Due to the Corona virus it is unclear how the programmes will take place. For the latest news please check the course page in Blackboard/Brightspace.

Prospectus

# Statistics

Course
2018-2019

## Tags

Y1

None, this is a compulsory Year 1 course for all first-year LUC students.

## Description

This course gives an introduction to the basic concepts and techniques of statistical inference. Students will learn the basic principles of data collection, and how to explore patterns in data by means of descriptive statistics and graphs. It provides an introduction to the quantitative analysis of data by means of point estimates, confidence intervals, statistical tests on means, and regression analysis.

In addition, the course gives an introduction to programming in R, and the use of this software for statistical analysis.

## Course objectives

Skills
After successful completion of this course students should be able to:

• Examine patterns in data graphically and quantitatively;

• Relate the mode, median, mean, variance, standard deviation, and skewness, to the shape of a distribution;

• Formulate and test hypotheses on the mean of a single population, and interpret the outcomes.

• Formulate and test hypotheses on the means of two populations, and interpret the outcomes.

• Perform a simple linear regression analysis and be able to interpret the results.

• Write simple programs in R

• Use R for statistical analysis

Knowledge
After successful completion of this course students should know and understand the following concepts and techniques:

• Mode, median, mean, variance, standard deviation, and skewness

• Histogram, Boxplot

• Normal distribution, Student-t distribution, Z-score

• Null hypothesis, alternative hypothesis

• test statistic, p-value, significance level, critical values, type-1 error, type-2 error

• confidence interval, confidence level, relation between confidence interval and hypothesis test, margin of error

• Simple linear regression, regression parameters, residuals, correlation coefficient, outliers, residual plot

• R code for working with variables, arrays, and dataframes, ‘for’-loops, computing (summary) statistics, making scatter plots and histograms, simple linear regression, importing data, installing and loading packages

## Timetable

Once available, timetables will be published here.

## Mode of instruction

Lectures, group discussion and -assignments, and computer labs with R.

## Assessment

• In-class participation: 5%

• Quizzes: 30% (weeks 2 to 7)

• R-coding exam, on laptop 25% (week 7)

• Final, written exam: 40% (Reading week)

## Blackboard

There will be a Blackboard site available for this course. Students will be enrolled at least one week before the start of classes.

Open Intro Statistics, 2015 (3rd edition)
By: David M. Diez, Christopher D. Barr, Mine Çetinkaya-Rundel