all hypothesis tests.

1. What is the average age of children in this dataset? What is the minimum and maximum age?

2. Create a new variable named gotoschool that equals 0 if the survey respondent does not attend school and equals 1 if the survey respondent goes to high school or college.

3. Test the hypothesis that mean income differs between households where the child goes to school and households where the child does not go to school.

4. Test the hypothesis that mean income differs between households where the child is a citizen and household where the child is not a citizen.

5. Test the hypothesis that the variables gotoschool and s*x are related (i.e. not independent).

6. Test the hypothesis that mean household income differs across mother’s place of birth.

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15/11/2022

Module 2, Week 4, paper and pencil - McDaniel
Solution Id: USHMD00010 & Price: $50 USD
Use gretl to answer the following questions.
Module 2, Week 4, Paper and Pencil Assignment 4

1. You have the following random samples containing information on annual income (measured in thousands) and marital status for individuals living in urban and rural areas.

Urban annual income Urban Married Rural annual income Rural Married
33 Yes 22 Yes
56 Yes 81 Yes
88 No 19 No
43 No 26 Yes
28 Yes 38 Yes
42 Yes 101 No
105 No 31 Yes
90 Yes 29 No
21 Yes 35 Yes
47 No 39 Yes
57 No 66 Yes
19 No 99 No
118 Yes 18 Yes
77 Yes 92 Yes
50 Yes 65 Yes
31 No 48 No

The samples have the following standard deviations:
s_Urban = 30.41 s_Rural = 29.26

Test the hypothesis that there is a difference in mean income between people living urban areas and rural areas. Use a 5% level of significance.

Test the hypothesis that there is a difference in the proportion of people married between those living in urban areas and those living in rural areas. Use a 5% level of significance.
2. The following table shows the expected and observed distribution of bachelor’s degrees held among a random sample of 100 full-time employees at a financial services firm.

Degree Expected Observed
Business 40 45
Economics 20 15
Accounting/Finance 20 28
Mathematics 10 2
Other 10 10

Conduct a hypothesis test to determine if the observed distribution of degrees fits the expected distribution. Use a 5% level of significance.


3. The following table is a cross-tabulation of home ownership and location of residence.

Own home Do not own home
Urban 31 59
Rural 29 41

Conduct a hypothesis test to determine if home ownership and location of residence are independent random variables. Use a 5% level of significance.
4. You have the following random sample of the number of daily calls to police stations in the cities of Baltimore, Washington DC, and Pittsburgh.

Baltimore Washington DC Pittsburgh
862 555 755
766 664 763
608 610 802
911 709 712
980 612 765
888 589 679
812 649 789
678 700 714
765 707 599
709 579 671
808 622 683

Test the hypothesis that the mean number of daily calls differs across the three cities. The total sum of squares (SST) is equal to 330,463.64. Use a 5% level of significance.
What is the difference between ANOVA with two categories and a two-sample t-test for differences in means?
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