Test Bank for Elementary Statistics 10th by Triola

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Product Details:

• ISBN-10 ‏ : ‎ 0321331834
• ISBN-13 ‏ : ‎ 978-0321331830
• Author:  Mario F. Triola

Addison-Wesley is proud to celebrate the Tenth Edition of Elementary Statistics. This text is highly regarded because of its engaging and understandable introduction to statistics. The author’s commitment to providing student-friendly guidance through the material and giving students opportunities to apply their newly learned skills in a real-world context has made Elementary Statistics the #1 best-seller in the market.

 

Table of Content:

1 Elementary Statistics Tenth Edition
Lecture SlidesElementary Statistics Tenth Editionby Mario F. TriolaCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

2 Chapter 1 Introduction to Statistics
1-1 Overview1-2 Types of Data1-3 Critical Thinking1-4 Design of ExperimentsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

3 Created by Tom Wegleitner, Centreville, Virginia
Section 1-1OverviewCreated by Tom Wegleitner, Centreville, VirginiaCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

4 OverviewA common goal of studies and surveys and other data collecting tools is to collect data from a small part of a larger group so we can learn something about the larger group.In this section we will look at some of the ways to describe data.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

5 DefinitionDataobservations (such as measurements, genders, survey responses) that have been collectedCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

6 Definition Statistics
a collection of methods for planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the dataCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

7 Definition Population
the complete collection of all elements (scores, people, measurements, and so on) to be studied; the collection is complete in the sense that it includes all subjects to be studiedCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

8 DefinitionsCensusCollection of data from every member of a populationSampleSubcollection of members selected from a populationCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

9 Chapter Key ConceptsSample data must be collected in an appropriate way, such as through a process of random selection.If sample data are not collected in an appropriate way, the data may be so completely useless that no amount of statistical torturing can salvage them.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

10 Created by Tom Wegleitner, Centreville, Virginia
Section 1-2Types of DataCreated by Tom Wegleitner, Centreville, VirginiaCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

11 Key ConceptThe subject of statistics is largely about using sample data to make inferences (or generalizations) about an entire population. It is essential to know and understand the definitions that follow.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

12 Definition population parameter Parameter
a numerical measurement describing some characteristic of a population.populationparameterCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

13 Definition sample statistic Statistic
a numerical measurement describing some characteristic of a sample.samplestatisticCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

14 Definition Quantitative data
numbers representing counts or measurements.Example: The weights of supermodelsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

15 Definition Qualitative (or categorical or attribute) data
can be separated into different categories that are distinguished by some nonnumeric characteristicExample: The genders (male/female) of professional athletesCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

16 Working with Quantitative Data
Quantitative data can further be described by distinguishing between discrete and continuous types.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

17 Definition Discrete data (i.e. the number of possible values is
result when the number of possible values is either a finite number or a ‘countable’ number(i.e. the number of possible values is0, 1, 2, 3, . . .)Example: The number of eggs that a hen laysCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

18 Definition Continuous (numerical) data
result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions, or jumpsExample: The amount of milk that a cow produces; e.g gallons per dayCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

19 Levels of MeasurementAnother way to classify data is to use levels of measurement. Four of these levels are discussed in the following slides.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

20 Nominal level of measurement
DefinitionNominal level of measurementcharacterized by data that consist of names, labels, or categories only, and the data cannot be arranged in an ordering scheme (such as low to high)Example: Survey responses yes, no, undecidedCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

21 Ordinal level of measurement
DefinitionOrdinal level of measurementinvolves data that can be arranged in some order, but differences between data values either cannot be determined or are meaninglessExample: Course grades A, B, C, D, or FCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

22 Definition Interval level of measurement
like the ordinal level, with the additional property that the difference between any two data values is meaningful, however, there is no natural zero starting point (where none of the quantity is present)Example: Years 1000, 2000, 1776, and 1492Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

23 Definition Ratio level of measurement
the interval level with the additional property that there is also a natural zero starting point (where zero indicates that none of the quantity is present); for values at this level, differences and ratios are meaningfulExample: Prices of college textbooks ($0 represents no cost)Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

24 Summary – Levels of Measurement
Nominal – categories onlyOrdinal – categories with some orderInterval – differences but no natural starting pointRatio – differences and a natural starting pointCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

