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Course Information

Course Information

Course Title	Code	Semester	L+U Hour	Credits	ECTS
STATISTICS I	IKT0203	3. Semester	3 + 0	3.0	5.0

Prerequisites

None

Language of Instruction	Turkish
Course Level	Bachelor's Degree
Course Type	Compulsory
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals	An important aspect of being a manager is making decisions. The best decisions are those which are made based on fact. Such decisions require the use of data which often exhibit variation. In this class, the underlying principle will be the use of statistical analysis of data to make intelligent, fact-based decisions.
Course Content	WHAT IS STATISTICS? Introduction and General Information Basic Statistical Concepts Population Sample Statistical Inference Collection and Analysis of Numerical Data Statistical Analysis of Specific Relations Being concerned with uncertainties Decision Making Against Uncertainties and Making Predictions SUMMARY OF DATA Population Concept Example Concept Summarization of Digital Information Summarization of Ungrouped Data Sets Central Tendency Measurements for Ungrouped Data Sets Mean Median (Medium) Mode (Model) Central Distribution (Spread) Measurements for Ungrouped Data Sets Variance Standard Deviation (Error) Mean Absolute Deviation December Quarter And Decimal Coefficient of Change Summarization of Grouped Data Sets Central Tendency And Distribution Measures Frequency Diffusion Absolute Frequency Distribution Cumulative Absolute Frequency Distribution Relative Frequency Distribution Relative Cumulative Frequency Distribution Graphical Analysis histograms polygons Body and Leaf Graphics Other Graphical Methods Graphical Summarization of Data Important Points to be Taken to Remove Misconceptions INDEX NUMBERS Index Numbers Poblemi Simple And Total Index Numbers Simple Index Simple Total Index Weighted Total Index Laspeyres Index Paasche Index Fisher Ideal Index Consumer Price Index Deflation of Prices by Using Consumer Price Index Weighted Total Quantity Indexes Laspeyres Quantity Index Paasche Quantity Index Fisher Ideal Quantity Index Value Index Summary And End PROBABILITY THEORY Introduction and Overview of the Subject Random trial (random trial) What is Probability? Probability Approaches Inferences from Probability Theory, Consequences, Probability Types and Rules Two Variable Probabilities Bayes Theory DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS Random Variables Probability Distributions for Intermittent Variables Expectations for Intermittent Random Variables DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS Expected Value and Variance Binomial Distribution Hypergeometric Distribution Poisson Distribution CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS Uniform Distribution Normal distribution CONTINUOUS RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS Central Limit Theory Exponential Distribution GUESS Sampling and Sample Distribution Point Estimation Estimates for Single Population: Range Measurements and Confidence Interval Point and Range Forecasts Estimation of population mean when variance is not known GUESS t- Distribution Creating Confidence Intervals for Large Scaled Examples Establishing Confidence Interval for Normal Population Variance Creating a Confidence Interval for the Differences of Ordinary Populations in Normal Population Estimation of Number of Sample Observations HYPOTHESIS TESTS 1 Introduction and Overview of the Subject The Structure of Hypothesis Tests Test of Average of Normal Population in Case of Knowledge of Population Variance HYPOTHESIS TESTS 2 Test of Average of Normal Population in Case of Unknown Population Variance 2 (Chi Square) Test HYPOTHESIS TESTS 3 Testing of Large Scale Samples Testing the Mean of Two Normal Populations HYPOTHESIS TESTS 4 Equality Test for Variances of Normal Population Measurement of Test Power Some Views and Comments on Hypothesis Testing
Learning Outcomes	1) How to distinguish between different types of data. 2) How to construct and interpret several pictorial and numerical summaries of data. 3) How to calculate, interpret and use measures of variance. 4) Olasılık ve olasılık dağılımlarının nasıl kullanılacağının öğrenilmesi.