| Course Description |
The aim of the course is to give the fundamentals of statistical analysis. This course introduces the basics of statistics for engineers to summarize numerical and categorical data obtained from surveys, experiments, etc. The topics include different data types, measures of location, variability, shape, and association between variables. The students are expected to learn the fundamental concepts of estimation, confidence intervals, hypothesis testing and apply appropriate tests for population mean, proportion, variance and difference, independence, and goodness to fit. Students will be able to apply Analysis of Variance and Linear Regression using modern tools. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Summarize numerical and categorical data by frequency distribution, histograms, and computing descriptive statistics (mean, median, variance) by hand and\or using Excel and\or Google Sheets. Analyze and interpret association between two variables using covariance and correlation coefficient by hand and/or using Excel and\or Google Sheets.
2) Understand the basic concepts of sampling distributions, estimation, confidence intervals, and hypothesis testing (type I and II errors), explain the differences among various statistical techniques and identify an appropriate technique for a given set of variables and research questions.
3) Design, solve and interpret the results of hypothesis tests (t-test, z-test, chi-square test, F-test) related to population mean, population proportion, population differences, goodness to fit, tests of independence, and population variances.
4) Design, solve and interpret the results of Analysis of Variance (ANOVA) to compare population means. Analyze numerical data by graphs, create and test the validity of a simple and multiple linear regression models using Excel. Understand the use of regression models in prediction and estimation.
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Program Outcomes and Competences |
Level |
Assessed by |
| 1) |
Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. |
H |
|
| 2) |
Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. |
H |
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| 3) |
Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. |
H |
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| 4) |
Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. |
H |
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| 5) |
Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. |
S |
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| 6) |
Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. |
S |
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| 7) |
Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. |
S |
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| 8) |
Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. |
N |
|
| 9) |
Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. |
S |
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| 10) |
Understanding of social and environmental aspects of machine learning applications. |
N |
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| Week |
Subject |
| 1) |
1. Data and Statistics
1.1 Data, data types, sources of data
2. Descriptive Statistics
2.1 Summarizing data for categorical variables
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| 2) |
2. Descriptive Statistics
2.2 Summarizing data for quantitative variables
2.3 Measures of location
2.4 Measures of variability
2.5 Measures of distribution shape
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| 3) |
2. Descriptive Statistics
2.6 Box plots
2.7 Measures of association between two variables
2.8 Summarizing data for two variables
2.9 The weighted mean and working with grouped data
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| 4) |
3. Sampling Distributions and Interval Estimation
3.1 Point estimation
3.2 Sampling distribution of sample mean
3.3 Law of large numbers and the central limit theorem
3.4 Sampling distribution of sample proportion
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| 5) |
3. Sampling Distributions and Interval Estimation
3.5 Interval estimation for population mean, known variance
3.6 Interval estimation for population mean, unknown variance
3.7 Interval estimation for population differences
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| 6) |
3. Sampling Distributions and Interval Estimation
3.7 Interval estimation for population differences (continued)
3.8 Interval estimation for population variance
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| 7) |
4. Hypothesis Testing Basics
4.1 Null and alternative hypothesis
4.2 Type I and II errors
4.3 Statistical techniques for hypothesis testing
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| 8) |
5. Hypothesis Testing Involving Single Sample
5.1 Testing population mean, known variance
5.2 Testing population mean, unknown variance
5.3 Testing population proportion
5.4 Testing population variance
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| 9) |
6. Hypothesis Testing Involving Multiple Samples
6.1 Testing population differences, known variances
6.2 Testing population differences, unknown variances
6.3 Testing population differences, paired samples
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| 10) |
6. Hypothesis Testing Involving Multiple Samples
6.4 Testing the ratio of population variances
6.5 Goodness to Fit test and Testing independence
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| 11) |
Analysis of Variance |
| 12) |
8. Linear Regression
8.1 Simple linear regression
8.2 Prediction and estimation using linear regression
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| 13) |
8. Linear Regression
8.3 Multiple regression
8.4 Logistic regression
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| 14) |
Review on deciding the appropriate tests for different problems
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| 15) |
Final Examination Period |
| 16) |
Final Examination Period |