47 Questions
What is the purpose of validation metrics in statistical analysis?
To measure the quality of the analysis
Which statistical output is used to indicate the relationship between variables in regression analysis?
Correlation coefficient (R)
Which validation metric is used to assess the precision of the regression model's predictions?
Standard deviation (STDEV)
What is the purpose of considering both statistical outputs and validation metrics when conducting an analysis?
To evaluate the robustness of the data and interpret the analysis results
What is the formula for calculating Lift%?
Number of additional conversions / Number of conversions without ads $\times$ 100
What does the Lift % indicate?
How much the ads increased the rate at which people converted
What type of join returns records that have matching values in both tables?
Inner join
What is the purpose of data visualization?
To communicate relationships among the represented data to viewers
Which type of visualization is a graphic representation of data achieved through the use of a systematic mapping between graphic marks and data values?
Bubble chart
What is the purpose of a scatter plot?
To show the relationship between two variables
What does the Confidence percentage represent in the context of conversion lift?
How confident Meta is that the ads caused conversion lift
In SQL, which join returns all records when there is a match in either left or right table?
Full outer join
What is the purpose of a line graph?
To show trends or changes over time
What is the main purpose of a bar chart?
To compare categories of data
What type of join returns all records from the left table and the matched records from the right table?
Left join
What is the purpose of a heat map?
To visualize data using colors
What does the positional model consider in distributing conversion credit?
First and last touchpoints
What does the time decay model give increasing credit to?
Touchpoints closer in time to the conversion
How many configurations of the time decay model are offered?
2
What is the data-driven attribution model based on?
Estimated incremental impact
What needs to be reconciled before data analysis when comparing results from campaigns between Meta Ads Manager and Google Ads Manager?
Attribution windows
What are included as summary statistics for data analysis?
Mean, median, mode, standard deviation, and more
Which attribution model gives 100% of the credit for a conversion to the first click on the conversion path?
Single-touch attribution model
Which attribution model gives 100% of the credit for a conversion to the last click, visit, impression, or view in a conversion path?
Single-touch attribution model
Which attribution model gives each touchpoint equal credit for a conversion, regardless of its position in the conversion path?
Linear or Even attribution model
Which attribution model gives a specific percentage of credit to the first and last touchpoints in a conversion path, with the remaining credit distributed evenly across all intermediate touchpoints?
Positional model
Which attribution model uses machine learning to assign credit to touchpoints based on their calculated contribution to the conversion?
Data-driven attribution model
Which attribution model gives more credit to touchpoints closer to the conversion event?
Time decay model
Which attribution model may oversimplify conversion paths that rely on middle- and lower-funnel activity?
First click or visit model
Which attribution model considers more than one interaction with a media channel?
Multi-touch attribution model
Which attribution model gives 100% of the credit for a conversion to only one touchpoint?
First click or visit model
Which attribution model gives more credit to touchpoints closer to the conversion event?
Time decay model
Which attribution model considers the full conversion path and can inform business decisions?
Multi-touch attribution model
Which attribution model uses machine learning to assign credit to touchpoints based on their calculated contribution to the conversion?
Data-driven attribution model
Match the following summary statistics with their descriptions:
Range = The difference between the maximum and minimum values in a dataset Minimum = The smallest value in a dataset Sum = The result of adding up all the values in a dataset Count = The total number of values in a dataset
Match the following SQL join types with their descriptions:
Inner join = Returns records that have matching values in both tables Left join = Returns all records from the left table and the matched records from the right table Right join = Returns all records from the right table and the matched records from the left table Full outer join = Returns all records when there is a match in either left or right table
Match the following data visualization types with their descriptions:
Heat map = A graphic representation of data using colors to communicate relationships among data values Bubble chart = A type of chart that displays three dimensions of data Line graph = A type of chart that displays information as a series of data points called 'markers' connected by straight line segments Bar chart = A chart with rectangular bars of lengths proportional to the values they represent
Match the attribution model with its description:
Positional model (30%) = Considers the first and last touchpoints, distributing remaining credit evenly among all other touchpoints Time decay model (1 day half-life) = Gives increasing credit to touchpoints closer in time to the conversion with smaller lookback window Data-driven attribution model = Assigns fractional credit based on estimated incremental impact Time decay model (7 day half-life) = Gives increasing credit to touchpoints closer in time to the conversion with higher lookback window
Match the statistical analysis concept with its description:
Data input reconciliation = Involves considering data input, time frames, attribution window, and measurement methodology Summary statistics = Include mean, median, mode, standard deviation, and more Hypothesis-based statistical analysis = Requires different statistical analyses based on the hypothesis Measurement solutions reconciliation = Reconciling differences across different measurement solutions involves considering data input, time frames, attribution window, and measurement methodology
Match the data visualization concept with its description:
Line graph = Represents data using a series of data points connected by straight line segments Scatter plot = Uses dots to represent values for two different numeric variables Heat map = Visual representation of data where values are depicted by colors Bar chart = Uses rectangular bars to represent data values
Match the following statistical outputs with their descriptions:
