Erkam Minsin
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- The book "Using R for Introductory Statistics" by John Verzani
- Scripted on: Notepad++
- Executed on: R 4.1.1 and R Studio
- Dependent on : Standard R libraries, UsingR and MASS libraries
- Instructions on installing R and R Studio
- Some R essentials
- Using R as a calculator
- Assignment
- Using c() to enter data
- Using functions on a data vector
- Creating structured data
- Accessing data by using indices
- Assigning values to data vector
- Logical values
- Missing values
- Managing the work environment
- Reading in other sources of data
- Using R's built-in libraries and data sets
- Using the data sets that accompany this book
- Other methods of data entry
- Categorical Data
- Tables
- Barplots
- Pie Charts
- Factors
- Numeric Data
- Stem-and-leaf plots
- Strip Charts
- The center:mean,median,and mode
- Variation:the variance,standard deviation,and IQR
- Shape of a distribution
- Histogram
- Modes,symmetry, and skew
- Box plots
- Pairs of categorical variables
- Making two-way tables from summarized data
- Making two-way tables from unsummarized data
- Marginal distributions of two-way tables
- Conditional distributions of two-way tables
- Graphical summaries of two-way contingency tables
- Comparing independent samples
- Side-by-side boxplots
- Density plots
- Strip charts
- Quantile-quantile plots
- Relationships in numeric data
- Using scatterplots to investigate relationships
- The correlation between two variables
- Simple Linear Regression
- Using the regression model for prediction
- Finding the regression coefficients using lm()
- Transformations of the data
- Interacting with a scatterplot
- Outliers in the regression model
- Resistant regression lines: lqs() and rlm()
- Trend lines
- Viewing multivariate data
- Summarizing categorical data
- Comparing independent samples
- Comparing relationships
- R basics: data frames and lists
- Creating a data frame or list
- Accessing values in a data frame
- Setting values in a data frame or list
- Applying functions to a data frame or list
- Using model formula with multivariate data
- Boxplots from a model formula
- The plot() function with model formula
- Creating contingency tables with xtabs()
- Manipulating data frames: split() and stack()
- Lattice graphics
- Types of data in R
- Factors
- Coercion of objects
- Populations
- Discrete random variables
- Continuous random variables
- Sampling from a population
- Sampling distributions
- Families of distributions
- Binomial, normal, and some other named distributions
- Popular distributions to describe populations
- Sampling distributions
- The central limit theorem
- Normal parent population
- Nonnormal parent population
- The normal approximation for the binomial
- for loops
- Simulations related to the central limit theorem
- Defining a function
- Editing a function
- Function arguments
- The function body
- Investigating distributions
- Script files and source()
- The geometric distribution
- Bootstrap samples
- Alternates to for loop
- Confidence interval ideas
- Finding confidence intervals using simulation
- Confidence intervals for a population proportion, p
- Using prop.test() to find confidence intervals
- Confidence intervals for the population mean, mu
- One-sided confidence intervals
- Other confidence intervals
- Confidence intervals for differences
- Difference of proportions
- Difference of means
- Matched samples
- Confidence intervals for the median
- Confidence intervals based on the binomial
- Confidence intervals based on signed-rank statistic
- Confidence intervals based on the rank-sum statistic
- Significance test for a population proportion
- Using prop.test() to compute p-values
- Significance test for the mean (t-tests)
- Significance tests and confidence intervals
- Significance tests for the median
- The sign test
- The signed-rank test
- Two-sample tests of proportion
- Two-sample tests of center
- Two sample tests of center with normal populations
- Matched samples
- The Wilcoxon rank-sum test for equality of center
- The chi-squared goodness-of-fit test
- The multinomial distribution
- Pearson's chi-squared statistic
- The chi-squared test of independence
- The chi-squared test of homogeneity
- Goodness-of-fit tests for continuous distributions
- Kolmogorov-Smirnov test
- The Shapiro-Wilk test for normality
- Finding parameter values using fitdistr()
- The simple linear regression model
- Model formulas for linear models
- Examples of the linear model
- Estimating the parameters in simple linear regression
- Using lm() to find the estimates
- Statistical inference for simple linear regression
- Testing the model assumptions
- Statistical inferences
- Using lm() to find values for a regression model
- Multiple linear regression
- Fitting the multiple regression model using lm()
- Interpreting the regression parameters
- Statistical inferences
- Model selection
- One-way ANOVA
- Using R's model formulas to specify ANOVA models
- Using oneway.test() to perform ANOVA
- Using aov() for ANOVA
- The nonparametric Kruskal-Wallis test
- Using lm() for ANOVA
- Treatment coding for analysis of variance
- Comparing multiple differences
- ANCOVA
- Two-way ANOVA
- Treatment coding for additive two-way ANOVA
- Testing for row or column effects
- Testing for interactions
- Logistic regression
- Generalized linear models
- Fitting the model using glm()
- Nonlinear models
- Fitting nonlinear models with nls()