Course Objectives
1. We can compute the basic computation of the fundamentals of statistic processes.
2. We can understand basic properties and get the conditional probability and Bay's estimation.
3. We can make a solution of mean value, variance and standard deviation of basic probability distributions.
Rubric
| Ideal Level | Standard Level | Unacceptable Level |
Achievement 1 | We can compute the basic computation of the fundamentals of statistic processes and apply these for the various problems. | We can compute the basic computation of the fundamentals of statistic processes. | We can compute the basic computation of the elementary statistic processes. |
Achievement 2 | We can understand basic properties and get the conditional probability and Bay's estimation and apply these for the various problems. | We can understand basic properties and get the conditional probability and Bay's estimation. | We can understand basic properties and get the elementaries of conditional probability and Bay's estimation. |
Achievement 3 | We can make a solution of mean value, variance and standard deviation of basic probability distributions and apply these for the various problems. | We can make a solution of mean value, variance and standard deviation of basic probability distributions. | We can make a solution of mean value, variance and standard deviation of elementary probability distributions. |
Assigned Department Objectives
Teaching Method
Outline:
We are to make a concentration for our class and use the knowledges and techniques about undergraduate mathematics to construction of understanding of the probability and statistics.
Style:
Our class is construction of the next three phases.
1. Review the important facts from the previous class.
2. Lecture about the new section.
3. Short exercises.
Notice:
Please make a good preparation and self-review.
You will build up the good style to do homework of the previous class.
*Mastery of this course is required to complete the Mathematical and Data Science and AI Education Program (Literacy)
Characteristics of Class / Division in Learning
Course Plan
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Theme |
Goals |
1st Semester |
1st Quarter |
1st |
Analyzing the data of one-dimensional variable |
We can understand and explain of frequency distribution and its measures of center.
|
2nd |
Analyzing the data of one-dimensional variable |
We can understand and explain of its distribution bias and the dispersion.
|
3rd |
Analyzing the data of one-dimensional variable |
We can understand and explain of its distribution bias and the dispersion.
|
4th |
Analyzing the data of two-dimensional variables |
We can understand and explain of its distribution scatter plot bias and the regression line.
|
5th |
Analyzing the data of two-dimensional variables |
We can understand and explain of its distribution co-variance and the correlation coefficient.
|
6th |
Analyzing the data of two-dimensional variables |
We can understand and explain of its distribution co-variance and the correlation coefficient.
|
7th |
The properties of probability |
We can understand and explain of the definition of probability and the number of cases.
|
8th |
The properties of probability |
We can understand and explain of its probability theorems of the addition and multiplication .
|
2nd Quarter |
9th |
The properties of probability |
We can understand and explain of its probability theorems of the addition and multiplication .
|
10th |
Mid-term examination |
|
11th |
The probability variables and its probability distributions |
We can understand and explain of the discrete variables and binomial distribution.
|
12th |
The probability variables and its probability distributions |
We can understand and explain of the continuous variables and normal distribution.
|
13th |
The probability variables and its probability distributions |
We can understand and explain of the continuous variables and normal distribution.
|
14th |
The fundamentals of statistic |
We can understand and explain of the statistics and sampling distribution.
|
15th |
Final examination |
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16th |
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Evaluation Method and Weight (%)
| Examination | Presentation | Mutual Evaluations between students | Behavior | Portfolio | Other | Total |
Subtotal | 60 | 0 | 0 | 0 | 40 | 0 | 100 |
Basic Proficiency | 30 | 0 | 0 | 0 | 20 | 0 | 50 |
Specialized Proficiency | 20 | 0 | 0 | 0 | 15 | 0 | 35 |
Cross Area Proficiency | 10 | 0 | 0 | 0 | 5 | 0 | 15 |