The instructor will also demonstrate to produce the above mentioned high-quality figures in listing (EMF, EPS, etc.) and document formats (RTF, PDF etc.).

These topics will be addressed through the analysis of data from two studies: (1) a school-based intervention program designed to impact students’ body mass index (BMI); and (2) an animal xenograft experiment designed to assess the effects of a drug and of radiotherapy on tumor growth.

* The three key properties of a function. * A proven strategy for writing new functions. * How to use functions to reduce duplication in your code. * How `lapply()` works and why it's so important. * A handful of FP tools that increase the clarity of your code.

This workshop is suitable for beginning and intermediate R users. You need to know the basics of R (like importing your data and executing basic instructions). If you're an advanced R user, you probably won't learning anything completely new, but you will learn techniques that allow you to solve new challenges with greater ease.

The workshop will be hands-on and interactive, so please make sure to bring along your laptop with R installed!

This short course aims to address the problems of missing data. The concept of different missing data mechanisms or typologies including missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR) will be discussed with illustration from real clinical trial examples. Moreover, this short course will introduce how to conduct two commonly-used model-based methods for missing data analysis including the multiple imputation (Little & Rubin, 2002; Reiter & Raghunathan, 2007) and maximum likelihood (Allison, 2012) using R/SAS. Some sensitivity analysis approaches to handle missing data under MNAR will also be discussed briefly.

We consider a variety of statistics (mean, trimmed mean, regression, etc.), and a number of sampling situations (one-sample, two-sample, stratified, finite-population), stressing the common techniques that apply in these situations. We'll look at applications from a variety of fields, including telecommunications, finance, and biopharm.

These methods let us do confidence intervals and hypothesis tests when formulas are not available. This lets us do better statistics, e.g. use robust methods (we can use a median or trimmed mean instead of a mean, for example). They can help clients understand statistical variability. And some of the methods are more accurate than standard methods.

The single most important thing we need to recognize about marketing mix modeling is that it is a causal question. This means, we are not looking for a prediction of an outcome variable based on the observation of marketing variables. Rather, we are looking to manipulate marketing variables to optimize an outcome variable. Thus, we are performing an intervention, which requires us to perform causal inference. This leads us to the Holy Grail of statistics, i.e. causal inference from observational data.

In this workshop, we introduce the basic concepts of graphical models and how they can help us perform causal identification, e.g. using causal assumptions and the well-known Adjustment Criterion. While this is straightforward in theory, the complexity of the marketing domain prevents the practical application of this criterion. Thus, we introduce a new criterion (Shpitser and VanderWeele, 2011) that reduces the number of assumptions that we require for confounder selection and causal identification.

Implementation with BayesiaLab With the confounders identified, we can now build a high-dimensional statistical model that represents the joint probability distribution of all marketing variables. We do that using the machine-learning algorithms of the BayesiaLab software platform. We obtain a Bayesian network that represents a multitude of relationships between all marketing variables and the outcome variable. Using BayesiaLab’s visualization functions, we can compare the machine-learned graph to our understanding of the domain. Furthermore, we can examine the (mostly nonlinear) response curves of the outcome variable as a function of the marketing variables. Most importantly, we use BayesiaLab to perform Likelihood Matching on all confounders to establish the causal response of the outcome variable.

With all causal response curves computed, we introduce cost functions for the marketing variables via BayesiaLab’s Function Node. On that basis, we proceed to BayesiaLab’s Target Optimization function, which, by means of a genetic algorithm, searches for an optimal combination of all marketing variables, while being subject to constraints of individual variables and an overall marketing budget constraint. The optimization report shows feasible solutions along with the degree of achievement.

Clients will hire (and re-hire) you only if they know, like, and trust you. This will only happen when you build a solid marketing process that conveys your strengths and what you can offer to the right clients. In this tutorial, you will learn about how to approach and get started creating a simple, yet solid, marketing plan that allows the right clients to know, like, and trust you.

The instructor will share her personal experiences and case studies of colleagues who built a consulting business and guide you through small group exercises.