1.1 - 1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Lecture Slides Elementary Statistics Eleventh Edition and the Triola.

1.1 - 2 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Chapter 1 Introduction to Statistics 1-1Review and Preview 1-2Statistical Thinking 1-3Types of Data 1-4Critical Thinking 1-5Collecting Sample Data

1.1 - 5 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Preview Polls, studies, surveys and other data collecting tools collect data from a small part of a larger group so that we can learn something about the larger group. This is a common and important goal of statistics: Learn about a large group by examining data from some of its members.

1.1 - 6 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Preview In this context, the terms sample and population have special meaning. Formal definitions for these and other basic terms will be given here. In this section we will look at some of the ways to describe data.

1.1 - 8 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Statistics is the science of planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data Statistics

1.1 - 9 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Population  Population the complete collection of all individuals (scores, people, measurements, and so on) to be studied; the collection is complete in the sense that it includes all of the individuals to be studied

1.1 - 10 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Census versus Sample  Census Collection of data from every member of a population  Sample Subcollection of members selected from a population

1.1 - 11 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Chapter Key Concepts  Sample 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.

1.1 - 13 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Key Concept This section introduces basic principles of statistical thinking used throughout this book. Whether conducting statistical analysis of data that we have collected, or analyzing a statistical analysis done by someone else, we should not rely on blind acceptance of mathematical calculation. We should consider these factors:

1.1 - 14 Parameter A numerical measurement describing some characteristic of a population Example: In New York City, there are 3250 walk buttons that pedestrians can press at traffic intersections. It was found that 77% of those buttons do not work (based on data from the article “For Exercise in New York Futility, Push Button” by Michael Luo, New York Times). The figure of 77% is a parameter because it is based on the entire population of all 3250 pedestrian push buttons Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 15 Statistic A statistic is a numerical measurement describing some characteristic of a sample. Example: Based on a sample of 877 surveyed executives, it is found that 45% of them would not hire someone with a typographic error on their job application. That figure of 45% is a statistic because it is based on a sample, not the entire population of all executives. Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 16 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Context of the data  Source of the data  Sampling method  Conclusions  Practical implications Key Concept (continued)

1.1 - 17 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Context  What do the values represent?  Where did the data come from?  Why were they collected?  An understanding of the context will directly affect the statistical procedure used.

1.1 - 18 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Source of data  Is the source objective?  Is the source biased?  Is there some incentive to distort or spin results to support some self- serving position?  Is there something to gain or lose by distorting results?  Be vigilant and skeptical of studies from sources that may be biased.

1.1 - 19 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Sampling Method  Does the method chosen greatly influence the validity of the conclusion?  Voluntary response (or self-selected) samples often have bias (those with special interest are more likely to participate). These samples’ results are not necessarily valid.  Other methods are more likely to produce good results.

1.1 - 20 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Conclusions  Make statements that are clear to those without an understanding of statistics and its terminology.  Avoid making statements not justified by the statistical analysis.

1.1 - 21 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Practical Implications  State practical implications of the results.  There may exist some statistical significance yet there may be NO practical significance.  Common sense might suggest that the finding does not make enough of a difference to justify its use or to be practical.

1.1 - 22 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Statistical Significance  Consider the likelihood of getting the results by chance.  If results could easily occur by chance, then they are not statistically significant.  If the likelihood of getting the results is so small, then the results are statistically significant.

