Biostat Lab 1-5

• Excel is convenient for data entry and manipulating rows and columns prior to statistical analysis.
• Ribbon start button - used to access commands such as creating new documents, saving existing work, and printing.
• Ribbon tabs - used to group similar commands together, with the home tab being used for basic commands like formatting data and finding specific data.
• Ribbon bar - used to group similar commands together, such as the Alignment ribbon bar for aligning data.
• Uses of SPSS (conduct statistical analysis, manipulate data, generate tables and graphs)
• Steps to enter quantitative data in SPSS (open new data editor window, enter data values, create variable names, define labels and values)
• How to define labels and values for variables in SPSS
• Changing the format display of text in a cell involves selecting the cell, right-clicking, selecting "Format Cells," choosing a format from the Number tab, and clicking OK.
• Sorting data can be done for the entire worksheet or a specific cell range, with options to sort A to Z or Z to A.
• Basic statistical functions in Excel include summation, count, mean/average, median, mode, variance, and standard deviation.
• A workbook is a collection of worksheets, while a worksheet is a collection of rows and columns.
• Worksheets can be renamed to more meaningful names.
• Data is collected to obtain information and is comprised of observations on one or more variables.
• Population of interest: The group or set of individuals or objects that the researcher wants to study.
• Variable: A numerical characteristic or attribute associated with the population being studied.
• Nominal: Categories are not ordered but simply have names. Example: Marital status.
• Ordinal: Categories are ordered in some way. Example: Disease staging systems and degree of pain.
• Discrete: Obtained by "counting" and can only take certain numerical values. Example: Number of asthma attacks.
• Continuous: There is no limitation on the values that the variable can take and obtained by "measuring". Example: Weight/height.
• Nominal scale: Classifies elements into two or more categories without indicating order or magnitude. Example: Religion.
• Ordinal scale: Ranks individuals in terms of the degree to which they possess a characteristic of interest. Example: Anxiety level.
• Interval scale: The unit of measurement is arbitrary and there is no "true zero" point. Example: Temperature.
• Ratio scale: Similar to interval scale but has an "absolute zero" in the scale. Example: Volume of reagent used in an experiment.
• Sample size: The number of individuals or objects included in a sample.
• Reasons for sampling: Complete enumerations are practically impossible, time constraints, limited resources, and destructive investigation.
• Frequency distribution table: A table that shows the number of observations falling into different classes or categories.
• Raw data: The original set of data.
• Array: An arrangement of observations according to their magnitude.
• Class frequency: The number of observations falling into a class.
• Class interval: The numbers defining the class.
• Class limits: The end numbers of the class.
• Class boundaries: The true class limits, often defined as halfway between the lower and upper class limits.
• Class size: The difference between the upper class boundaries of the class and the preceding class.
• Class mark: The midpoint of a class interval.
• Open-end class: A class that has no lower limit or upper limit.
• Sturges' formula: K = 1 + 3.322 log n (approximate number of classes)
• Class size determination:

  A. Solve for the range, R = max - min

  B. Compute for C' = R / K

  C. Round off C' to a convenient number, C, and use C as the class size.
• Determining lowest class limit and all class limits by adding the class size to the limit of the previous class.
• Tallying frequencies for each class and checking against the total number of observations.
• Relative Frequency (RF) Distribution and Relative Frequency Percentage (RFP):

  - RF = class frequency / no. of observations

  - RPF = RF * 100%