Provide basic principles of experimental design, data collection and evaluation methods before the intensive use of statistical software to get better outputs and prevent misunderstandings during the analysing stage of the research data.. Correlation and regression. χ 2 test, t test, and ANOVA, time series and indices.

Learning Outcomes

CTPO

TOA

Upon successful completion of the course, the students will be able to :

LO - 1 :

gain insight about population-sample, parameter-statistic relationships in fisheries and oceanography.

3,12,13,16

1,3

LO - 2 :

construct hypothesis, design scientific experiments, collect and present data.

3,12,13,16

1

LO - 3 :

apply measure of central tendency and dispersion for different types of statistical distributions.

3,12,13,16

1,3

LO - 4 :

recognise data coming from such distributions as normal, binomial and poisson, sampling (means, difference of means, ratios) and test distributions (z, t, chi-square, F) in order to applye them in their professional field.

3,12,13,16

1

LO - 5 :

test hypothesis using Z, Chi-square and variance analysis.

3,12,13,16

1

CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome

Contents of the Course

Variables and graphs. Frequency distributions. Measures of central tendency (mean, mode, median, etc. ) . Measure of dispersion (range, mean and standard deviation. Elementary probability theory. Normal, binomial and poisson distributions. Elementary sampling theory. Statistical estimation theory, statistical decision theory and tests of hypothesis and significance. Small sampling theory. Curve fitting and the methods of least squares

Course Syllabus

Week

Subject

Related Notes / Files

Week 1

Introduction to statistics, terms and definitions, population and sample, sample size and sampling methods, discrete and continuous variables, tables and graphs.

Week 2

Raw data, arrays, frequency distributions, class intervals and class limits, class boundaries, the size of class intervals, class mark, general rules for forming frequency distributions, histograms and frequency polygons, relative and cumulative frequency distributions.

Week 3

Measure of central tendency, arithmetic mean, weighed arithmetic mean, median, mode, empirical relation between mean median and mode, geometric mean, harmonic mean, properties of different measures.

Week 4

Measures of dispersion, range, mean deviation, variation, standard deviation, coefficient of variation, properties of variance, Sheppard's correction for variance.

Week 5

Elementary probability theory, classical and statistical definition, probability theorems, independent and dependent events, conditional probability, probability distributions, mathematical expectation, factorial n, permutations, combinations.

Week 6

Classical populations, normal distributions, binomial distributions, poisson distributions.

Week 7

Relationship between variables, definitions, regresion lines and coefficients, estimation, correaltion coefficient, lineer and non-lineer relationships, computation, least square method.

Week 8

Mid-term exam

Week 9

Sampling distributions, definitions, distribution of means, distribution of difference of means, distribution of proportions.

Week 10

Standart error, standart error of mean, difference of means, correlation and regression coefficients.

Week 11

Test distributions, Z, t, Chi-square, F distributions, estimation of parameters, confidence intervals.

Week 12

Problem solving exercises

Week 13

Hypothesis testing, Type I and Type II errors, significance levels, Z, Student's t, chi-square tests and tables.

Week 14

Analysis of variance, mathematical model and analyses, means of squares, F test, computations evaluation of analyses, determination of different groups, least significance, Duncan method.

Week 15

Determination of different groups, least significance, Duncan method.

Week 16

End-term exam

Textbook / Material

1

Düzgüneş, O., Kesici, T., Gürbüz, F. 1993. İstatistik Metodları (Statistical Methods). A.Ü. Ziraat Fak. Yay. No:1291, 218 pp.

2

Spiegel, M.R.1972. Theory and Problems of Statistics. Schaum

Recommended Reading

1

s Outline Series. McGraw-Hill Book Company, 359 pp.

2

Yıldız, N., Bircan, H. 1994. Araştırma ve Deneme Metodları. Atatürk. Ün. Zir. Fak. No 305. Erzurum. 266 s.

3

Düzgüneş, O. 1963. Bilimsel Araştırmalarda İstatistik Prensipleri ve Metodları. EÜ. Matbaası.375 s.