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Big Data Analytics in Business Question 1 Prices for round-shaped

November 3, 2021

Big Data Analytics in Business

Question 1

Prices for round-shaped diamonds taken from an online trader are given in Table 1. Weight is the independent variable x and price is the dependent variable y. We will analyze this data in the computer. For Windows users, I suggest using Excel (Microsoft Office). For Mac users, I suggest using Numbers (iWork ’09). For Excel and Numbers, enter chart into the Help menu to find out how to create charts, and enter trend into the Help menu to find detailed instructions on creating and manipulating trendlines for data series.

In Excel, you can adjust the precision in the trendline equations by clicking on the trendline equation.

Table 1. Prices for round-shaped diamonds Weight (carats)

Weights

Price

0.5

2790

0.6

3191

0.7

3694

0.8

4154

0.9

5018

1.0

5898

The data into a spreadsheet (including the header Weight and Price).
Create a scatter plot of the data. Note that the scatter plot for this data appears roughly linear.
Compute the linear regression equation (trendline). The trendline will be y = ax + b for some values of a and b.
Adjust the minimum/maximum values on the axes of the graph to 0.4 and 1.2 for the weight and 2000 and 6500 for the price to make the graph look nice.

Question 2

An article discussed the number of Internet search results that Web surfers typically scan before selecting one. The following table represents the results for a sample of 2369 people.

Number of Internet Search Results Scanned

Percentage (%)

A few search results

First page of search results

First two pages

First three pages

More than first three pages

Construct a bar chart and a pie chart.
Which graphical method do you think is best for portraying these data?
What conclusion can you reach concerning how people scan Internet search results?

Question 3

Consider an example of a retail store chain that wants to optimize its products’ prices for boosting its revenue. The store chain has thousands of products over hundreds of outlets, making it a highly complex scenario. Once you identify the store chain’s objective, you find the data you need, prepare it, and go through the Data Analytics lifecycle process.

You observe different types of customers, such as ordinary customers and customers like contractors who buy in bulk. According to you, treating various types of customers differently can give you the solution. However, you don’t have enough information about it and need to discuss this with the client team.

How would you apply the data analytics life cycle to this problem?
What would business understanding look like?
What would data understanding look like?

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