# Stats Chapter 1

Home > Flashcards > Print Preview

The flashcards below were created by user SusanneS28 on FreezingBlue Flashcards. What would you like to do?

1. Data
• Consist of information coming from
• observations, counts, measurements, or
• responses. ex “People who eat three daily servings of whole grains have been
• shown to reduce their risk of…stroke by 37%.”
2. Statistics
• The
• science of collecting, organizing, analyzing, and interpreting data in order to
• make decisions.
3. Data Sets
• Population
• The collection of all outcomes, responses,
• measurements, or counts that are of interest.
• Sample
4. A subset of the population.
5. Sample
A subset of the population.
6. Population
• The collection of all outcomes, responses,
• measurements, or counts that are of interest.
7. Parameter
• A number
• that describes a population characteristic.
• Average age of all people in the United States
8. Statistic
• A number
• that describes a sample
• characteristic.
9. Average age of people
from a sample of three states
10. Branches of Statistics
Descriptive Statistics Inferential Statistics
11. Descriptive Statistics
• Involves
• organizing, summarizing, and displaying data.
• e.g. Tables, charts, averages
12. Inferential Statistics
Involves using sample data to draw conclusions about a population.
13. Qualitative Data
• Consists
• of attributes, labels, or nonnumerical entries.
14. Quantitative data
Numerical measurements or counts
15. Designing a Statistical Study
• Identify
• the variable(s) of interest (the focus) and the population of the study.
• Develop a
• detailed plan for collecting data. If you use a sample, make sure the sample is
• representative of the population.
• Collect
• the data.
• Describe
• the data using descriptive statistics techniques.
• Interpret
• the data and make decisions about the population using inferential statistics.