Regression coefficients = Indicate the relationship between variables in regression analysis R-squared = Measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s) Slope and intercept = Represent the slope and intercept of the regression line in a simple linear regression model Correlation coefficient (R) = Measures the strength and direction of the linear relationship between two variables
Match the following validation metrics with their descriptions:
Log-likelihood = Assesses the likelihood of the observed data given the parameters of a statistical model Standard deviation (STDEV) = Measures the amount of variation or dispersion of a set of values P-values = Indicate the probability of obtaining a test statistic at least as extreme as the one that was actually observed Adjusted R-squared = Takes into account the number of independent variables in a regression model to provide a more accurate measure of the proportion of the variance in the dependent variable explained by the independent variable(s)
Match the following statistical outputs with their primary purpose:
Mean, median, mode = Summary statistics for central tendency and distribution of data Standard errors (SE) = Estimates the accuracy of sample statistics in representing the population parameters Confidence Intervals = Provide a range of values that is likely to contain the population parameter with a certain degree of confidence Mean error = Indicates the average difference between the observed and true values in a data set
Match the following concepts with their relevance to statistical analysis:
Validation metrics = Assess the reliability and accuracy of statistical models and outputs Statistical outputs = Provide quantitative results and relationships to support data interpretation and decision-making Regression coefficients = Show the impact of independent variables on the dependent variable in regression analysis Sample size = Determines the precision and generalizability of statistical findings based on the amount of data available
Match the attribution model with its description:
Single-touch attribution model = Gives credit to only one touchpoint First click or visit model = Gives 100% of the credit for a conversion to the first click on the conversion path Last touch model = Gives 100% of the credit for a conversion to the last click, visit, impression, or view in a conversion path Multi-touch attribution models = Consider more than one interaction with a media channel
Match the attribution model with its characteristic:
Even credit model = Gives each touchpoint equal credit for a conversion, regardless of its position in the conversion path Positional model = Gives a specific percentage of credit to the first and last touchpoints in a conversion path, with the remaining credit distributed evenly across all intermediate touchpoints Data-driven attribution model = Uses machine learning to assign credit to touchpoints based on their calculated contribution to the conversion Time decay model = Gives more credit to touchpoints closer to the conversion event
Match the attribution model with its consideration:
First-click and last-touch models = May oversimplify conversion paths that rely on middle- and lower-funnel activity Multi-touch models = Consider the full conversion path, and can inform business decisions Data-driven attribution model = Uses machine learning to assign credit to touchpoints based on their calculated contribution to the conversion Time decay model = Gives more credit to touchpoints closer to the conversion event
Study Notes
Attribution Models Overview
- Single-touch attribution models give credit to only one touchpoint
- The First click or visit model gives 100% of the credit for a conversion to the first click on the conversion path
- Last touch model gives 100% of the credit for a conversion to the last click, visit, impression, or view in a conversion path
- Multi-touch attribution models consider more than one interaction with a media channel
- Even credit model gives each touchpoint equal credit for a conversion, regardless of its position in the conversion path
- Positional model gives a specific percentage of credit to the first and last touchpoints in a conversion path, with the remaining credit distributed evenly across all intermediate touchpoints
- Data-driven attribution model uses machine learning to assign credit to touchpoints based on their calculated contribution to the conversion
- Time decay model gives more credit to touchpoints closer to the conversion event
- Data-driven attribution model uses machine learning to assign credit to touchpoints based on their calculated contribution to the conversion
- Time decay model gives more credit to touchpoints closer to the conversion event
- First-click and last-touch models may oversimplify conversion paths that rely on middle- and lower-funnel activity
- Multi-touch models, such as even credit and positional, consider the full conversion path, and can inform business decisions
Attribution Models Overview
- Single-touch attribution models give credit to only one touchpoint
- The First click or visit model gives 100% of the credit for a conversion to the first click on the conversion path
- Last touch model gives 100% of the credit for a conversion to the last click, visit, impression, or view in a conversion path
- Multi-touch attribution models consider more than one interaction with a media channel
- Even credit model gives each touchpoint equal credit for a conversion, regardless of its position in the conversion path
- Positional model gives a specific percentage of credit to the first and last touchpoints in a conversion path, with the remaining credit distributed evenly across all intermediate touchpoints
- Data-driven attribution model uses machine learning to assign credit to touchpoints based on their calculated contribution to the conversion
- Time decay model gives more credit to touchpoints closer to the conversion event
- Data-driven attribution model uses machine learning to assign credit to touchpoints based on their calculated contribution to the conversion
- Time decay model gives more credit to touchpoints closer to the conversion event
- First-click and last-touch models may oversimplify conversion paths that rely on middle- and lower-funnel activity
- Multi-touch models, such as even credit and positional, consider the full conversion path, and can inform business decisions
Test your knowledge of attribution models with this overview quiz. Explore single-touch and multi-touch attribution models, including first-click, last-touch, even credit, positional, data-driven, and time decay models. Understand the impact of different attribution models on conversion paths and business decisions.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
