new finance and statistics courses!

Learn about Equity Valuation and Inference for Linear Regression! How do we know when a stock is cheap or expensive? Learn how to value stocks in Equity Valuation in R. In our newest statistics course, you'll get to combine your skill in inference and linear models. [DataCamp]( New Courses! [Equity Valuation in R]( Equity Valuation in R Taught by Clifford Ang, Vice President at Compass Lexecon How do we know when a stock is cheap or expensive? To do this, we need to compare the stock's price with its value. In this course, you will learn the fundamentals of valuing stocks. By the end of this course, you will be able to build your own valuation models. [Start Learning]( [Inference for Linear Regression]( Inference for Linear Regression Taught by Jo Hardin, Professor at Pomona College Previously, you learned the fundamentals of both statistical inference and linear models; now, the next step is to put them together. This course gives you a chance to think about how different samples can produce different linear models. [Start Learning]( Equity Valuation in R: What You'll Learn Chapter 1: [Present Value Approaches]( We begin with a discussion of time value of money and then move on to the first of two discounted cash flow methods. Chapter 2: [Perpetuity Growth Rate, Analyzing Projections, and Using a Dividend Discount Model]( One of the critical components of free cash flow to equity valuation is using reliable projections. In this chapter, we will discuss ways to analyze the projections. Chapter 3: [Discount Rate / Cost of Capital Estimation]( To be able to discount cash flows, we need a discount rate. Chapter 4: [Relative Valuation]( Relative valuation allows us to use the valuation of comparable companies to infer the value of our subject firm. [Play Now]( Inference for Linear Regression: What You'll Learn Chapter 1: [Inferential ideas]( In the first chapter, you will understand how and why to perform inferential analysis on a regression model. Chapter 2: [Simulation-based inference for the slope parameter]( Learn about the ideas of the sampling distribution using simulation methods for regression models. Chapter 3: [t-Based Inference For the Slope Parameter]( Learn how to use the t-distribution to perform inference in linear regression models. Chapter 4: [Technical Conditions in linear regression]( You will consider the technical conditions that are important when using linear models to make claims about a larger population. Chapter 5: [Building on Inference in Simple Linear Regression]( This chapter covers topics that build on the basic ideas of inference in linear models, including multicollinearity and inference for multiple regression models. [Play Now]( [DataCamp] [DataCamp]( DataCamp Inc. | 350 Fifth Avenue | Suite 7730 | New York, NY 10118 [Facebook] [Facebook]( [Twitter] [Twitter]( [LinkedIn] [LinkedIn]( [YouTube] [YouTube]( [Unsubscribe](