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# Tag Archives: dependent variable

## Regression Fantasies

Common Reasons for Doubting a Regression Model Finding a model that fits a set of data is one of the most common goals in data analysis. Least squares regression is the most commonly used tool for achieving this goal. It’s … Continue reading

Posted in Uncategorized
Tagged accuracy, autocorrelation, correlation coefficient, dependent variable, heteroscedasticity, intercept, misspecification, model, multicollinearity, Non-linear relationships, number of samples, outliers, overfitting, precision, regression, sample size, samples, software, standardization, statistical analysis, statistical tests, statistics, stats with cats, stepwise regression, trend, variability, variance
4 Comments

## Regression Fantasies: Part III

Is Your Regression Model Telling the Truth? There are many technologies we use in our lives without really understanding how they work. Television. Computers. Cell phones. Microwave ovens. Cars. Even many things about the human body are not well understood. … Continue reading

## Regression Fantasies: Part II

Six More Reasons for Doubting a Regression Model There are more than a few reasons for being skeptical about a regression model. Some are easy to identify, others are more subtle. Here are six more reasons you might doubt the … Continue reading

Posted in Uncategorized
Tagged autocorrelation, cats, dependent variable, heteroscedasticity, model, multicollinarity, overfitting, regression, variance, weighting
1 Comment

## Regression Fantasies: Part I

Five Common Reasons for Doubting a Regression Model Finding a model that fits a set of data is one of the most common goals in data analysis. Least squares regression is the most commonly used tool for achieving this goal. … Continue reading

## A Picture Worth 140,000 Words

Even if it’s been a while since your last statistics class, when you read Stats with Cats: The Domesticated Guide to Statistics, Models, Graphs, and Other Breeds of Data Analysis you’ll figure out that there’s much more to data analysis … Continue reading

## Grasping at Flaws

Even if you’re not a statistician, you may one day find yourself in the position of reviewing a statistical analysis that was done by someone else. It may be an associate, someone who works for you, or even a competitor. … Continue reading

Posted in Uncategorized
Tagged cats, correlation coefficient, criticism, dependent variable, jargon, math, mean, Normal distribution, number of samples, objectives, population, precision, probability, rule of thumb, sample size, samples, software, statistical analysis, statistical tests, statistics, stats with cats, uncertainty, variability
8 Comments

## The Santa Claus Strategy

I’m working all out Deadline is near Model’s in doubt Dooming my career. Sta-tis-tics will chill my meltdown. I’m adding new vars Testing them twice Trying to find out which ones’ll suffice Sta-tis-tics will give the lowdown. I see the … Continue reading

Posted in Uncategorized
Tagged cats, dependent variable, measurement, measurement scales, model, objectives, statistical analysis, statistics, stats with cats
4 Comments

## You’re Off to Be a Wizard

The process of developing a statistical model (https://statswithcats.wordpress.com/2010/12/04/many-paths-lead-to-models/) involves finding the mathematical equation of a line, curve, or other pattern that faithfully represents the data with the least amount of error (i.e., variability). Variability and pattern are the yin and … Continue reading

Posted in Uncategorized
Tagged AIC, BIC, cats, coeffiient of determination, Cook’s Distance, dependent variable, DFBETAs, F-test, jargon, model, multicollinearity, Normal distribution, probability, regression coefficients, residuals, standard error of estimate, statistical analysis, statistical leverage, statistical tests, statistics, stats with cats, t-test, trend, uncertainty, variability, variance inflation factor
8 Comments

## The Seeds of a Model

Perhaps the most complicated and time-consuming aspect of model building is selecting the components of your model—the variables, the samples, and the data (https://statswithcats.wordpress.com/2010/12/04/many-paths-lead-to-models/). Here are a few tips for collecting the seeds of your model. Models Revisited Here’s a … Continue reading