
Types of business data: quantitative and qualitative; cross-sectional and time series
Predicting Apartment price
Regression as a three-in-one tool
Modelling customer churn (binary variable)
Time series - forecasting Sales
Why correlation is not causation
Installing Analysis ToolPak in Excel
Running your first regression
Predicting price of a house
Estimating linear regression in Excel
Interpreting model output
Practical and statistical significance
1. Identifying the potential relationship between variables
2. Providing additional variables for analysis
3. Describing steps in CRISP-DM methodology related to the business problem
Your data at a glance:
Descriptive statistics
Correlations
Histograms
1. Using Claude.AI for quick data analysis
2. Checking correlations in the data
3. Attempting a histogram of sale price
Types of qualitative variables
Linear regression with binary outcome
Alternatives to Linear Probability Model
Estimating linear probability model
Interpreting model output
Can a probability be negative?
Types of error messages
Using function COUNT to identify non-numeric entries
Filtering and sorting to avoid error messages
Time series applications in business
Seasonal effects
Trend
Total Vehicle Sales from FRED
Accounting for seasonal effects
Adding linear trend
1. Interpreting model results
2. Identifying significant and insignificant variables
3. Predicting vehicle sales for the next 3 months
Serial dependence and lagged variables
Exponential smoothing
Moving average
Creating lagged variables in Excel
Choosing a damping factor in exponential smoothing
Applying moving average model
1. Getting data from external sources including the internet
2. Introduction to Power Query editor in MS Excel
Bestseller course with students from 142 countries updated in 2024 with modern AI tools!
Do you still use old-fashioned approaches to learn data analytics that take tens of hours to do the basic multivariate analysis? Do you want to speed up your learning of business data applications with the help of modern AI tools such as ChatGPT and Claude AI? Then this course is for you!
There are many great data courses on Udemy which sell in thousands of copies. However, busy MBA students and professionals might not have time to learn programming to run econometric models in Python, R, Stata or Eviews. MS Excel, on the other hand, is a familiar and accessible tool which can be used for advanced data analytics.
Take this course and you will:
Harness the full potential of Excel's analytical capabilities for multivariate analysis
Conduct in-depth correlation analyses to identify and quantify variable interactions
Build robust regression models to forecast future trends and uncover causal relationships
Transform raw data into actionable insights through advanced statistical modeling
Leverage Excel Analytics to visualize complex multivariate data and communicate findings effectively
Whether you're a data analyst, business professional, researcher, or student, this course will equip you with the skills to tackle even the most complex datasets with confidence. Unleash the true power of your data and elevate your analytical prowess to new heights.
Enroll now and embark on a journey to become a multivariate analysis master, unlocking a world of opportunities in data-driven decision-making and strategic planning.
In this concise course I have carefully selected the most important econometric tools and techniques that are really needed to understand the complicated relationships that exist in your data. Linear regression is the absolute must for data analysis and Analysis ToolPak is a simple yet powerful feature available in MS Excel. The key concepts used in this course have been distilled based on my experience with many groups of business students that I have taught in three MBA programs and many companies since 2014.
This course is built around the notion of linear regression – a fantastic tool that you can use to (i) identify the strength of the relationships between variables that might exist in your data, (ii) test whether these relationships are robust in a statistical sense, and (iii) use the model for prediction. Linear regression is a classic and robust method that is easy to understand and interpret; it is widely used in the industry, and it works with virtually any data (after certain modifications).
Improved English subtitles (not automatically generated)