Trend Curve, Correlation, And Regression To The Mean Syllabus

Trend Curve, Correlation, and Regression to the Mean

This course covers Trend Curve, Correlation, and Regression to the Mean and consists of 12 topics.

Linear Regression Part One – In this video we show how to find the line best fitting a set of bivariate data.
Linear Regression Part Two – In this video we show how the RSQ, SLOPE, INTERCEPT, and STEYX functions can be used to analyze a linear regression.
Exponential Growth – In this video we show how exponential growth can be used to fit bivariate data where the graph gets steeper.
Power and Demand Curves – In this video we show how to fit a constant elasticity or power demand curve.
Polynomial Demand Curves – In this video we show how to fit a quadratic demand curve based on 3 data points.
Learning Curves – In this video we show how to estimate a Learning curve which shows how the unit cost of producing a product drops as more of the product has been produced.
Correlation – In this video we show how the concept of correlation can measure the strength of the linear association between monthly returns on six stocks.
Estimating the Beta of a Stock – In this video we show how the SLOPE function can be used to quickly estimate the Betas of many stocks.
Regression to the Mean – In this video we show how the idea of Regression to the Mean explains why NFL teams that do well in one season usually do not do as well the next season.
Moving Average – In this video we show how to summarize quarterly Amazon.com revenues with a Moving Average Chart and smooth out the trend and eliminate seasonality.
Finding a Good R Squared – In this video we show that an R Squared value of 0.99 is not necessarily that good.
Finding the Best Curve to Fit Data – In this video we show how to determine which of the linear, exponential, and power curve best fit a bi-variate relationship.
Learn More