SGTA (Small Group Teaching Activities): Formerly known as "workshop", begins next week.
Capacity Issues: Enrollment numbers have increased; some classes are full, monitoring for additional capacity.
Teaching Staff: New and experienced educators leading classes, all enthusiastic to help.
Reinforcement of Learning: Attendance is crucial for SAT1250; SGTA allows practice of material learned in lectures.
Sequential Learning: Activities in SGTA are based on topics from previous weeks to enhance understanding before moving to new content.
Assessment Preparation: Starting from week four, SGTA will include sessions focused on the first assessment (the report).
Lecture Schedule: First two weeks led by main lecturer, focusing on data introduction and summarization, with a transition to Excel skills.
Use of Laptops: Laptops are required for both statistics and Excel parts; they can be borrowed from the library if needed.
PowerPoints: Available at the back of the lecture hall for easy access.
Engagement with Societies: Encouragement to explore various societies, including the Maths and Stats Society.
Assessment Overview: Three assessments planned; 35% report due in week seven, online quantitative analysis task, and final exam during the exam period.
Special Considerations: Guidelines on handling late submissions and extensions through special considerations.
Interactive Lectures: Focus on engagement through discussion among peers.
Feedback Mechanism: A survey taken to gauge student expectations and concerns at the start of the semester, with a word cloud summarizing the feedback.
Worries About Skills: Relaying that everyone feels the pressure regarding data analysis and workload; reassurance of support provided by instructors.
Office Hours: Encourage students to reach out if they're feeling overwhelmed.
Data Literacy: Defined as the ability to analyze, interpret, and question data—a critical skill in today’s data-driven environment.
Types of Data Collection: Discussion on collecting new data versus using existing sources; various types of information handled by organizations.
Data: Described as raw, unprocessed figures or records.
Information: Data processed and analyzed to create meaning which aids in decision-making.
Main Objective: To derive information about a target population via sampling methods.
Steps in Data Analysis:
Study Design: Formulating a question and identifying the target population.
Data Collection: Gathering data via surveys or existing information.
Data Analysis: Using tools such as Excel for quantitative analysis.
Reporting: Delivering findings through report writing and presentations.
Sample vs. Population: Defining target population and ensuring sample selection is representative and unbiased.
Types of Studies:
Quantitative: Focuses on numerical data.
Qualitative: Encompasses descriptive data.
Observational vs. Experimental Studies: Differentiation based on whether the researcher intervenes in data collection.
Types of Variables:
Numerical: Continuous (e.g., weight, age) and discrete (e.g., number of students).
Categorical: Nominal (no order) and ordinal (ordered categories).
Importance of Variable Types: Influences statistical methods used and visualization techniques.
Bias Types: Definition and examples of selection bias, measurement bias, and response bias.
Importance of Representative Sampling: Ensuring that selected samples reflect the characteristics of the broader target population.
Available Support Services: Mentioned numeracy center, consultation hours, and overall encouragement for reaching out for assistance.