Fitting logistic models

Many business problems require the prediction of a binary response variable. Your future employer may need to detect spam e-mails, credit card frauds, or rare diseases.

The logistic regression model is the go-to method for binary classification problems.

In this exercise, you will use Parkinson's dataset from the UCI repository. This dataset is composed of a range of biomedical voice measurements from people with and without Parkinson's disease.

You will use the following variables from the dataset:

status - 1 - if a person has Parkinson's disease, 0 - otherwise,
NHR - a measure of the ratio of noise to tonal components in the voice,
DFA - a signal fractal scaling exponent.

The dataset is available as parkinsons.

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Practicing Statistics Interview Questions in R

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# Plot status vs NHR
___(status ~ ___, data = ___)

# Plot status vs DFA
___(___ ~ ___, ___ = ___)

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Practicing Statistics Interview Questions in R

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Want to increase your odds of acing your job interview? If so, brush up on your knowledge of probability theory. In this chapter, we'll roll dice and shoot baskets to explain probabilities using real-life examples.

Exercise 1: Discrete distributions Exercise 2: Probability functions Exercise 3: Bernoulli trials Exercise 4: Binomial distribution Exercise 5: Continuous distributions Exercise 6: Uniform distribution Exercise 7: Shape of normal distribution Exercise 8: Sample from normal distribution Exercise 9: Central limit theorem Exercise 10: Law of large numbers Exercise 11: Simulating central limit theorem

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Exercise 1: Descriptive statistics Exercise 2: Centrality measures Exercise 3: Variability measures Exercise 4: Categorical data Exercise 5: Survey analysis Exercise 6: Data encoding Exercise 7: Time series Exercise 8: Time series object Exercise 9: Wrangling time series Exercise 10: Principal Component Analysis Exercise 11: PCA - rotation Exercise 12: PCA - dimension reduction

March confidently into your job interview after reviewing confidence intervals. We'll review the t-test, ANOVA, and normality tests to prepare you for statistics-based coding questions.

Exercise 1: Normality tests Exercise 2: Shapiro-Wilk test Exercise 3: Q-Q plot Exercise 4: Inference for a mean Exercise 5: Confidence interval Exercise 6: One-sample t-test Exercise 7: Comparing two means Exercise 8: Two-sample t-test Exercise 9: Paired test Exercise 10: ANOVA Exercise 11: Comparing groups Exercise 12: ANOVA for plant growth

Is your potential employer planning to test your R skills? Make sure you’re prepared and practice model evaluation beforehand. In this chapter, we will fit and evaluate linear and logistic regression models using various biomedical datasets. By the end of this chapter, you’ll be fully prepared to answer any question the interviewer throws your way!

Exercise 1: Covariance and correlation Exercise 2: Covariance by hand Exercise 3: Linear relationship Exercise 4: Nonlinear relationship Exercise 5: Linear regression model Exercise 6: Fitting linear models Exercise 7: Predicting with linear models Exercise 8: Logistic regression model Exercise 9: Fitting logistic models

Ejercicio actual

Exercise 10: Predicting with logistic models Exercise 11: Model evaluation Exercise 12: Validation set approach Exercise 13: Regression evaluation Exercise 14: Classification evaluation Exercise 15: Wrapping up