1.1 - 23 Quantitative Data Quantitative data consist of numbers representing counts or measurements Example: The weights of supermodels The heights of the basketball team Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 24 Qualitative (or categorical or attribute) data Qualitative data can be separated into different categories that are distinguished by some nonnumeric characteristic Example: The genders (male/female) of professional athletes The colors in a bag of m&m’s Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 25 Discrete data Discrete data result when the number of possible values is either a finite number or a “countable” number (0 or 1 or 2 and so on) Example: The numbers of eggs that hens lay Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 26 Continuous (numerical) data 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 jumps. Example: The amounts of milk from cows Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 27 Nominal level of measurement Nominal level of measurement is characterized by data that consist of names, labels, or categories only. The data cannot be arranged in an ordering scheme (such as low to high) Examples: survey responses of yes, no, and undecided Colors Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 28 Ordinal level of measurement Data are at the Ordinal level of measurement if they can be arranged in some order, but differences between data values either cannot be determined or are meaningless. Examples: Course Grades, ranks Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 29 Interval level of measurement Interval level of measurement is like the ordinal level, with the additional property that the difference between any two data values is meaningful. However, data at this level do not have a natural zero starting point (where none of the quantity is present) Examples: Temperatures Years: Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 30 Ratio level of measurement Interval level of measurement is 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 both meaningful Examples: Weights, prices Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 31 Identify the population and the sample: a) A survey of 1353 American households found that 18% of the households own a computer. b) A recent survey of 2625 elementary school children found that 28% of the children could be classified obese. c) The average weight of every sixth person entering the mall within 3 hour period was 146 lb. Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 32 Determine whether the numerical value is a parameter or a statistics (and explain): a) A recent survey by the alumni of a major university indicated that the average salary of 10,000 of its 300,000 graduates was 125,000. b) The average salary of all assembly-line employees at a certain car manufacturer is $33,000. c) The average late fee for 360 credit card holders was found to be $56.75. Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 33 Determine whether the data are qualitative or quantitative a) the colors of automobiles on a used car lot b) the numbers on the shirts of a girl’s soccer team c) the number of seats in a movie theater d) a list of house numbers on your street e) the ages of a sample of 350 employees of a large hospital Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 34 Identify the data set’s level of measurement (nominal, ordinal, interval, ratio) a) hair color of women on a high school tennis team b) numbers on the shirts of a girl’s soccer team c) ages of students in a statistics class d) temperatures of 22 selected refrigerators e) number of milligrams of tar in 28 cigarettes f) number of pages in your statistics Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 35 Identify the data set’s level of measurement (nominal, ordinal, interval, ratio) g) marriage status of the faculty at the local community college h) list of 1247 social security numbers i) the ratings of a movie ranging from “poor” to “good” to “excellent” j) the final grades (A,B,C,D, and F) for students in a chemistry class k) the annual salaries for all teachers in Utah l) list of zip codes for Chicago m) the nationalities listed in a recent survey n) the amount of fat (in grams) in 44 cookies Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 37 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Key Concept The 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.

1.1 - 40 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Quantitative Data  Quantitative (or numerical) data consists of numbers representing counts or measurements. Example: The weights of supermodels Example: The ages of respondents

1.1 - 41 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Categorical Data  Categorical (or qualitative or attribute) data consists of names or labels (representing categories) Example: The genders (male/female) of professional athletes Example: Shirt numbers on professional athletes uniforms - substitutes for names.

1.1 - 42 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Working with Quantitative Data Quantitative data can further be described by distinguishing between discrete and continuous types.

1.1 - 43 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Discrete data result when the number of possible values is either a finite number or a ‘countable’ number (i.e. the number of possible values is 0, 1, 2, 3,...) Example: The number of eggs that a hen lays Discrete Data

1.1 - 44 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  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 jumps Continuous Data Example: The amount of milk that a cow produces; e.g. 2.343115 gallons per day

1.1 - 45 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Levels of Measurement Another way to classify data is to use levels of measurement. Four of these levels are discussed in the following slides.

1.1 - 46 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Nominal level of measurement characterized 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, undecided Nominal Level

1.1 - 47 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Ordinal level of measurement involves data that can be arranged in some order, but differences between data values either cannot be determined or are meaningless Example: Course grades A, B, C, D, or F Ordinal Level

1.1 - 48 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  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 1492 Interval Level

1.1 - 49 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  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 meaningful Example: Prices of college textbooks ($0 represents no cost, a $100 book costs twice as much as a $50 book) Ratio Level

1.1 - 50 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Summary - Levels of Measurement  Nominal - categories only  Ordinal - categories with some order  Interval - differences but no natural starting point  Ratio - differences and a natural starting point

1.1 - 51 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Recap  Basic definitions and terms describing data  Parameters versus statistics  Types of data (quantitative and qualitative)  Levels of measurement In this section we have looked at:

1.1 - 53 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Key Concepts  Success in the introductory statistics course typically requires more common sense than mathematical expertise.  Improve skills in interpreting information based on data.  This section is designed to illustrate how common sense is used when we think critically about data and statistics.  Think carefully about the context, source, method, conclusions and practical implications.

