Statistics
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1. komponenta
| Lecture type | Total |
|---|---|
| Lectures | 30 |
| Practicum | 10 |
| Exercises | 20 |
Description:
This course is an introduction to Statistics. It starts with intuitively clear and often used descriptive statistics and then moves on to the basic probability theory. The second part of the course deals with inferential statistics: parameter estimates, confidence intervals and statistical hypothesis testing. At the very end of the course, linear regression is explained.
--- Descriptive Statistics: Presentation of tables and graphs. Measures of central tendency. Measures of variability. Measures of location.
--- Basic Probability Theory: Sample space and events. The probability of an event. Conditional probability. Independent events. Discrete and continuous random variables. Expectation and variance of a random variable. Binomial distribution. Hypergeometric distribution. Poisson distribution. Normal distribution.
--- Statistical Hypothesis Testing and Confidence Intervals: Random samples. Point estimation of a parameter (mean and variance). Statistical tests. Type I and type II errors. Hypothesis testing for a population mean (a random sample from a normal distribution sample or a large sample). Confidence interval for a population mean (a random sample from a normal distribution sample or a large sample). Comparing two population means (t-test). Comparing two population variances (F-test). Single factor analysis of variance (ANOVA). Hypothesis testing for a binomial proportion. Confidence interval for a binomial proportion. Comparing two binomial proportions. Pearson's chi-square test. Chi-square test of independence. Chi-square test of homogeneity.
--- Linear Regression: A straight-line regression model; the method of least squares. Confidence intervals for the parameters of the model. Testing hypothesis for the parameters of the model. Making predictions. Confidence interval for a prediction of the mean response for a specified x value. Confidence interval of a prediction of a single response for a specified x value. Pearson's product-moment correlation coefficient. Hypothesis testing for the population correlation coefficient.
--- Descriptive Statistics: Presentation of tables and graphs. Measures of central tendency. Measures of variability. Measures of location.
--- Basic Probability Theory: Sample space and events. The probability of an event. Conditional probability. Independent events. Discrete and continuous random variables. Expectation and variance of a random variable. Binomial distribution. Hypergeometric distribution. Poisson distribution. Normal distribution.
--- Statistical Hypothesis Testing and Confidence Intervals: Random samples. Point estimation of a parameter (mean and variance). Statistical tests. Type I and type II errors. Hypothesis testing for a population mean (a random sample from a normal distribution sample or a large sample). Confidence interval for a population mean (a random sample from a normal distribution sample or a large sample). Comparing two population means (t-test). Comparing two population variances (F-test). Single factor analysis of variance (ANOVA). Hypothesis testing for a binomial proportion. Confidence interval for a binomial proportion. Comparing two binomial proportions. Pearson's chi-square test. Chi-square test of independence. Chi-square test of homogeneity.
--- Linear Regression: A straight-line regression model; the method of least squares. Confidence intervals for the parameters of the model. Testing hypothesis for the parameters of the model. Making predictions. Confidence interval for a prediction of the mean response for a specified x value. Confidence interval of a prediction of a single response for a specified x value. Pearson's product-moment correlation coefficient. Hypothesis testing for the population correlation coefficient.
Learning outcomes:
Literature:
- Skripta,
- G. K. Bhattacharyya, R. A. Johnson, Statistical Concepts and Methods, Wiley, New York, 1977.,
Optional literature:
- , M. R. Spiegel, L. J. Stephens, Statistics, 3rd Edition, Schaum's Outline Series, McGrow-Hill, New York, 1999., , , .
- , S. Lipschutz and J. Schiller, Introduction to probability and statistics, Shaum´s outline series, McGraw-Hill, 1998., , , .
- , I. Pavlić, Statistička teorija i primjena, Tehnička knjiga, Zagreb, 1988., , , .
- , Ž. Pauše, Uvod u matematičku statistiku, Školska knjiga, Zagreb, 1993., , , .
- , J. Pitman, Probability, Springer, 1993., , , .
- , F. Daly, D.J. Hand, M.C. Jones, A.D. Lunn, K.J. McConway, Elements of Statistics, Addison-Wesly, Wokingham, England, 1995., , , .
Prerequisit for:
3. semester
Mandatory course
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Regular
studij
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Food Technology
Consultations schedule: