BMS458 Quantitative Methods For Biology UITM Assignment Sample Malaysia

BMS458 Quantitative Methods for Biology is an introductory course in biological data analysis. Its primary aim is to provide students with essential analytical knowledge and tools to support their studies in biological sciences. The course covers fundamental topics such as basic probability theory, probability distributions (normal, binomial, and Poisson), elementary sampling theory, estimation, hypothesis testing, and analysis of variance.

Emphasis is placed on the practical application of learned concepts during laboratory sessions. In these sessions, students will gain hands-on experience in formulating testable hypotheses, designing scientifically sound experiments, conducting experiments, and analyzing data using the methods taught in the lectures. Through this approach, students will develop critical skills needed to navigate real-world scientific challenges.

Overall, BMS458 equips students with the foundational quantitative skills required to excel in their future pursuits within the field of biological sciences.

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Assignment Task 1 : Describe data variables and fundamental concepts of hypothesis testing in biological data analysis

In biological data analysis, data variables refer to the characteristics or attributes that are measured or observed in a study. These variables can be of different types:

• Continuous Variables: These variables can take any numerical value within a specific range. For example, height, weight, temperature, etc.
• Categorical Variables: These variables represent categories or groups and are often non-numerical. Examples include gender, species type, treatment groups, etc.
• Ordinal Variables: These variables have categories with a natural order or ranking but the exact numerical difference between the categories is not well-defined. For instance, rating scales (e.g., Likert scales: strongly agree, agree, neutral, disagree, strongly disagree).
• Binary Variables: These are categorical variables with only two possible outcomes, often represented as 0 or 1, true or false, yes or no, etc.

Fundamental concepts of hypothesis testing in biological data analysis include:

• Null Hypothesis (H0): It is the default assumption that there is no significant difference or effect between groups or variables. Researchers aim to reject this hypothesis with statistical evidence.
• Alternative Hypothesis (Ha): This is the hypothesis that researchers want to support, suggesting that there is a significant difference or effect in the data.
• Significance Level (α): It denotes the probability of rejecting the null hypothesis when it is actually true. Commonly used significance levels are 0.05 (5%) and 0.01 (1%).
• P-value: It is the probability of obtaining results as extreme as observed, assuming that the null hypothesis is true. A p-value less than the significance level suggests evidence to reject the null hypothesis.
• Type I Error (False Positive): Occurs when the null hypothesis is incorrectly rejected.
• Type II Error (False Negative): Occurs when the null hypothesis is incorrectly accepted.

Assignment Task 2 : Calculate statistics commonly used in biological data analysis

In biological data analysis, various statistics are used to summarize and analyze data. Some commonly used statistics include:

• Mean: The arithmetic average of a set of values, calculated by summing all values and dividing by the total number of values.
• Standard Deviation (SD): It measures the dispersion or spread of data points around the mean.
• Median: The middle value in a dataset when the values are arranged in ascending or descending order.
• Range: The difference between the maximum and minimum values in a dataset.
• Variance: The average of the squared differences from the mean. It quantifies the variability of data.
• Correlation Coefficient: Measures the strength and direction of the linear relationship between two variables, often denoted by “r.”

Assignment Task 3 : Apply appropriate statistical tests and tools on biological data

In biological data analysis, the choice of statistical tests depends on the type of data and the research question being addressed. Some common statistical tests used are:

• T-tests: Used to compare means between two groups, either independent or paired.
• ANOVA (Analysis of Variance): Used to compare means between three or more groups.
• Chi-square test: Used to analyze categorical data and determine if there is an association between variables.
• Regression analysis: To examine the relationship between a dependent variable and one or more independent variables.
• Mann-Whitney U test and Kruskal-Wallis test: Non-parametric tests for comparing groups when assumptions of normality are not met.
• Pearson correlation or Spearman correlation: Used to assess the strength of the relationship between two continuous variables.

Assignment Task 4 : Perform analysis on biological data using spreadsheet functions and other software tools.

To perform analysis on biological data, spreadsheet functions like Microsoft Excel or Google Sheets can be used for basic calculations such as means, medians, standard deviations, and creating graphs. More advanced analysis might require specialized software tools like:

• R: A powerful statistical programming language with numerous packages for data analysis, visualization, and hypothesis testing.
• Python: Similar to R, Python has various libraries (e.g., NumPy, SciPy, Pandas) that support data manipulation and statistical analysis.
• SPSS: A software package commonly used for statistical analysis in social sciences, including biology and health-related research.
• GraphPad Prism: A tool specialized for scientific graphing and statistical analysis.
• JMP: A user-friendly statistical software often used in life sciences and research.

These tools can handle more complex statistical tests and provide a more in-depth exploration of biological data, enabling researchers to draw meaningful conclusions from their experiments.

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