VARIÁVEIS QUALITATIVAS E QUANTITATIVAS ✅ INTRODUÇÃO À ESTATÍSTICA #02

Random Variables - Classification ✅ INTRODUCTION TO STATISTICS Qualitative Variables - Nominal and Ordinal Quantitative Variables Quantitative variables are characteristics that can be described by numbers, and these are classified as continuous or discrete. – Discrete variables: the variable is evaluated in numbers that are the result of counts and, therefore, only whole numbers make sense. Examples: number of children, number of bacteria per liter of milk, number of cigarettes smoked per day. – Continuous variables: the variable is evaluated in numbers that are the result of measurements and, therefore, can assume values with decimal places and must be measured using some instrument. Examples: mass (scale), height (ruler), time (clock), blood pressure, age. Qualitative Variables Qualitative (or categorical) variables are characteristics that do not have quantitative values, but rather are defined by categories, that is, they represent a classification of individuals. They can be nominal or ordinal. – Nominal variables: There is no ordering within the categories. Examples: gender, eye color, smoker/non-smoker, sick/healthy. – Ordinal variables: There is an ordering within the categories. Examples: education level (elementary, secondary, or tertiary), stage of disease (initial, intermediate, terminal), month of observation (January, February, December). -------------------------------------------------------------------------------------------------------------------------------------------------------- Tags: qualitative variable, discrete quantitative variable, qualitative and quantitative variable, continuous quantitative variable, examples of qualitative variables, qualitative and quantitative statistics, qualitative and quantitative variables, examples of quantitative and qualitative variables, nominal and ordinal qualitative variables, statistics, qualitative variables, quantitative variables, qualitative variables, continuous quantitative variables, nominal qualitative variable examples 1. Descriptive statistics and exploratory data analysis: graphs, diagrams, tables, descriptive measures (position, dispersion, skewness, and kurtosis). 2. Probability. 2.1. Basic definitions and axioms. 2.2. Conditional probability and independence. 2.3. Discrete and continuous random variables. 2.4. Probability distribution. 2.5. Probability function. 2.6. Probability density function. 2.7. Expected value and moments. 2.8. Special distributions. 2.9. Conditional distributions and independence. 2.10. Variable transformation. 2.11 Laws of large numbers. 2.12 Central limit theorem. 2.13 Random samples. 2.14 Sampling distributions. 3 Statistical inference. 3.1 Point estimation: estimation methods, estimator properties, sufficiency. 3.2 Interval estimation: confidence intervals, credibility intervals. 3.3 Hypothesis testing: simple and compound hypotheses, significance levels and test power, Student's t-test, chi-square test. 4 Linear regression analysis. 4.1 Least squares and maximum likelihood criteria. 4.2 Linear regression models. 4.3 Inference on model parameters. 4.4 Analysis of variance. 4.5 Residual analysis. 5 Sampling techniques: simple random, stratified, systematic, and cluster sampling. 5.1 Sample size. Become a member of this channel and gain benefits:    / @murakami.   Now on the Mathematics Rapidola channel, you can become a member of the Basic Statistics and Applied Statistics teams.