test 1 biotstat vocabulary

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test 1 biotstat vocabulary
2015-02-24 04:21:41
biostats biostatistics statistics

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  1. statistics
    The study of the collection and organization and analysis and interpretation and presentation of data,
  2. statistic
    Numerical summary and calculated measured data.
  3. statements of research question
  4. Data collection
    Designed careful study (observation, experiments)
  5. Data analysis
    • compute the consequences of your hypothesis
    • what are the chances of seeing my result when the null hypothesis is true.
  6. statistical inference
    Interpret the analysis in terms of the statistic.
  7. Variable
    a characteristic or quality that can change from individual to individual (or event)
  8. Categorical (qualitative) variable

    Classified by the type or group

    Values whose original observation is a catagory and order does not matter (eye color)
  9. Categorical (qualitative) variable

    Rank (ordinal)
    Classified by the type or group

    values have defined order, hierarchy that matters (year in school, grades)
  10. Quantitative variable

    Discrete variable
    grouped later to make cata data, variable us measured originally as a number

    integer, whole number, no fraction
  11. Quantitative variable

    continuous variable
    variable measured as a number

    any possible numerical value like ratio, interval
  12. Population
    Complete set of items that share at least one property in common that is the subject of a statistical analysis.
  13. A summary measure of a population is a
  14. A summary measure of a sample is a
  15. Observational study
    attempt to understand cause-and-effect relationships. However, unlike experiments, the researcher is not able to control
  16. Experimental study
    An experiment is any process or study which results in the collection of data, the outcome of which is unknown. In statistics, the term is usually restricted to situations in which the researcher has control over some of the conditions under which the experiment takes place.
  17. Explanatory (Independent) variable
    In an experiment, the independent variable is thevariable that is varied or manipulated by the researcher
  18. Response (dependent) variable
    The dependent variable is the response that is measured. An independent variableis the presumed cause, whereas the dependent variable is the presumed effect.
  19. Comparative study
    Two or more groups compared to determine diff in value  variable of interest
  20. Descriptive study
    characterize or ID certain attributes (Qualitative/categorical) Describes values of variable.
  21. Confounding Variables
    An extraneous variable. It correlates with both the dependent var and independent var
  22. Spurious associations
    Apparently but actually false misleadingly false. Apparent but not valid.
  23. Independence
    Randomness ensures independence. The inclusion of one indiv or even in a study does not affect the chance of an other indiv to be included in the study.
  24. Randomness
    Each possible sample was the same chance of being selected as any other sample.
  25. Factors
    explanatory variables
  26. Treatments
    levels of the factor
  27. Control Treatment
    No treatment, placebo
  28. randomness
    andomisation is the process by which experimental units (the basic objects upon which the study or experiment is carried out) are allocated to treatments; that is, by a random process
  29. Replication
    Within the experiment, different sample same procedure
  30. Repeatability
    External, repeating the experiment with new subjects and similar results
  31. Categorical Variables - Frequency
    The number of times the event occurred in an experiment or study.
  32. Categorical Variables - frequency distributions
    a table that displays the frequency of various outcomes in a sample. Each entry in the table contains thefrequency or count of the occurrences of values within a particular group or interval, and in this way, the table summarizes the distribution of values in the sample.
  33. Relative frequency/Proportion
    Relative frequency is another term for proportion; it is the value calculated by dividing the number of times an event occurs by the total number of times an experiment is carried out. The probability of an event can be thought of as its long-run relative frequency when the experiment is carried out many times.If an experiment is repeated n times, and event E occurs r times, then the relative frequency of the event E is defined to berfn(E) = r/n
  34. Continuous quantitative variables - histogram
    Created by constructing class intervals or bind which are equally sized ranges of values associated with the variable.
  35. Accuracy
    Central tendency (most likely to occur) (mean, median, mode)
  36. Precision
    How well a value is repeated (dispersion)
  37. Uniform distribution
    same in each bin
  38. symmetric/unimodal distribution
    Most freq occurring value
  39. Right-skewed distribution
    • Lowest value has the highest frequency.
    • Positive trend.
  40. Left-skewed distribution
    • negative trend 
    • highest value has the highest frequency
  41. Bimondal
    two modes, two separate high frequencies
  42. Bimodal skewed distribution
    One monster mode and a mini mode.
  43. central tendency
    the most common numerical measure of central tendency, which is a measure of center of the distribution (mean average).
  44. Medians (M)
    The midpoint of a distribution, the number such that half of the observation are smaller and the other half are larger.
  45. Relationship between shape and central tendency
    • right skew - y>M
    • left skew - y<M
  46. Dispersion (variability) - range
  47. Dispersion (variability) - quarile
    3 quartiles that divides the number of observations into 4 equal parts

    first quartile Q1 the median of observations bellow the median.

    third quartile Q3 the median of obervations  above the median.
  48. Dispersion - Inter-quartile Range
    Q3 - Q1 
  49. Dispersion- variance- (s2)
    the average of the squares of deviations of the observations from their mean.
  50. standard deviation (s)
    • Square root of the variance 
    • Measures the spread of the mean
  51. Outliers
    The observations whose values to not follow the trend or bulk of the rest of the data.

    • Values less than Q1 - 1.5 (IQR)
    • Values more than Q3 + 1.5 (IQR)
  52. Probability
    How often a specific event occurs in repeated samples or repeated processes.

    P(E)=(number of ways even
  53. The Binomial Distribution
    • 1. The experiment consists of n identical trials.
    • 2. The experiment consists of one of two outcomes. 
    • 3. The probability success on a single trial is equal to pi and pi remains the same from trial to trial.
    • 4. The trials are independent; that is, the outcome of one trial does not influence the outcomes of any other trial.
    • 5. The random variable y is the number of success observed during the n trials.

    p(y) = (n!/(y!(n-y)!)) *piy * (1-pi)(n-y)
  54. The chi-square goodness-of-fit
    It test whether or not the viability observed in the frequencies is simply due to random chance or if it is improbable that the specified distribution is not the frequency distribution specified.

    • 1. Measurement is on at least a nominal scale.
    • 2. Categories or groups are mutually exclusive.
    • 3. Observations are independent.
    • 4. No category has a sample size restriction.
  55. p-value
    The probability that, If the null were true, sampling variation would produce an estimate that is further away from the hypothesized value than our data estimate.

    How likely it is to get that hypothesis if the null were true.
  56. Chi-square test for independence
    • a. Data are frequencies based on random sample.
    • b. Samples are independent.
    • c. value restriction

    • 2. null: There is no relationship, the variables are independent.
    •  alt. :There is a relationship, the two variables are dependent.

    3.  Epected

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