Statistical Inference with R

Inference is an essential aspect of statistical analysis. Inference is the process of drawing conclusions out of data in order to draw conclusions about a larger population. This is a common practice, even though it might seem counterintuitive. We could conclude that the same treatment will lead to equal survival rates to show that it is superior in medicine. This assumption will be disproved by the data. A "p-value" is the agreement level between the data and the hypothesis. We also discuss confidence intervals, which can be used to measure the effect. Which treatment is more effective?

Categorical data is all around. It's in the most recent polling results, new genomics breakthroughs, and the huge amounts of data that internet businesses gather to market their products. This course will help you distinguish between signal and noise, as well as the tools to determine when data can be used as a source for interesting phenomena or random noise.

This course will show you how to use statistical methods for inferring and estimating numerical data. This course will cover two common methods to accomplish these tasks. To create confidence intervals, and tests based upon resamples, the first method uses bootstrapping. The second uses theoretical results and the t-distribution method to achieve the same result. Learn how to perform an ANOVA, create confidence intervals and conduct a t-test.

You've already learned the basics about linear modeling and statistical inference. It is now time to combine them. This course will help you understand how different samples can produce different linear models. This course is designed to help you understand the basic population model. The course will teach you how to estimate intervals and calculate the significance of the effect of the estimated linear model. Comparisons will be made between the prediction intervals of the response variable as well as the estimates for the average response. For cleaning up models, you can use ggplot2, dplyr and the broom packages. These three tools are vital in data science.