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Updated in [February 21st, 2023]

Introduction to Simple Linear Regression.
Simple Linear Regression: The Least Squares Regression Line.
Simple Linear Regression: Interpreting Model Parameters.
Simple Linear Regression: Assumptions.
Simple Linear Regression: Checking Assumptions with Residual Plots.
Inference on the Slope (The Formulas).
Inference on the Slope (An Example).
Simple Linear Regression: An Example.
Simple Linear Regression: Always Plot Your Data!.
Simple Linear Regression: Transformations.
Intervals (for the Mean Response and a Single Response) in Simple Linear Regression.
Leverage and Influential Points in Simple Linear Regression.

(Please note that we obtained the following content based on information that users may want to know, such as skills, applicable scenarios, future development, etc., combined with AI tools, and have been manually reviewed)
1. You can learn the basics of simple linear regression, including the least squares regression line, interpreting model parameters, assumptions, and checking assumptions with residual plots.

2. You can understand the formulas for inference on the slope and apply them to an example.

3. You can gain an understanding of how to use simple linear regression in an example, and the importance of always plotting your data.

4. You can learn about transformations, intervals for the mean response and a single response, leverage, and influential points in simple linear regression.

[Applications]
Learners can apply the concepts of Simple Linear Regression to analyze data sets and draw meaningful conclusions. They can use the least squares regression line to fit a linear model to the data, interpret the model parameters, and check the assumptions with residual plots. They can also use the formulas to make inference on the slope, use transformations to improve the model, and calculate intervals for the mean response and a single response. Additionally, learners can identify leverage and influential points in Simple Linear Regression.

[Career Paths]
1. Data Scientist: Data Scientists use Simple Linear Regression to analyze data and develop predictive models. They use the models to identify trends and patterns in data, and to make predictions about future outcomes. As data becomes increasingly important in the business world, the demand for Data Scientists is growing rapidly.

2. Business Analyst: Business Analysts use Simple Linear Regression to analyze data and develop insights that can be used to inform business decisions. They use the models to identify trends and patterns in data, and to make predictions about future outcomes. Business Analysts are in high demand as businesses look to leverage data to make better decisions.

3. Machine Learning Engineer: Machine Learning Engineers use Simple Linear Regression to develop and deploy machine learning models. They use the models to identify trends and patterns in data, and to make predictions about future outcomes. As machine learning becomes increasingly important in the business world, the demand for Machine Learning Engineers is growing rapidly.

4. Statistician: Statisticians use Simple Linear Regression to analyze data and develop insights that can be used to inform decisions. They use the models to identify trends and patterns in data, and to make predictions about future outcomes. Statisticians are in high demand as businesses look to leverage data to make better decisions.