1.1 - 54 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Misuses of Statistics 1.Evil intent on the part of dishonest people. 2.Unintentional errors on the part of people who don’t know any better. We should learn to distinguish between statistical conclusions that are likely to be valid and those that are seriously flawed.

1.1 - 55 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. To 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. READ labels and units on the axes! Graphs

1.1 - 56 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Part (b) is designed to exaggerate the difference by increasing each dimension in proportion to the actual amounts of oil consumption. Pictographs

1.1 - 57 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Bad Samples  Voluntary response sample (or self-selected sample) one in which the respondents themselves decide whether to be included In this case, valid conclusions can be made only about the specific group of people who agree to participate and not about the population.

1.1 - 58 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Correlation and Causality  Concluding that one variable causes the other variable when in fact the variables are linked Two variables may seemed linked, smoking and pulse rate, this relationship is called correlation. Cannot conclude the one causes the other. Correlation does not imply causality.

1.1 - 59 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 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 students

1.1 - 60 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Percentages Misleading or unclear percentages are sometimes used. For example, if you take 100% of a quantity, you take it all. If you have improved 100%, then are you perfect?! 110% of an effort does not make sense.

1.1 - 61 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Loaded Questions If survey questions are not worded carefully, the results of a study can be misleading. Survey questions can be “loaded” or intentionally worded to elicit a desired response. Too little money is being spent on “welfare” versus too little money is being spent on “assistance to the poor.” Results: 19% versus 63%

1.1 - 62 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Order of Questions Questions are unintentionally loaded by such factors as the order of the items being considered. Would you say traffic contributes more or less to air pollution than industry? Results: traffic - 45%; industry - 27% When order reversed. Results: industry - 57%; traffic - 24%

1.1 - 63 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Nonresponse Occurs when someone either refuses to respond to a survey question or is unavailable. People who refuse to talk to pollsters have a view of the world around them that is markedly different than those who will let poll-takers into their homes.

1.1 - 64 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Missing Data Can dramatically affect results. Subjects may drop out for reasons unrelated to the study. People with low incomes are less likely to report their incomes. US Census suffers from missing people (tend to be homeless or low income).

1.1 - 65 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Self-Interest Study Some parties with interest to promote will sponsor studies. Be wary of a survey in which the sponsor can enjoy monetary gain from the results. When assessing validity of a study, always consider whether the sponsor might influence the results.

1.1 - 66 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Precise Numbers Because as a figure is precise, many people incorrectly assume that it is also accurate. A precise number can be an estimate, and it should be referred to that way.

1.1 - 67 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Deliberate Distortion Some studies or surveys are distorted on purpose. The distortion can occur within the context of the data, the source of the data, the sampling method, or the conclusions.

1.1 - 68 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Recap  Reviewed misuses of statistics  Illustrated how common sense can play a big role in interpreting data and statistics In this section we have:

1.1 - 70 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Key Concept  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.  Method used to collect sample data influences the quality of the statistical analysis.  Of particular importance is simple random sample.

1.1 - 71 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Statistical methods are driven by the data that we collect. We typically obtain data from two distinct sources: observational studies and experiment. Basics of Collecting Data

1.1 - 72 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Observational study observing and measuring specific characteristics without attempting to modify the subjects being studied Observational Study

1.1 - 73 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Experiment apply some treatment and then observe its effects on the subjects; (subjects in experiments are called experimental units) Experiment

1.1 - 75 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Cross sectional study data are observed, measured, and collected at one point in time  Retrospective (or case control) study data are collected from the past by going back in time (examine records, interviews, …)  Prospective (or longitudinal or cohort) study data are collected in the future from groups sharing common factors (called cohorts) Types of Studies

