## 47 Questions

What is the purpose of simple linear regression (SLR) model?

What distinguishes different types of models?

What fundamental skill is essential for a marketer in the context of SLR?

What does a linear regression model look at?

Which of the following is a requirement for the independent variable in SLR?

What type of variables are used in Simple Linear Regression (SLR)?

Which of the following is a key assumption for the data in SLR?

What is a crucial step before employing SLR in marketing analysis?

How are scatter plots and trend lines created in Tableau for SLR?

What can be done once the data is plotted and the trend line is drawn in SLR?

What does a SLR model aim to provide in marketing analysis?

What insights can the SLR model provide in marketing analysis?

How can marketers ensure the suitability and accuracy of employing SLR in their marketing analysis?

What is the primary purpose of employing SLR in marketing analysis?

Which assumption refers to the independence of observations in SLR?

What does homogeneity of variance assumption in SLR refer to?

What does the assumption of normality in SLR refer to?

What is the primary purpose of cluster analysis, such as K-means clustering, in marketing analytics?

What distinguishes cluster analysis models, like K-means clustering, from other modeling techniques?

What is the purpose of market segmentation in the context of cluster analysis in marketing analytics?

What does cluster analysis, particularly K-means clustering, aim to achieve with the data in marketing analysis?

What is the minimum sample size requirement for data points per grouping in K-means cluster analysis?

What does sphericity refer to in K-means clustering?

What is the purpose of equal prior probability assumption in K-means cluster analysis?

What are the basic steps for running a K-means cluster analysis?

What visualization tool is mentioned for conducting K-means cluster analysis?

In Tableau, how is the scatter plot for K-means cluster analysis created?

What is an interesting observation from the cluster analysis mentioned in the text?

What do the assumptions for K-means cluster analysis serve as?

What type of variables must the data include for K-means cluster analysis?

What does the homogeneity of variance assumption in K-means clustering imply?

What is the primary purpose of time series analysis?

What type of variable is required for time series analysis?

What is a key data assumption for running a time series analysis?

What does a time series analysis model aim to predict?

What is the minimum sample size requirement for quarterly data measurements in time series analysis?

What does the stationarity assumption in time series analysis entail?

What is the primary purpose of creating a time series graph in time series analysis?

What does the 'Forecast' option in Tableau do in time series analysis?

What is the minimum sample size requirement for annual data measurements in time series analysis?

What does the dependence assumption in time series analysis require?

What is the purpose of the 'rows shelf' in Tableau for time series analysis?

What does the 'columns shelf' in Tableau for time series analysis do?

What is the purpose of the 'Forecast options' in Tableau for time series analysis?

What does the 'Forecast' line represent in Tableau for time series analysis?

What does the 'Analytics' tab in Tableau do for time series analysis?

What is the minimum sample size requirement for daily data measurements in time series analysis?

## Summary

Simple Linear Regression Model in Marketing Analysis

- Simple linear regression (SLR) is used to evaluate the relationship between independent and dependent variables in marketing analysis.
- The SLR model requires at least one quantitative independent variable and one quantitative dependent variable.
- The data for SLR must meet five assumptions: minimum sample size of 20, linearity, homogeneity of variance, normality, and independence.
- These assumptions ensure the accuracy of the results obtained from the SLR analysis.
- Before employing SLR in marketing analysis, it is crucial to verify that all the variable requirements and data assumptions are met.
- The basic steps for running SLR include creating a scatter plot for the independent and dependent variables and drawing a trend line to represent the data.
- In Tableau, a popular data visualization tool, the scatter plot and trend line can be created by dragging the variables to the appropriate shelves and selecting the "Trend Lines" option.
- Once the data is plotted and the trend line is drawn, predictions can be made using the trend line.
- An example visualization of a SLR conducted using Tableau showed the prediction that 60 Adware Clicks will yield around 6 Adware Conversions.
- The SLR model can be a valuable tool for making predictions in marketing analysis, particularly when evaluating the relationship between ad clicks and conversions.
- This type of analysis can provide insights into the effectiveness of marketing strategies and guide decision-making for optimizing ad campaigns.
- By following the characteristic purpose, variable requirements, and data assumptions, marketers can ensure the suitability and accuracy of employing SLR in their marketing analysis.

Key Points on K-means Cluster Analysis

- K-means clustering is the default method in various software programs, including Tableau, and involves calculating specific points within data to create groups by minimizing the distance from them.
- For cluster analysis, the data must include at least one quantitative independent variable and one quantitative dependent variable.
- The data assumptions for K-means cluster analysis include a minimum sample size of 50 data points per grouping, sphericity, homogeneity of variance, and equal prior probability.
- Sphericity in K-means clustering means that the groupings fall into a rounded area when plotted, forming rounded clusters around central data points.
- Homogeneity of variance assumption in K-means clustering implies roughly the same amount of variance in independent and dependent variables.
- Equal prior probability assumption in K-means clustering means each grouping should have roughly the same likelihood of occurring within the data.
- These assumptions serve as a checklist to determine the suitability of K-means cluster analysis for data analysis.
- Once determined suitable, the basic steps for running a K-means cluster analysis involve creating a scatter plot for the independent and dependent variables and then clustering the data into the desired groups.
- In Tableau, the scatter plot for K-means cluster analysis is created by placing the independent variable on the X-axis and the dependent variable on the Y-axis, followed by clustering the data through the "Analytics" tab.
- A sample visualization for a cluster analysis using Tableau involves analyzing customer spending data to define groups based on spending, as shown in the scatter plot.
- An interesting observation from the analysis is that the group of lowest spenders also tends to have the highest number of clicks.
- The text provides practical guidance and examples for conducting K-means cluster analysis using Tableau, emphasizing its application in marketing analysis.

## Description

Linear Regression, Cluster Analysis and Time Series