Inhalt: With businesses having to grapple with increasing amounts of data, the need for data reduction has intensified in recent years. To make sense of an overabundance of information, you can use cluster analysis-which allows you to develop inferences about a handful of groups instead of an entire population of individuals-as well as principal components analysis, which exposes latent variables. In this course, Conrad Carlberg explains how to carry out cluster analysis and principal components analysis using Microsoft Excel, which tends to show more clearly what''s going on in the analysis. Then he explains how to carry out the same analysis using R, the open-source statistical computing software, which is faster and richer in analysis options than Excel. Plus, he walks through how to merge the results of cluster analysis and factor analysis to help you break down a few underlying factors according to individuals'' membership in just a few clusters. Umfang: 01:02:15.00
Inhalt: Exponential smoothing is a term for a set of straightforward forecasting procedures that apply self-correction. Each forecast comprises two components. It''s a weighted average of the prior forecast, plus an adjustment that would have made the prior forecast more accurate. Smoothing-like most credible approaches to forecasting-requires a baseline of observations, in sequence, to work properly. Weekly revenues and daily hospital admissions are typical examples. Several versions of exponential smoothing exist, each corresponding to a type of baseline. In this course, Conrad Carlberg provides an introduction to simple exponential smoothing, diving into the basic idea behind it, and explaining how to assemble the forecast equation and optimize forecasts. Umfang: 01:05:13.00
Inhalt: Seasonal exponential smoothing is an extension of simple exponential smoothing (SES). Seasonal smoothing is often used when a baseline shows regular seasonal peaks and valleys. Residential water usage is a familiar example: consumption rises during the summer and fall and drops during winter and spring-but the overall annual consumption tends to remain stationary over several years. In this course, veteran business analytics consultant and instructional expert Conrad Carlberg shows how to incorporate seasonal variation for more accurate and insightful forecasts. Learn how to identify seasonality, perform seasonal smoothing of horizontal baselines, and optimize your forecasts with R and Microsoft Excel. Umfang: 00:49:39.00
Inhalt: Simple exponential smoothing (SES) incorporates most of the elements used in the smoothing approach to forecasting, such as a level smoothing constant, self-correction, and the gradual weakening of the influence of older observations on new forecasts. But SES works poorly with baselines that display either trends or seasonality. The trended time series is one step up in complexity from the stationary time series analyzed by SES-its baseline trends up or down. The use of exponential smoothing with a trended baseline is often called Holt''s method, and this course was designed to equip you with this technique. Here, instructor Conrad Carlberg explains how to use Holt''s method to create forecasts in R that deal with trends in a baseline. Umfang: 01:01:30.00
Inhalt: Statistical analysis often goes beyond simple analysis of variance (ANOVA), which tells you only if a reliable difference exists somewhere-but not specifically where. Sometimes, you may have to determine whether group A's mean value differs reliably from the mean value of group B, or from that of group C. Multiple comparison tests are the standard for pinpointing these mean differences. In this course, Conrad Carlberg shows how to use Excel and the open-source platform R to run Tukey's HSD test and the Scheffe ? multiple comparison test following an ANOVA. Along the way, he discusses related concepts, such as critical values, group size, data snooping, and statistical power, and explains how they influence your choice of tests. Umfang: 00:59:00.00
Inhalt: Confidence intervals are a family of statistical techniques that show up, often in unexpected guises, in your personal and professional lives. Whether you're looking at reference ranges on blood tests or the range of risk you assume when you enter a new line of business, confidence intervals enable you to summarize data in a way that pinpoints an outcome, while also considering a range of other possibilities for context-so it''s helpful to understand what they mean and how they work. In this course, Conrad Carlberg discusses just that, sharing basic techniques for constructing and interpreting confidence intervals. Conrad digs into the purpose of confidence intervals; how to calculate them (including how to do so using R and Excel); how to compare means using confidence intervals; and more. Umfang: 01:01:35.00
Inhalt: Business decisions are often binary: take on this project or put it off for a year; extend credit to this customer or insist on cash; open a new retail outlet in a particular location or find another spot. When an outcome is a continuous variable such as revenue, ordinary regression is often a good technique, but when there are only two outcomes, logistic regression usually offers better tools. Learn how to use R and Excel to analyze data in this course with Conrad Carlberg. He takes you through advanced logistic regression, starting with odds and logarithms and then moving on into binomial distribution and converting predicted odds back to probabilities. After this foundation is established, he shifts the focus to inferential statistics, likelihood ratios, and multinomial regression. Conrad''s comprehensive coverage of how to perform logistic regression includes tackling common problems, explaining relationships, reviewing outcomes, and interpreting results. Umfang: 01:37:41.00
Inhalt: In a world where nearly everyone uses data to inform their business methodologies, an emerging consensus is that more emphasis needs to be placed on validating data; verifying that data-driven conclusions are accurate; and minimizing the risk that your conclusions are incorrect. Although most researchers know what meta-analysis is, few understand how to calculate an effect size from popular metrics such as risk ratios, or how the distinction between fixed and random effects can lead the meta-analyst astray. This advanced-level course for data science and statistics practitioners and researchers covers raw mean differences-specifically for experimental and comparison groups-and how to convert useful outcome measures such as relative risk and odds ratios to commensurate measures of effect size. Plus, learn about how confidence intervals are created for binary outcome measures. Umfang: 00:49:05.00
Inhalt: Data scientists who use Excel realize that R is emerging as the new standard for statistical wrangling (especially for larger data sets). This course serves as the perfect bridge for the many Excel-reliant data analysts and business users who need to update their data science skills by learning R. Much of the course focuses on how crucial statistical tasks and operations are done in R-often with the DescTools package-as contrasted with Excel functions and Data Analysis add-in, and then scales up from there, showing the more powerful features of R. Conrad Carlberg helps you effectively toggle between both programs, moving data back and forth so you can get the best of both worlds. Learn about calculating descriptive statistics, running bivariate analyses, and more. Umfang: 01:28:06.00
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