25 Recap In this section we have looked at:
Basic definitions and terms describing dataParameters versus statisticsTypes of data (quantitative and qualitative)Levels of measurementCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

26 Created by Tom Wegleitner, Centreville, Virginia
Section 1-3Critical ThinkingCreated by Tom Wegleitner, Centreville, VirginiaCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

27 Key ConceptsSuccess in the introductory statistics course typically requires more common sense than mathematical expertise.This section is designed to illustrate how common sense is used when we think critically about data and statistics.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

28 Misuses of StatisticsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

29 Misuse # 1- Bad Samples Voluntary response sample
(or self-selected sample)one in which the respondents themselves decide whether to be includedIn this case, valid conclusions can be made only about the specific group of people who agree to participate.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

30 Misuse # 2- Small Samples
Conclusions should not be based on samples that are far too small.Example: Basing a school suspension rate on a sample of only three studentsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

31 Misuse # 3- GraphsTo correctly interpret a graph, you must analyze the numerical information given in the graph, so as not to be misled by the graph’s shape.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

32 Misuse # 4- PictographsPart (b) is designed to exaggerate the difference by increasing each dimension in proportion to the actual amounts of oil consumption.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

33 Misuse # 5- PercentagesMisleading or unclear percentages are sometimes used. For example, if you take 100% of a quantity, you take it all. 110% of an effort does not make sense.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

34 Other Misuses of Statistics
Loaded QuestionsOrder of QuestionsRefusalsCorrelation & CausalitySelf Interest StudyPrecise NumbersPartial PicturesDeliberate DistortionsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

35 Recap In this section we have: Reviewed 13 misuses of statistics
Illustrated how common sense can play a big role in interpreting data and statisticsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

36 Created by Tom Wegleitner, Centreville, Virginia
Section 1-4Design of ExperimentsCreated by Tom Wegleitner, Centreville, VirginiaCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

37 Key ConceptIf sample data are not collected in an appropriate way, the data may be so completely useless that no amount of statistical tutoring can salvage them.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

38 Definition Observational study
observing and measuring specific characteristics without attempting to modify the subjects being studiedCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

39 Definition Experiment
apply some treatment and then observe its effects on the subjects; (subjects in experiments are called experimental units)Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

40 DefinitionsCross sectional studydata are observed, measured, and collected at one point in timeRetrospective (or case control) studydata are collected from the past by going back in timeProspective (or longitudinal or cohort) studydata are collected in the future from groups (called cohorts) sharing common factorsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

41 Definition Confounding
occurs in an experiment when the experimenter is not able to distinguish between the effects of different factorsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

42 Controlling Effects of Variables
Blindingsubject does not know he or she is receiving a treatment or placeboBlocksgroups of subjects with similar characteristicsCompletely Randomized Experimental Designsubjects are put into blocks through a process of random selectionRigorously Controlled Designsubjects are very carefully chosenCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

43 Replication and Sample Size
repetition of an experiment when there are enough subjects to recognize the differences from different treatmentsSample Sizeuse a sample size that is large enough to see the true nature of any effects and obtain that sample using an appropriate method, such as one based on randomnessCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

44 Definitions Random Sample Simple Random Sample (of size n)
members of the population are selected in such a way that each individual member has an equal chance of being selectedSimple Random Sample (of size n)subjects selected in such a way that every possible sample of the same size n has the same chance of being chosenCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

45 Random Sampling selection so that each individual member has an
equal chance of being selectedCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

46 Systematic Sampling Select some starting point and then
select every k th element in the populationCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

47 use results that are easy to get
Convenience Samplinguse results that are easy to getCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

48 subdivide the population into at
Stratified Samplingsubdivide the population into atleast two different subgroups that share the same characteristics, then draw a sample from each subgroup (or stratum)Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

49 divide the population into sections
Cluster Samplingdivide the population into sections(or clusters); randomly select some of those clusters; choose all members from selected clustersCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

50 Methods of Sampling – Summary
RandomSystematicConvenienceStratifiedClusterCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

51 Definitions Sampling error Nonsampling error
the difference between a sample result and the true population result; such an error results from chance sample fluctuationsNonsampling errorsample data incorrectly collected, recorded, or analyzed (such as by selecting a biased sample, using a defective instrument, or copying the data incorrectly)Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

52 Recap In this section we have looked at:
Types of studies and experimentsControlling the effects of variablesRandomizationTypes of samplingSampling errorsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

