**Hafta ** |
**Konu ** |

**1** |
Pretest, Introduction to Probability and Statistic, Course Objectives |

**2** |
The Definition of Statistics.
Classification of Statistics; Descriptive and Inferential Statistics
Data Description and Graphical Methods: |

**3** |
Histogram, Stem and Leaf Diagrams, Frequency distributions, box plots. |

**4** |
Numerical Methods of Describing Data: (for Ungrouped and Grouped data)
Measure of Central Tendency, Measure of Variability, Measure of Relative Standing. |

**5** |
Normal distribution, Moments-Parameters of Distribution; Skewness, Kurtosis,
Some computer applications. |

**6** |
Approaches to the theory of probability, definition of probability.
Fundamentals of Probability, basic concepts: experiment, outcome, sample space, event, elementary event, event space. |

**7** |
Computing Probabilities: Set algebra, Combinatorial analysis: Basic principle of counting,
Permutations, Combinations. |

**8** |
Conditional Probability and Independence. Bayes formula.
Conditional probability applications and examples, |

**9** |
Bernoulli trials, Binomial distribution
Random Variables, Probability Distributions Functions |

**10** |
Random Variables, Probability Distributions Functions |

**11** |
Probability Distributions Functions, Probability Density Function, Expectations and Moments |

**12** |
Sampling Distribution, Sampling Theory, Random Sampling, Central Limit Theorem,
Statistical Inference: Small Sample Results |

**13** |
Statistical Inference: Small Sample Results
Statistical Inference: Large Sample |

**14** |
General Linear Models and Applications
Correlation Theory, Computer Applications, The Chi-Square Test; Time Series |