Course Structure and Assessments for MAT2001: Statistical Reasoning

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What You Will Learn in MAT2001: Statistical Reasoning

In this course, students will develop a solid understanding of how to organize, analyze, and interpret data. Emphasis is placed on using statistical reasoning to solve problems, interpret results, and apply statistical methods to make informed decisions in professional and academic contexts. The course focuses on building a strong foundation in statistical concepts and techniques, with a focus on practical application and real-world examples.

Key Topics Covered in MAT2001: Statistical Reasoning

The course covers a range of essential topics in statistics, including:

  • Descriptive Statistics: Learn how to summarize and describe the essential features of data sets using measures of central tendency (mean, median, mode), variability (range, variance, standard deviation), and graphical representations (histograms, bar charts, box plots).

  • Probability: Understand the fundamental concepts of probability, including outcomes, events, and the laws of probability. Learn how to calculate probabilities and use probability distributions to model real-world scenarios.

  • Sampling and Data Collection: Study different methods of sampling and data collection, including random sampling, stratified sampling, and survey techniques. Understand the importance of sample size and the potential for bias in data collection.

  • Probability Distributions: Learn about common probability distributions such as the normal distribution, binomial distribution, and Poisson distribution. Understand their applications and how they help model real-world phenomena.

  • Inferential Statistics: Gain an understanding of hypothesis testing, confidence intervals, and significance levels. Learn how to make inferences about populations based on sample data, and interpret p-values and test results.

  • Correlation and Regression: Study how to analyze relationships between variables using correlation and regression analysis. Learn how to interpret regression models and predict outcomes based on data.

  • Chi-Square Tests: Understand the chi-square test for independence and the chi-square goodness-of-fit test. Learn how these tests are used to analyze categorical data.

  • Statistical Software: Gain experience using statistical software tools, such as Excel, SPSS, or R, to perform data analysis and interpret results.

Learning Objectives of MAT2001: Statistical Reasoning

By the end of this course, students will:

  • Understand Key Statistical Concepts: Master the fundamentals of descriptive and inferential statistics, including probability, hypothesis testing, and regression analysis.

  • Apply Statistical Methods: Learn how to apply statistical reasoning to solve real-world problems and make data-driven decisions.

  • Analyze Data: Develop the ability to organize, summarize, and analyze data using statistical techniques, and communicate findings clearly.

  • Use Statistical Software: Gain proficiency in using software tools to perform statistical analysis and interpret results.

  • Interpret Statistical Results: Understand how to draw valid conclusions from statistical data and interpret the implications of statistical findings in various contexts.

  • Understand Sampling and Data Collection: Learn the importance of proper sampling techniques and data collection methods in producing valid statistical results.

Who Should Take MAT2001: Statistical Reasoning?

MAT2001 is ideal for students pursuing a Bachelor of Science in Information Technology, business, healthcare, or any field that involves data analysis. The course is especially valuable for:

  • Business Majors: Students in business administration, marketing, or finance who need to understand how to analyze data to make informed business decisions.

  • Healthcare Students: Students in health sciences or healthcare administration who require statistical reasoning to understand research, clinical trials, and public health data.

  • Social Science Majors: Students in psychology, sociology, or economics who need statistical tools to analyze survey data, experimental results, and other data sets.

  • Data Enthusiasts: Individuals who are interested in developing their skills in data analysis and applying statistical reasoning to solve real-world problems.

  • Aspiring Data Analysts: Students who want to build a foundation in statistics that can lead to careers in data science, market research, or analytics.

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