The virtual course "Linear Regression for Business Statistics - Virtual Course - Coursera", is a course with different contents and that offers video classes of Approx. 28 hours to complete. Explore its essential features, and click the orange button to get detailed information on the Coursera e-Learning platform
Regression analysis is perhaps the most important business statistics tool used in the industry.
Regression is the engine behind a multitude of data analysis applications that are used for many forms of forecasting and prediction.
This is the fourth course of the "Statistics and Business Analysis" specialty.
The course introduces you to the very important tool known as Linear Regression.
You will learn how to apply various procedures, such as dummy variable regressions, transformation variables, and interaction effects.
All of this is presented and explained using easy to understand examples in Microsoft Excel.
The focus of the course is understanding and application, rather than detailed mathematical derivations.
Note: This course uses the 'Data Analysis' toolbox that is standard with the Windows version of Microsoft Excel.
It's also standard with the 2016 or later Mac version of Excel.
However, it is not standard with earlier versions of Excel for Mac.
WEEK 1 Module 1: Regression Analysis: An Introduction In this module you will be introduced to the linear regression model.
We will build a regression model and estimate it using Excel.
We will use the estimated model to infer relationships between various variables and we will use the model to make predictions.
The module also introduces the notion of errors, residuals, and R-squared in a regression model.
Topics covered include: • Introduction to linear regression • Building a regression model and estimation using Excel • Making inferences using the estimated model • Using the regression model to make predictions • Errors, residuals, and R-squared WEEK 2 Module 2: Regression Analysis: Hypothesis Tests and Goodness of Fit This module introduces different hypothesis tests that you could perform using the regression output.
These tests are an important part of inference and the module introduces them using Excel-based examples.
The p-values are entered along with the R-squared and adjusted R-squared goodness-of-fit measures.
Towards the end of the module we introduce 'dummy variable regression' which is used to incorporate categorical variables into a regression.
Topics covered include: • Hypothesis testing in a linear regression • Measures of 'goodness of fit' (R-squared, adjusted R-squared) • Dummy variable regression (using categorical variables in a regression) WEEK 3 Module 3: Analysis Regression: Dummy Variables, Multicollinearity This module continues the application of Dummy Variable Regression.
You can understand the interpretation of the Regression output in the presence of categorical variables.
Examples are elaborated to reinforce various concepts introduced.
The module also explains what multicollinearity is and how to deal with it.
Topics covered include: • Dummy variable regression (using categorical variables in a regression) • Interpreting coefficients and p-values in the presence of dummy variables • Multicollinearity in regression models WEEK 4 Module 4: Regression Analysis: Various Extensions Module extends their understanding of linear regression, introducing techniques such as centering the mean of the variables, and constructing confidence limits for predictions using the regression model.
A powerful regression extension known as 'Interaction Variables' is introduced and explained using examples.
We also study the transformation of variables in a regression and in that context we present the log-log and semi-log regression models.
Topics covered include: • Centering the Mean of Variables in a Regression Model • Constructing Confidence Limits for Predictions Using a Regression Model • Interaction Effects in a Regression • Transformation of Variables • Log-log Regression Models and half-log
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