# |
Learning Outcomes |
1 |
will be able to define data and summarize the relationship between datas |
2 |
will be able to create and use graphs for categorical and numerical data, and to describe relationships between variables |
3 |
will be able to use measures of central tendency, variation, and shape, and use population summary measures |
4 |
will be able to assess outcomes and events in a probability experiment, apply basic rules of probability |
5 |
will be able to apply the concept of statistical independence and use Bayes' Theorem |
6 |
will be able to use mean and standard deviation for discrete and continuous random variables |
7 |
will be able to use and apply some special probability distributions, and the normal approximation to the binomial distribution |
8 |
will be able to determine the skewness and curtosis of datas |
# |
Subjects |
Teaching Methods and Technics |
1 |
Basic statistical definitions. What is statistics and statistics? |
Lecturing, Discussion |
2 |
Data analysis. |
Lecturing, Discussion |
3 |
Data summarization methods. Frequency distributions and graphs. |
Lecturing, Problem Solving |
4 |
Data summarization methods. Central tendency measurements. |
Lecturing, Problem Solving |
5 |
Data summarization methods. Central variability measurements. |
Lecturing, Problem Solving |
6 |
Skewness and curtosis. |
Lecturing, Problem Solving |
7 |
Probability theory. |
Lecturing, Problem Solving |
8 |
Mid-Term Exam. |
Written exam |
9 |
Discrete probability distributions. Binomial Distribution. |
Lecturing, Problem Solving |
10 |
Discrete probability distributions. Poisson Distribution. |
Lecturing, Problem Solving |
11 |
Discrete probability distributions. Hypergeometric Distribution. |
Lecturing, Problem Solving |
12 |
Normal Distribution. |
Lecturing, Problem Solving |
13 |
Normalization of discrete probability distributions. |
Lecturing, Problem Solving |
14 |
Normalization of discrete probability distributions. |
Lecturing, Problem Solving |
15 |
Normalization of discrete probability distributions. |
Lecturing, Problem Solving |
16 |
Final Exam |
Written exam |
# |
Learning Outcomes |
Program Outcomes |
Method of Assessment |
1 |
will be able to define data and summarize the relationship between datas |
2͵5 |
1͵2 |
2 |
will be able to create and use graphs for categorical and numerical data, and to describe relationships between variables |
2͵5 |
1͵2 |
3 |
will be able to use measures of central tendency, variation, and shape, and use population summary measures |
2͵5 |
1͵2 |
4 |
will be able to assess outcomes and events in a probability experiment, apply basic rules of probability |
2͵5 |
1͵2 |
5 |
will be able to apply the concept of statistical independence and use Bayes' Theorem |
2͵5 |
1͵2 |
6 |
will be able to use mean and standard deviation for discrete and continuous random variables |
2͵5 |
1͵2 |
7 |
will be able to use and apply some special probability distributions, and the normal approximation to the binomial distribution |
2͵5 |
1͵2 |
8 |
will be able to determine the skewness and curtosis of datas |
2͵5 |
1͵2 |