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Digital Analytics & Regression Cognitive Class Exam Quiz Answers

Digital Analytics & Regression Cognitive Class Certification Answers

Question 1: You are a newly-hired analyst at a small tech startup in a big metropolitan city. In your first team meeting, and one week before the scheduled product launch, the Chief Executive Officer (CEO) and the Chief Marketing Officer (CMO) have a heated argument about what to call the product.

The CEO has done a quick search on Google Trends and found that ‘Analytics’ is, by far, a more popular search term than ‘Data Science’ or ‘Data Scientist’. The CMO, who has some experience in SEM from his work at another tech company, has a gut feeling that ‘Data Scientist Workbench’ will bring the right target group to the new product. The two executives ask you to weigh in.

Based on the Case Study you have read, which of the following is the most suitable data set to start an analysis to help the executives decide which term to include in the product name?

• Open Data
• Search Engine Trends Data
• Survey Data

Question 2: Based on the Case Study you have read, how many domains available for purchase?

• 1
• 2
• 3
• 4

Question 3: Based on the Case Study you have read, what was the ‘Expert Tip’?

• Don’t offer your opinion to senior executives if you are new to a company.
• Your first assumption is always the correct one.
• If it can’t be measured, it doesn’t exist.
• When solving business problems with data, be curious.

Question 1: How many rows does the function head(dataset) return?

Question 2: What does head(case_table\$WeekID) do?

• returns the first six rows of case_table and all of its columns
• returns all the data in case_table
• returns the first six items of the WeekID column in case_table
• returns the entire WeekID column of data in case_table

Question 3: TRUE or FALSE? The function str() can be used to tell you the number of rows and columns in a dataframe, and some other characteristics of the data.

• True
• False

Question 1: Which search term has the most number of searches?

• Machine Learning
• Data Scientist
• Analytics
• Data Science
• Regression

Question 2: A correlation score of 0.9 between variables X and Y indicate which of the following?

• As X increases, Y decreases.
• There is a weak positive correlation between X and Y.
• X and Y are opposites of each other.
• A strong positive correlation.
• X causes Y.

Question 3: In the R script file, which command can you use to plot line graphs?

• makePlot( )
• createGraph( )
• plot( )
• linegraph( )
• graph( )

Question 1: What is the correlation between searches for ‘data science’ and ‘data scientist’? Give the numeric answer to two decimal places.

Question 2: Running a linear model in R for a dataset results in the formula: Y = 5.0 + 3.0X. Which of the following is true?

• Y will always be positive.
• X is 3 times as large as Y.
• There is a negative correlation between X and Y.
• As X increases, Y decreases.
• When X is equal to 5, the model predicts that Y will equal 20.

Question 3: In R, what symbol or character do we use to specify a column in a table?

• ?
• ==
• /
• \$

Question 1: What is the correlation (as a %) between the two search terms in the exam data set? For example, if the correlation is 0.5, enter 50 below.

QUESTION 2: What is the mean of the values for Hadoop searches? Your answer should include two decimal places (for example, 10.01).

QUESTION 3: What is the R Squared for the Regression model in the exam data set? The answer format must be a percentage with no decimal places. For example, if the R^2 value is 0.5, then you should write 50 below.

QUESTION 4: What weeks correspond to WeekID 16?

• 2004-06-06 – 2004-06-12
• 2004-01-25 – 2004-01-31
• 2004-04-18 – 2004-04-24
• 2004-08-15 – 2004-08-21

QUESTION 5: Which R function creates a linear model?

• lm ( )
• abline ( )
• regression ( )
• summary ( )

QUESTION 6: Determine the formula for your linear model. Using the linear model you have just created to predict search index values: if the search index for ‘Hadoop’ is 35, what is the predicted corresponding search index for ‘Big Data’ according to your model?

• 88
• 19
• 45
• 30

QUESTION 7: Generally speaking, the simple linear model created from these two search terms fits the data well, TRUE/FALSE?

• TRUE
• FALSE

QUESTION 8: In the Linear Model, ‘Hadoop’ is the Y variable, TRUE/FALSE?

• TRUE
• FALSE

QUESTION 9: Having now gone through the exercises in this course, and going back to the debate between the CEO vs. CMO, what product name was chosen as the most suitable for the target audience?

• Analytics Workbench
• Data Scientist Workbench

Introduction to Digital Analytics & Regression

Digital analytics refers to the collection, measurement, analysis, and interpretation of digital data from various online sources such as websites, mobile applications, social media platforms, and online advertising campaigns. Its primary goal is to understand user behavior and optimize digital strategies to achieve business objectives.

Regression analysis is a statistical technique used in digital analytics to explore the relationship between one or more independent variables (predictors) and a dependent variable (outcome). It helps in understanding how changes in the independent variables affect the dependent variable. Regression analysis is widely used in digital analytics to predict outcomes, identify trends, and uncover patterns in data.

Here’s a basic introduction to both concepts:

1. Digital Analytics:
• Data Collection: Digital analytics begins with the collection of data from various digital sources. This includes website traffic, user interactions, conversions, social media engagement, and more.
• Measurement: Once the data is collected, it needs to be organized and measured using key performance indicators (KPIs) relevant to the business goals. Common KPIs include website traffic, conversion rates, bounce rates, click-through rates, etc.
• Analysis: After measurement, the data is analyzed to gain insights into user behavior, trends, and patterns. This involves techniques such as segmentation, cohort analysis, funnel analysis, and more.
• Interpretation: Finally, the analyzed data is interpreted to make data-driven decisions and optimize digital strategies. This could involve A/B testing, personalization, content optimization, and other tactics to improve performance.
2. Regression Analysis:
• Understanding Relationships: Regression analysis helps in understanding the relationship between one or more independent variables and a dependent variable. It answers questions like “How does changing X affect Y?”
• Types of Regression: There are different types of regression analysis, including linear regression, logistic regression, polynomial regression, etc. Each type is suitable for different types of data and research questions.
• Model Building: Regression analysis involves building a mathematical model based on the relationship between variables. The model is then used to make predictions or understand the impact of changes in variables.
• Assumptions: Regression analysis relies on certain assumptions, such as linearity, independence of errors, homoscedasticity, etc. It’s important to check these assumptions to ensure the validity of the analysis.

In digital analytics, regression analysis can be used for various purposes such as predicting website traffic based on advertising spend, analyzing the impact of different marketing channels on conversions, understanding the factors influencing user engagement, and more. It’s a powerful tool for extracting actionable insights from digital data.