Please answer all the questions?

Please answer all the questions?Please 50-100 words for each question

Analysis:

1.What set of functions would be used to group the internal data set iris by the column Species and summarize with the average of the column Petal.Width? What does the result look like?

2.What type of data is a factor?

3.If I were to change the data type of a field to factor how would that function call be used? Provide your answer as if you were typing directly into the console.

4.When using filter from the dplyr library, does this access the observations or the variables?

5.What is difference mentioned in the lecture, between the use of the functions read_csv() and read.csv()?

6.What does a numeric data type represent?

7.What is the statistical analysis?

8.What would this collection of functions do in R? You can enter it directly into RStudio’s console, if you are not sure. You will have to call the library dplyr.

iris %>% group_by(Species) %>% summarize(Quantity = n())

9.What function in R will provide a summary of a data frame? Do you need to call a library to use it?

10.What is one way to determine the names of the columns in a data frame?

11.When you want to change some parts of a program or delete some parts of a program, what is the appropriate method to do so?

12. Filtering data is sometimes problematic. Filter the data in the data set iris for the factors setosa and versicolor from the column Species, group by Species and summarize by the number of rows.

13. Is a data frame a collection of lists or vectors?

14. How do you add a column to a data frame? What requirements does the new column have to meet in order to add it?

15. When using select from the dplyr library, does this access the observations or the variables?

16. What is a row in a data frame otherwise known as?

BI:

Chapter 3:

1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain.

2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum?

3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data?

4. What are the most common metrics that make for analytics-ready data?

Chapter 4:

1. Define data mining. Why are there many names and definitions for data mining?

2. What are the main reasons for the recent popularity of data mining?

3. Discuss what an organization should consider before making a decision to purchase data mining software.

4. Distinguish data mining from other analytical tools and techniques.

5. Discuss the main data mining methods. What are the fundamental differences among them?

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