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