biostats midterms

• statistics - deals with scientific methods of collecting, organizing, summarizing, presenting, and analyzing data
• Data - are the values that the variables can assume
• random variables. - whose values are determined by chance
• Qualitative variables - are words or codes that represent a class or category
• quantitative variables - are numbers that represent an amount or a count.
• two types of variables = quantitative, qualitative
• Discrete variables - can be assigned values such as 0, 1, 2,3, … and are said to be countable
• continuous variables - can assume all values between any two specific values like 0.5, 1.2, etc
• Statistical Methods - are those procedures used in the collection, presentation, analysis, and interpretation of data
• Descriptive Statistics - comprises those methods concerned with collecting and describing a set of data so as to yield meaningful information
• Statistical Inference - comprises those methods concerned with the analysis of a subset of data leading to predictions or inferences about the entire set of data.
• Population- consists of the totality of the observations with which we are concerned
• Sample - is a subset of a population.
• Parameter – any numerical value describing a characteristics of a population
• Statistic – any numerical value describing a characteristic of a sample.
• Nominal level - this is characterized by data that consist names, labels, or categories only
• Ordinal level - this involves data that may be arranged in some order, but differences between data values either cannot be determined or are meaningless.
• Interval level - this is the same as the ordinal level, with an additional property that we can determine meaningful amounts of differences between the data. Data at this level may lack an inherent zero starting point.
• Ratio level - this is an interval level modified to include the inherent zero starting point. The difference and ratios of data are meaningful. This is also the highest level of measurement.
• types of samples - probability, non-probability
• quota sampling - in this type of sampling, the proportions of the various subgroups in the population are determined and the sample is drawn (usually not at random) to have the same percentages in it
• examples of non-probability sampling - accidental/incidental sampling, quota sampling, purposive sampling
• simple random sampling - each individual in the population has an equal chance of being drawn into the sample
• Systematic sampling - selects every kth element in the population for the sample, with the starting point to be determined at random from the first k elements.
• Cluster sampling - selects a sample containing either all, or a random selection
• Stratified random sampling - selects simple random samples from mutually exclusive subpopulations, or strata, of the population.
• average - is the measure of the center of the data in increasing or decreasing order
• measure of central tendency - mean, median, mode
• frequency histogram - uses bars to represent frequencies of the distribution of a set
• frequency polygon- is a line graph that emphasizes the changes in frequencies
• relative frequency histogram - has the same shape and horizontal scale as the frequency histogram. the difference is that the vertical scale is relative to the frequency of the category
• ogive - is a line graph that display the cumulative frequency of each class and its upper boundary
• normal distribution - Its graph called the normal curve, is bell-shaped and describes so many sets of data
• A continuous random variable X having the bell-shaped distribution- normal random variable
• In 1733, DeMoivre derived the mathematical equation of the normal curve.
• The normal distribution is often referred to as the Gaussian distribution in honor of Gauss
• Statistical Decisions - ery often in practice we are called upon to make decisions about populations on the basis of sample information. Such decisions are called statistical decisions.
• Statistical Hypotheses - in attempting to reach decisions, it is useful to make assumptions (or guesses) about the population involved.
• Null Hypothesis - in many instances we formulate a statistical hypothesis for the sole purpose of rejecting or nullifying it
• Alternative Hypothesis - A hypothesis that differs from the null hypothesis is called an alternative hypothesis