Statistics

Students should be able to:

• Define and apply the meaning of descriptive statistics and statistical inference, describe the importance of statistics, and interpret examples of statistics in a professional context;

• Distinguish between a population and a sample;

• Calculate and explain the purpose of measures of location, variability, and skewness;

• Apply simple principles of probability;

• Compute probabilities related to both discrete and continuous random variables;

• Identify and analyze sampling distributions for statistical inferences;

• Identify and analyze confidence intervals for means and proportions;

• Compare and analyze data sets using descriptive statistics, parameter estimation, hypothesis testing;

• Explain how the central limit theorem applies in inference, and use the theorem to construct confidence intervals;

• Calculate and interpret confidence intervals for one population average and one population proportion;

• Differentiate between type I and type II errors;

• Conduct and interpret hypothesis tests;

• Define statistic

• Define parameter

• Define point estimate

• Define interval estimate

• Define margin of error

• Compute the probability of a sample mean being at least as high as a specified value when σ is known

• Compute a two-tailed probability

• Compute the probability of a sample mean being at least as high as a specified value when σ is estimated

• State the assumptions required for item before

• What null hypothesis is tested by ANOVA

• Describe the uses of ANOVA

• Identify and evaluate relationships between two variables using simple linear regression; and

• Discuss concepts pertaining to linear regression, and use regression equations to make predictions.

• Search for, analysis and synthesis of data and information, with the use of the necessary technology

• Decision-making

• Working independently

• Team work

• Working in an international environment

• Working in an interdisciplinary environment

• Production of new research ideas

• Respect for the natural environment

• Production of free, creative and inductive thinking

• Introduction

• Descriptive Statistics.

• Elements of Probability.

• Discrete and Continuous Distributions.

• Inference – Central limit theorem.

• Hypothesis testing.

• Analysis of variance.

• Simple Linear Regression.

• Verzani J. 2017. Εισαγωγή στην Στατιστική με την R. Κλειδάριθμος

• Φουσκάκης Δ. 2013. Ανάλυση Δεδομένων με Χρήση της R. Τσότρας

• Ντζούφρας Ι, Καρλής Δ. 2015. Εισαγωγή στον Προγραμματισμό και στη Στατιστική Ανάλυση με R. (ηλεκτρονικό βιβλίο) http://hdl.handle.net/11419/2601

• Γναρδέλλης Χ. 2003. Εφαρμοσμένη Στατιστική. Παπαζήση

• Μυλωνάς Ν. 2013. Πιθανότητες & Στατιστική. Τζιόλα

• Crawley MJ. 2012. The R Book. Wiley

• Diez DM, Barr CD, Cetinkaya-Rundel M. 2012. OpenIntro Statistics. http://www.openintro.org/stat/