Tips and Techniques for Statistics and Quality Improvement

Surveys have become commonplace in almost every industry, especially with the use of smartphones and social media. Survey data can help analysts understand customer behavior and attitudes. 

And it's not as direct as reading a number off a gage. Questions need to be constructed to get at the complex social phenomenon of behavior. The wording, responses to select from and process of collecting respondents all are part...

The most practical reason for statistical analysis is the fact that a subset of data is collected and not the entire population. The flexibility of collecting sampled data saves money and time! This flexibility comes with a cost in errors in our decisions.

“We are not makers of history. We are made by history.” – Martin Luther King, Jr.

Like this quote, Time Series analyses place emphasis on history, or in our case, emphasis on data. For better time series analyses, a full practical history of the data needs to be accounted for with a strong understanding of the context of those data.

The goal of regression is to make accurate predictions. Two factors that impact the predictability of the model are the terms in the model (linear, interactions, quadratic) and the sample data used to calculate the model. Models with too many terms often overfit the sample data but lead to poor prediction of new data values.

While there are many new predictive analytics and machine learning tools in the market, Regression is a classical tool for building predictive models. Regression allows the user to model the relationship between a response and various predictors. Companies need to properly implement predictive tools, and, Minitab’s Regression can help achieve meaningful predictive modeling capability!

Regression can pretty much do it all! But, most of the time, we like to think of a Regression problem as a best fitted line:

Predicted y = mx + b

The slope is denoted by m, which denotes the average change in y for every 1-unit increase in x. The y-intercept is denoted by b, the average outcome of y when x equals zero. In practical uses, we care more about the impact of x and focus on the slope.