Publications by Robb Sinn
S2: Experiments, Data and Conclusions: The Lady Tasting Tea
I. Initialization Block Initializing RStudio The Mosaic package was created by statistics instructors to help students learn the coding in R. Commands are streamlined to be more intuitive. Execute the code block below to load Mosaic (required each session). library(mosaic) II. Exercises Using the Mosaic function rflip, flip 16 coins and count ...
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S12: QQ-Plots
Effect of right-skew on qq-plot Let’s create a data set that is clearly skewed right. We’ll name our variable right, and put 500 observations in it using random draws from the exponential distribution (rate = 2). right = rexp(500, rate = 2) The histogram shows this data set appears to have been drawn from an approximately normal distribution ...
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S11: Randomization Tutorial
I. Sampling Techniques in the Mosaic Package We have three main sampling functions in the Mosaic package for R that help produce randomized versions of statistical tests and confidence intervals. Shuffle. A permutation, size equal to number of observations. Sample. Draws without replacement, size less than or equal to number of observations. Res...
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S7: Basic Regression
I. Correlation and Linear Models When two numeric variables are related, we say they are correlated. If the correlation is reasonably strong, we can create a linear model. The modeling process is usually referred to as linear regression. Like the hypothesis testing procedures, linear regression has a verification process based on the model assump...
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S6: Statistical Estimation
I. Confidence Intervals When conducting exploratory data analysis, we often wish to estimate parameters like the mean and standard deviation for numeric data, or a proportion for category data. Statisticians prefer interval estimates to point estimates. Though the confidence interval gives less apparent precision, the range of values can be const...
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S8: Multiple Regression
I. Multiple Predictor Linear Models In the previous module we discussed bivariate regression models where one predictor or \(x\)-variable is used to create a model for one \(y\)-variable or dependent variable. If we use \(k\) predictors, say, \(x_1, x_2, \cdots, x_k\), then the parameters of our linear model will be \[y = \alpha + \beta_1x_1 + \b...
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S9: Chi-Squared GOF
We can test the proportions in a probability model using a variant of \(\chi^2\) called Goodness of Fit (GOF). The \(\chi^2\) GOF hypothesis compares observed data to theoretical probabilities. Rejecting the null indicates evidence for a lack of fit. I. Example: Doberman Breeding Dobermans can be bred in 4 colors: black, red, blue and fawn, all ...
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S10: Error and Power
Statistical processes are based on probability models. This means some (hopefully small) chance of error will always be present due to randomness. We don’t question the validity of every statistical result, but one should wonder if some way exists to quantify how certain we are that our results are accurate. Spoiler Alert: there is a way! Init...
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Module S1 HW Version
I. Initialization Block Initializing RStudio If you execute the code block below, RStudio will prompt you to install Mosaic if needed. library(mosaic) II. Exercises Using the built-in R data frame ChickWeight which examines weight vs age of chicks on different diets, conduct an analysis of the variable weight. Include all relevant plots, and i...
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Hypothesis Testing: Proportions - HW Version
I. Initialization Block Initializing RStudio The data set we will use primarily is Data3350 which was produced in 2015 during an undergraduate research project about personality and humor. The VarsData3350 PDF file has descriptions of each variable in the Data3350 file. Both are available for download in D2L. Be sure to put the Data3350 in your ...
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