1.1 - 76 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Confounding occurs in an experiment when the experimenter is not able to distinguish between the effects of different factors. Try to plan the experiment so that confounding does not occur. Confounding

1.1 - 77 Example of Confounding Vermont professor experiments with a new attendance policy. The new policy is that your course average will drop one point for each class you miss. An exceptionally mild winter lacks the snow and cold temperatures that have caused people to miss in the pass, so… if attendance does improve we can’t determine whether the improvement was because of the new policy OR the mild winter. The effects of the attendance policy and the weather have been confounded. Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 78 Three important considerations in experiment design 1.Control the effects of variables. 2.Use replication. 3.Use randomization. Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.

1.1 - 79 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Randomization is used when subjects are assigned to different groups through a process of random selection. The logic is to use chance as a way to create two groups that are similar. Randomization

1.1 - 80 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Replication is the repetition of an experiment on more than one subject. Samples should be large enough so that the erratic behavior that is characteristic of very small samples will not disguise the true effects of different treatments. It is used effectively when there are enough subjects to recognize the differences from different treatments. Replication Use a sample size that is large enough to let us see the true nature of any effects, and obtain the sample using an appropriate method, such as one based on randomness.

1.1 - 81 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Blinding is a technique in which the subject doesn’t know whether he or she is receiving a treatment or a placebo. Blinding allows us to determine whether the treatment effect is significantly different from a placebo effect, which occurs when an untreated subject reports improvement in symptoms. Blinding

1.1 - 82 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Double-Blind Blinding occurs at two levels: Double Blind (1)The subject doesn’t know whether he or she is receiving the treatment or a placebo (2)The experimenter does not know whether he or she is administering the treatment or placebo

1.1 - 83 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Simple Random Sample  Simple Random Sample of n subjects selected in such a way that every possible sample of the same size n has the same chance of being chosen

1.1 - 84 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Random Sample members from the population are selected in such a way that each individual member in the population has an equal chance of being selected Random & Probability Samples  Probability Sample selecting members from a population in such a way that each member of the population has a known (but not necessarily the same) chance of being selected

1.1 - 88 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Stratified Sampling subdivide the population into at least two different subgroups that share the same characteristics, then draw a sample from each subgroup (or stratum)

1.1 - 89 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Cluster Sampling divide the population area into sections (or clusters); randomly select some of those clusters; choose all members from selected clusters

1.1 - 90 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Multistage Sampling Collect data by using some combination of the basic sampling methods In a multistage sample design, pollsters select a sample in different stages, and each stage might use different methods of sampling

1.1 - 92 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Controlling Effects of Variables  Completely Randomized Experimental Design assign subjects to different treatment groups through a process of random selection  Randomized Block Design a block is a group of subjects that are similar, but blocks differ in ways that might affect the outcome of the experiment  Rigorously Controlled Design carefully assign subjects to different treatment groups, so that those given each treatment are similar in ways that are important to the experiment  Matched Pairs Design compare exactly two treatment groups using subjects matched in pairs that are somehow related or have similar characteristics

1.1 - 93 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Three very important considerations in the design of experiments are the following: Summary 1.Use randomization to assign subjects to different groups 2.Use replication by repeating the experiment on enough subjects so that effects of treatment or other factors can be clearly seen. 3.Control the effects of variables by using such techniques as blinding and a completely randomized experimental design

1.1 - 94 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.  Sampling error the difference between a sample result and the true population result; such an error results from chance sample fluctuations  Nonsampling error sample data incorrectly collected, recorded, or analyzed (such as by selecting a biased sample, using a defective instrument, or copying the data incorrectly) Errors No matter how well you plan and execute the sample collection process, there is likely to be some error in the results.

1.1 - 95 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Recap In this section we have looked at:  Types of studies and experiments  Controlling the effects of variables  Randomization  Types of sampling  Sampling errors

1.1 - 1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Lecture Slides Elementary Statistics Eleventh Edition and the Triola.

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1.1 - 1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Lecture Slides Elementary Statistics Eleventh Edition and the Triola.

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