Key Concepts in Statistics: Data Analysis and Metrics
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data.
Population vs. Sample
- Population: All items of interest (e.g., all cars bought in Ontario last year). Note: It is often impossible to collect all these data points.
- Sample: Items randomly selected from the population (e.g., 1000 cars bought in Ontario last year).
Parameter vs. Statistic
- Parameter: A numerical description of the population.
- Statistic: A numerical description
Biostatistics in Medicine and Public Health: Key Concepts
Biostatistics in Medicine and Public Health
Biostatistics is the science that helps us make sense of data. It can be as simple as summarizing a distribution or identifying whether two variables are correlated. Statistics is crucial for biomedical science as it enables us to make sense of data that have been collected in an experiment or survey and to assess whether the observed result may just have arisen by chance.
- It tells us how to collect, organize, analyze, and interpret the collected numerical
Market Segmentation and Analysis Techniques
Market Segmentation Techniques
Cluster Analysis: This is a segmentation technique, a process by which we identify groups of consumers according to particular characteristics, with the aim of searching for a differentiated offer for each. We seek:
- Uniformity Inside: Maximize the difference between groups.
- Heterogeneity Between Groups: Maximize the difference between groups.
Conclusions: To determine what group/name and p-value > 0.05, accept the null hypothesis (H0) which means no difference between
Read MoreMultiple Linear Regression Assumptions and Diagnostics
Key Assumptions of Multiple Linear Regression (MLR)
Important Information from Midterm:
- MLR1: Linearity -> The model is linear in parameters. (Linearity = residuals have a mean of zero for every level of the fitted values and predictors)
- MLR2: Random Sample -> The data is randomly sampled from the population, ensuring that the sample represents the population.
- MLR3: No Perfect Collinearity -> The independent variables are not perfectly correlated, so the model can estimate coefficients uniquely.
Epidemiology: Understanding Disease Patterns and Risk Factors
Epidemiology
Epidemiology is the study of the distribution and determinants of disease or other health-related outcomes in human populations. It is also the application of that study to control health problems.
Etiology
- Etiology refers to all the determinants of a disease.
- These determinants can be physical, psychological, or behavioral.
- Rarely is there just one determinant.
Prevalence
- Period Prevalence: The proportion of cases over a length of time.
- Point Prevalence: The proportion of cases at one specific
Statistical Variables and Data Analysis Exercises
Exercise 1: Identifying Qualitative and Quantitative Variables
Indicate which variables are qualitative and which are quantitative:
- Favorite Food (Qualitative)
- Profession you like (Qualitative)
- Number of goals scored by your favorite team last season (Quantitative)
- Number of students in your Institute (Quantitative)
- The eye color of your classmates (Qualitative)
- IQ of your classmates (Quantitative)
Exercise 2: Identifying Discrete and Continuous Variables
Indicate which variables are discrete and which are
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