# Excel SPSS STATA EVIEWS R SOFTWARE ATLAS NVIVO

Excel SPSS STATA EVIEWS R SOFTWARE ATLAS NVIVO

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Introduction to Statistics & R-Programming

Introduction to Statistics

Introduction to R

Getting started with R.

Getting R and Rstudio.

Typing commands at the console.

Simple calculations.

Using functions.

Introduction to variables.

Numeric, character and logical data.

Storing multiple values as a vector

Additional R concepts

Installing and loading packages.

The workspace. Navigating the file system. More complicated data structures:

Factors, data frames, lists and formulas.

A brief discussion of generic functions

Descriptive statistics

Mean, median and mode. Range, interquartile range and standard deviations.

Skew and kurtosis.

Standard scores.

Correlations.

Tools for computing these things in R.

Brief comments missing data.

Descriptive statistics

Mean, median and mode. Range, interquartile range and standard deviations.

Skew and kurtosis.

Standard scores.

Correlations.

Tools for computing these things in R.

Brief comments missing data.

Pragmatic matters

Tabulating data

Transforming a variable

Subsetting vectors and data frames.

Sorting, transposing and merging data.

Reshaping a data frame.

Basics of text processing.

Reading unusual data files.

Basics of variable coercion.

Even more data structures.

Introduction to probability.

Probability versus statistics.

Basics of probability theory.

Common distributions: normal, binomial, t, chi-square, F.

Bayesian versus frequentist probability.

Estimating unknown quantities from a sample

Sampling from populations.

Estimating population means and standard deviations.

Sampling distributions.

The central limit theorem.

Confidence intervals.

Parametric Inference

Maximum Likelihood estimation

Hypothesis testing.

Research hypotheses versus statistical hypotheses.

Null versus alternative hypotheses.

Type I and Type II errors.

Sampling distributions for test statistics.

Hypothesis testing as decision making. p-values.

Reporting the results of a test. Effect size and power.

Controversies and traps in hypothesis testing

Comparing two means.

One sample z-test.

One sample t-test.

Student’s independent sample t-test.

Welch’s independent samples t-test.

Paired sample t-test.

Effect size with Cohen’s d.

Checking the normality assumption.

Wilcoxon tests for non-normal data.

Introduction to one-way ANOVA.

Doing it in R.

Effect size with eta-squared.

Simple corrections for multiple comparisons (post hoc tests).

Assumptions of one-way ANOVA.

Checking homogeneity of variance using Levene tests.

Avoiding the homogeneity of variance assumption.

Checking and avoiding the normality assumption. Relationship between ANOVA and t-tests.

Regression Analysis

Introduction to regression.

Estimation by least squares.

Multiple regression models.

Measuring the fit of a regression model.

Hypothesis tests for regression models.

Standardised regression coefficient.

Assumptions of regression models.

Basic regression diagnostics.

Model selection methods for regression

Principal component analysis

Generalized Linear Models

Correlation

Testing Goodness of Fit

Bayesian statistics.

Introduction to Bayesian inference.

Bayesian analysis of contingency tables.

Bayesian t-tests, ANOVAs and regressions

1. Statistics for Dummies, Deborah Rumsey, Wiley Publishing, Inc., (2010)

2. Kothari, C.R., Research methodology: Methods and techniques, (2edn) (New Delhi: New Age international ltd, (2015)

3. Chris Brooks (2014). Introductory econometrics for finance. 3rd ed. Cambridge: Cambridge University Press. ISBN: 978-1107661455.

4. James H. Stock & Mark W. Watson (2014). Introduction to econometrics. 3rd ed. Essex: Pearson Education Limited. ISBN: 978-0133486872

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