• Identify
• any possible errors.
16. Observational study
• A
• researcher observes and measures characteristics of interest of part of a
• population.
• Researchers
• observed and recorded the mouthing behavior on nonfood objects of children up
• to three years old. (Source: Pediatric
• Magazine)
17. Experiment
• A
• treatment is applied to part of a population and responses are observed.
18. Simulation
• Uses a
• mathematical or physical model to reproduce the conditions of a situation or
• process.
• Often
• involves the use of computers.
• Automobile
• manufacturers use simulations with dummies to study the effects of crashes on
• humans.
19. Control
• for effects other than the one being
• measured.
20. Confounding variables
• §Occurs
• when an experimenter cannot tell the difference between the effects of
• different factors on a variable.
• §A coffee
• shop owner remodels her shop at the same time a nearby mall has its grand
• opening. If business at the coffee shop increases, it cannot be determined
• whether it is because of the remodeling or the new mall.
21. Placebo effect
• §A subject
• reacts favorably to a placebo when in fact he or she has been given no medical
• treatment at all.
• §Blinding is a technique where the subject does
• not know whether he or she is receiving a treatment or a placebo.
• §Double-blind experiment neither the subject nor the
• experimenter knows if the subject is receiving a treatment or a placebo.
22. Simple Random Sample
• Every
• possible sample of the same size has the same chance of being selected.
23. Stratified Sample
• Divide a
• population into groups (strata) and select a random sample from each group.
24. Cluster Sample
• Divide
• the population into groups (clusters) and select all of the members in one or
• more, but not all, of the clusters.
25. Systematic Sample
• Choose a
• starting value at random. Then choose every kth member of the population.
26. nominal level of measurement
• A
• variable is at the nominal level of measurement if the
• values of the variable name, label, or categorize. In addition, the naming scheme does not allow
• for the values of the variable to be arranged in a ranked, or specific, order.
27. ordinal level of measurement
• A
• variable is at the ordinal level of measurement if it
• has the properties of the nominal level of measurement and the naming scheme
• allows for the values of the variable to be arranged in a ranked, or specific,
• order.
28. interval level of measurement
• A
• variable is at the interval level of measurement if it
• has the properties of the ordinal level of measurement and the differences in
• the values of the variable have meaning.
• A value of zero in the interval level of measurement does not mean the
• absence of the quantity. Arithmetic
• operations such as addition and subtraction can be performed on values of the
• variable.
29. ratio level of measurement
• A
• variable is at the ratio level of measurement if it
• has the properties of the interval level of measurement and the ratios of the
• values of the variable have meaning. A
• value of zero in the ratio level of measurement means the absence of the
• quantity. Arithmetic operations such as
• multiplication and division can be performed on the values of the variable.
30. Confounding
• in a study occurs when the effects of two or more
• explanatory variables are not separated.
• Therefore, any relation that may exist between an explanatory variable
• and the response variable may be due to some other variable or variables not
• accounted for in the study.
31. lurking variable
• A lurking variable is an explanatory
• variable that was not considered in a study, but that affect the value of the
• response variable in the study. In
• addition, lurking variables are typically related to any explanatory variables
• considered in the study.
32. Cross-sectional
Studies
• Observational studies that collect information about
• individuals at a specific point in time, or over a very short period of time.
33. Case-control Studies
• These studies are retrospective,
• meaning that they require individuals to look back in time or require the
• researcher to look at existing records.
• In case-control studies, individuals that have certain characteristics
• are matched with those that do not.
34. Cohort Studies
• A cohort study first identifies a group of individuals
• to participate in the study (cohort).
• The cohort is then observed over a period of time. Over this time
• period, characteristics about the individuals are recorded. Because the data is collected over time,
• cohort studies are prospective.
35. census
• A census is a
• list of all individuals in a population along with certain characteristics of
• each individual.
36. Random sampling
• is the process of using chance to select individuals
• from a population to be included in the sample.
37. simple random sampling
• A sample of size n from a
• population of size N is obtained through simple random sampling if every possible
• sample of size n has an equally
• likely chance of occurring. The sample
• is then called a simple random sample.
38. The 110th Congress of the United States had 435 members
in the House of Representatives. Explain how to conduct a simple random sample
of 5 members to attend a Presidential luncheon.
Then obtain the sample.
Put the members in alphabetical order. Number the members from 1 - 435.
39. stratified sample
• A stratified sample is one
• obtained by separating the population into homogeneous, non-overlapping groups
• called strata, and then obtaining
• a simple random sample from each stratum.
40. A systematic sample
• A systematic sample is
• obtained by selecting every kth individual from the population. The first individual
• selected is a random number between 1 and k.
41. cluster sample
• A cluster sample is
• obtained by selecting all individuals within a randomly selected collection or
• group of individuals.
42. convenience sample
• A convenience sample is one
• in which the individuals in the sample are easily obtained.
• Any studies that use this type of
• sampling generally have results that are suspect. Results should be looked upon with extreme
• skepticism.
43. Bias
• If the
• results of the sample are not representative of the population, then the sample
• has bias.
• Three Sources of Bias
44. 1.Sampling Bias
45. 2.Nonresponse Bias
46. 3.Response Bias
47. Sampling bias
• means that the technique used to obtain the individuals
• to be in the sample tend to favor one part of the population over another.
48. Undercoverage
• Undercoverage is a
• type of sampling bias. Undercoverage occurs when the proportion of one segment of the
• population is lower in a sample than it is in the population.
49. Nonresponse bias
• Nonresponse bias exists when
• individuals selected to be in the sample who do not respond to the survey have
• different opinions from those who do.
50. Response bias
• exists when the answers on a survey do not reflect the
• true feelings of the respondent.
• Types of Response Bias
• Interviewer error
• Words used in survey question
• Order of the questions or words within the question
51. Nonsampling errors
• are errors that result from sampling bias, nonresponse bias,
• response bias, or data-entry error. Such
• errors could also be present in a complete census of the population.
52. Sampling error
• is error that results from using a sample to estimate
• information about a population. This type of error occurs because a sample
• gives incomplete information about a population.
53. raw data
• When data is collected from a survey or designed
• experiment, they must be organized into a manageable form. Data that is not organized is referred to as raw data.
54. frequency distribution
• A frequency distribution lists
• each category of data and the number of occurrences for each category of data.
55. relative frequency
• The relative frequency is the
• proportion (or percent) of observations within a category and is found using
• the formula:
• A relative frequency distribution lists the
• relative frequency of each category of data.
56. bar graph
• A bar graph is constructed by labeling each category of data on
• either the horizontal or vertical axis and the frequency or relative frequency
• of the category on the other axis.
57. Pareto chart
• A Pareto chart is a bar graph where
• the bars are drawn in decreasing order of frequency or relative frequency
58. Difference between discrete and continuos data
• The first step in
• summarizing quantitative data is to determine whether the data is discrete or
• continuous. If the data is discrete and
• there are relatively few different values of the variable, the categories of data
• will be the observations (as in qualitative data). If the data is discrete, but
• there are many different values of the variable, or if the data is continuous,
• the categories of data (called classes)
• must be created using intervals of numbers.
59. histogram
• A histogram is constructed by
• drawing rectangles for each class of data whose height is the frequency or
• relative frequency of the class. The
• width of each rectangle should be the same and they should touch each other.
60. stem-and-leaf plot
• A stem-and-leaf
• plot uses digits to the
• left of the rightmost digit to form the stem. Each rightmost digit
• forms a leaf.
• For example, a data
• value of 147 would have 14 as the stem and 7 as the leaf.
61. dot plot
• A dot
• plot is drawn by placing
• each observation horizontally in increasing order and placing a dot above the
• observation each time it is observed.
62. class midpoint
• The class
• midpoint is found by adding
• consecutive lower class limits and dividing the result by 2.
63. frequency polygon
• A frequency
• polygon is drawn by plotting
• a point above each class midpoint on a horizontal axis at a height equal to the
• frequency of the class. After the points
• for each class are plotted, draw straight lines between consecutive points.
64. cumulative frequency distribution
• A cumulative
• frequency distribution displays
• the aggregate frequency of the category.
• In other words, for discrete data, it displays the total number of
• observations less than or equal to the category. For continuous data, it displays the total
• number of observations less than or equal to the upper class limit of a class.
65. cumulative relative frequency distribution
• A cumulative
• relative frequency distribution
• displays the aggregate proportion (or percent) of observations less than or
• equal to the category.
66. ogive
• An ogive (read as “oh jive”)
• is a graph that represents the cumulative frequency or cumulative relative
• frequency for the class. It is
• constructed by plotting points whose x-coordinates are the upper class limits and whose y-coordinates are the
• cumulative frequencies or cumulative relative frequencies. After the points for each class are plotted,
• draw straight lines between consecutive points.
• An additional line segment is drawn connecting the upper limit of the
• class that would preceed the first class (if
• it existed).
67. time series data.
• If the value of a
• variable is measured at different points in time, the data is referred to as time
• series data.

### Card Set Information

 Author: SusanneS28 ID: 127757 Filename: Stats Chapter 1 Updated: 2012-01-14 18:09:40 Tags: Stats Chapter Folders: Description: Stats Chapter 1 Show Answers:

What would you like to do?

Home > Flashcards > Print Preview