Syllabus

# Grade the total score in the course will be a

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Unformatted text preview: ill be a weighted average of the results of quizzes, problem sets, and exams. The weights (percentage of total score) of various assignments in the total score are as follows: Problem sets: 30% Quick quizzes 5% (Extra points, if eligible.) Long quizzes 30% Midterm 20% Final 20% 3 The overall grade in this course will be based on the following grade scheme. There will be a minimum passing grade (D) of 40% of the course maximum (score of 40 out of 100). The C- grade will be 50% of the course maximum (score of 50 out of 100). A AB+ B BC+ 90 – 100% 85 – 89.9% 80 – 84.9% 75 – 79.9% 70 - 74.9% 65 – 69.9% C CD+ D E 60 – 64.9% 50 – 59.9% 45 – 49.9% 40 – 44.9% Below 40% Tentative Course Schedule* Please note that the following schedule is tentative and the lesson plan or exams dates are subject to change according to the pace of the class. Any changes to the exam dates on this table will be announced in advance in class and on Carmen. Week 1: Math and Stats Review Math: Summation operator, Functions, Derivatives Stats: Random variables, Distributions Ch. 17 Working with statistical tables Week 2: Introduction What is Regression Analysis? Ch 1 Types of datasets, Variables, Summary statistics Week 3: Ordinary Least Squares Simple Linear Regression (SLR) Ch 2 Using spreadsheets to conduct SLR analysis Week 4: Multiple Linear Regression (MLR) Ch 2 Using GRETL to conduct SLR analysis Interpreting the MLR results Classical Assumptions Ch. 4 Week 5: Hypot...
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