# Course Details

Course Information Package

Course Unit TitleSTATISTICS II
Course Unit CodeAMAT210
Course Unit Details
Number of ECTS credits allocated5
Learning Outcomes of the course unitBy the end of the course, the students should be able to:
1. Understand and implement the sampling distribution of the mean.
2. Use the t-distribution and read the table of t-distribution.
3. Understand, calculate and interpret interval estimation of the population mean for either small or large sample with σ known or unknown
4. Determine interval estimation of the population proportion and ddetermining the sample size
5. Understand the concept of hypothesis testing and be in a position to state the null and alternative hypothesis. Also understand the meaning of the significant level and recognise the two types or errors (type I and II).
6. Distinguish the difference between independent and dependent variable and construct the scatter diagram. Calculate and interpret the Pearson’s coefficient, and, estimate and interpret the coefficients (b0 and b1) of the simple linear regression model.
7. Apply regression, using data from the business environment and do forecasting.
8. Recognize the chi-square distribution and make use of its table.
9. Implement the two chi square tests, goodness of fit test and test of independence, with real data and interpret the results. Explain the meaning of the “statistical significance”.
Mode of DeliveryFace-to-face
PrerequisitesAMAT112Co-requisitesNONE
Recommended optional program componentsNONE
Course Contents

Sampling and sampling distributions

The various kinds of sampling techniques (simple, stratified, clustering). Sampling from finite and infinite population. Sampling distribution of the mean.

Interval estimation

Recall of the normal distribution. Point estimation of the population mean. Interval estimation of the population mean for large sample with s known and s unknown. The t-distribution and the table of t-distribution. Interval estimations of the population mean for small sample with s known or s unknown. Interval estimation of the population proportion. Determining the sample size for estimating mean or proportion.

Hypothesis testing

Hypothesis testing. Null and alternative hypothesis. Significant level. Types or errors (type I and II).

Tests of goodness of fit and independence

Chi-square distribution and its table. Chi square goodness of fit test. Contingency tables and the chi square test of association. “Statistical significance”.

Correlation

Independent and dependent variables. Pearson’s coefficient and the values of it

Simple linear regression

Independent and dependent variable. Scatter diagram. Coefficients (b0 and b1) of the simple linear regression model. Regression, using data from the business environment. Forecasting.
Recommended and/or required reading:
Textbooks
• Anderson D.R., Sweeny D.J., Williams T.A., (2011) Statistics for Business and Economics, South Western
References
• Mavrikiou P., Understanding Essential Probability and ΣtatisticΣ: Some theory and applications (Instructor’s notes)
• Black K., (2012) Applied Business Statistics Making Better Business Decisions
Planned learning activities and teaching methods

The taught part of course is delivered to the students by means of lectures, and tutorials. Lecture notes are available through the e-learning platform of the University, and the instructor’s webpage. Students are encouraged for class work, problem solving and discussion. Students are also introduced in data analysis using IBM SPSS but under a different course (ARRW101).

Assessment methods and criteria
 Test 1 20% Test 2 20% Final Exam 60%
Language of instructionEnglish
Work placement(s)NO