53 Lecture Slides Elementary Statistics Tenth Edition
and the Triola Statistics Seriesby Mario F. TriolaCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

54 Chapter 2 Summarizing and Graphing Data
2-1 Overview2-2 Frequency Distributions2-3 Histograms2-4 Statistical GraphicsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

55 Section 2-1 Overview Created by Tom Wegleitner, Centreville, Virginia
Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

56 Important Characteristics of Data
OverviewImportant Characteristics of Data1. Center: A representative or average value that indicates where the middle of the data set is located.2. Variation: A measure of the amount that the values vary among themselves.3. Distribution: The nature or shape of the distribution of data (such as bell-shaped, uniform, or skewed).4. Outliers: Sample values that lie very far away from the vast majority of other sample values.5. Time: Changing characteristics of the data over time.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

57 Frequency Distributions
Section 2-2Frequency DistributionsCreated by Tom Wegleitner, Centreville, VirginiaCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

58 Key ConceptWhen working with large data sets, it is often helpful to organize and summarize data by constructing a table called a frequency distribution, defined later. Because computer software and calculators can generate frequency distributions, the details of constructing them are not as important as what they tell us about data sets.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

59 Definition Frequency Distribution (or Frequency Table)
lists data values (either individually or by groups of intervals), along with their corresponding frequencies or countsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

60 Frequency Distribution Ages of Best Actresses
Original DataFrequency DistributionCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

61 Frequency Distributions
DefinitionsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

62 Lower Class Limits Lower Class Limits
are the smallest numbers that can actually belong to different classesLower ClassLimitsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

63 Upper Class Limits Upper Class Limits
are the largest numbers that can actually belong to different classesUpper ClassLimitsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

64 Class Boundaries Class Boundaries
are the numbers used to separate classes, but without the gaps created by class limitsEditor: Substitute Table 2-2ClassBoundaries20.530.540.550.560.570.580.5Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

65 Class Midpoints Class Midpoints
can be found by adding the lower class limit to the upper class limit and dividing the sum by twoClassMidpoints25.535.545.555.565.575.5Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

66 Class Width Class Width
is the difference between two consecutive lower class limits or two consecutive lower class boundariesEditor: Substitute Table 2-2ClassWidth10Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

67 Reasons for Constructing Frequency Distributions
1. Large data sets can be summarized.2. We can gain some insight into the nature of data.3. We have a basis for constructing important graphs.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

68 Constructing A Frequency Distribution
1. Decide on the number of classes (should be between 5 and 20).2. Calculate (round up).class width (maximum value) – (minimum value)number of classes3. Starting point: Begin by choosing a lower limit of the first class.Using the lower limit of the first class and class width, proceed to list the lower class limits.5. List the lower class limits in a vertical column and proceed to enter the upper class limits.6. Go through the data set putting a tally in the appropriate class for each data value.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

69 Relative Frequency Distribution
includes the same class limits as a frequency distribution, but relative frequencies are used instead of actual frequenciesrelative frequency =class frequencysum of all frequenciesCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

70 Relative Frequency Distribution
28/76 = 37%30/76 = 39%etc.Total Frequency = 76Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

71 Cumulative Frequency Distribution
FrequenciesCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

72 Frequency TablesCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

73 Critical Thinking Interpreting Frequency Distributions
In later chapters, there will be frequent reference to data with a normal distribution. One key characteristic of a normal distribution is that it has a “bell” shape.The frequencies start low, then increase to some maximum frequency, then decrease to a low frequency.The distribution should be approximately symmetric.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

74 Recap In this Section we have discussed
Important characteristics of dataFrequency distributionsProcedures for constructing frequency distributionsRelative frequency distributionsCumulative frequency distributionsCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

75 Section 2-3HistogramsCreated by Tom Wegleitner, Centreville, VirginiaCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

76 Key ConceptA histogram is an important type of graph that portrays the nature of the distribution.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

77 HistogramA bar graph in which the horizontal scale represents the classes of data values and the vertical scale represents the frequenciesCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

78 Relative Frequency Histogram
Has the same shape and horizontal scale as a histogram, but the vertical scale is marked with relative frequencies instead of actual frequenciesCopyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

79 Critical Thinking Interpreting Histograms
One key characteristic of a normal distribution is that it has a “bell” shape. The histogram below illustrates this.Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

 

 

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