What is the science of collecting analyzing presenting and interpreting data?

Presentation on theme: "What is Statistics? Statistics The science of collecting, organizing, analyzing, and interpreting data in order to make decisions. 1 Data Information coming."— Presentation transcript:

1 What is Statistics? Statistics The science of collecting, organizing, analyzing, and interpreting data in order to make decisions. 1 Data Information coming from observations, counts, measurements, or responses. Why should you care about statistics? Statistics helps you make informed decisions that affect your life. Statistics helps the government make decisions that affect many people. Medical & LifeStyle Decisions Vaccines: Polio, Measles, Flu, HPV Meds: Blood Pressure, Cholesterol Hormone Replacement, Chemo Smoking Home in City/Country/Suburb College/Major Invest in Stock Market Marriage/Divorce/Children/Adopt Government Decisions Raise Retirement Age (Soc. Sec.) Drinking/Driving/Seatbelt Laws Mandatory School for children Common Statistical Data  Census  Health/Medical  Crime  Scientific  Education  Economic

2 Statements Based on Data Collection “People who eat three daily servings of whole grains have been shown to reduce their risk of…stroke by 37%.” (Source: Whole Grains Council) “Seventy percent of the 1500 U.S. spinal cord injuries to minors result from vehicle accidents, and 68 percent were not wearing a seatbelt.” (Source: UPI) In 2007, Florida’s High School graduation rate was: 65% compared with that of the U.S. at 74%. In 2006 the Florida graduation rate was 73%. (Data Source: U.S. Department of Education Website) What makes a statement based on data correct or incorrect? Statements may be correct based on the data Statements may be incorrect for the data due to a calculation/analysis error Statements may be correct for the data, but may be incorrect because the data collection was not done properly. (Difficult for average person to know) Controversy – Interpretation – Biased Data Can you think of some controversial statistical statements? Do TV advertisements use statistical statements? Do politicians use statistical statements?

3 Data Sets Population The collection of all outcomes, responses, measurements, or counts that are of interest. Sample A subset of the population. 3 Larson/Farber 4th ed. 1.Collection of all black men over 40 in the U.S. 2. Collection of all computer users 3. Major of each college student at VCC 1.Collection of 10,000 black men over 40 who participated in a study on blood pressure. 2. Collection of 567 computer users surveyed. 3. Major of college students at VCC who take statistics. “ALL”“SOME” Some Statistical Data WebSites: http://www.usa.gov/Topics/Reference_Shelf/Data.shtml http://www.cdc.gov/DataStatistics/ http://www.cdc.gov/D Note that #3 is a sample but, it is NOT representative of the Population given. Why? How Can this be fixed?

4 Example: Identifying Data Sets In a recent survey, 1708 adults in the United States were asked if they think global warming is a problem that requires immediate government action. Nine hundred thirty-nine of the adults said yes. Identify the population and the sample. Describe the data set. (Adapted from: Pew Research Center) 4 The population consists of the responses of all adults in the U.S. The sample consists of the responses of the 1708 adults in the U.S. in the survey. The sample is a subset of the responses of all adults in the U.S. The data set consists of 939 yes’s and 769 no’s.

5 Parameter and Statistic Parameter A number that describes a population characteristic. Average age of all people in the United States Statistic A number that describes a sample characteristic. Average age of people from a sample of three states 5 Parameter or Statistic? 1.A recent survey of a sample of MBAs reported that the average salary for an MBA is more than $82,000. (Source: The Wall Street Journal) Sample statistic (average of $82,000 is based on a subset of the population) 2. Starting salaries for the 667 MBA graduates from the University of Chicago Graduate School of Business increased 8.5% from the previous year. Population parameter (the percent increase of 8.5% is based on all 667 graduates’ starting salaries)

6 Branches of Statistics Descriptive Statistics Involves organizing, summarizing, and displaying data. e.g. Tables, charts, averages Inferential Statistics Involves using sample data to draw conclusions about a population. 6

7 Example: Descriptive and Inferential Statistics 1. Which part of the study represents the descriptive branch of statistics? 2. What conclusions might be drawn from the study using inferential statistics? A large sample of men, aged 48, was studied for 18 years. For unmarried men, approximately 70% were alive at age 65. For married men, 90% were alive at age 65. (Source: The Journal of Family Issues) 7 Descriptive Statistics For unmarried men, approximately 70% were alive at age 65. For married men, 90% were alive at age 65. The chart A possible conclusion Being married is associated with a longer life for men.

8 1.2 Types of Data Qualitative Data Consists of attributes, labels, or nonnumerical entries. Major Place of birth Eye color 8 Quantitative data Numerical measurements or counts. Age Weight of a letter Temperature Example: The base prices of several vehicles are shown in the table. Which data are qualitative data and which are quantitative data? (Source Ford Motor Company) Quantitative Data (Base prices of vehicles models are numerical entries) Qualitative Data (Names of vehicle models are non-numerical entries)

9 Levels of Measurement Nominal level of measurement Qualitative data only Categorized w/names, labels, or qualities No mathematical computations can be made Ordinal level of measurement Qualitative or quantitative data Data can be arranged in order Differences between data entries are not meaningful 9 (Source:Nielsen Media Research) Lists the rank of five TV programs. Data can be ordered but no meaning for Difference between ranks. Lists the call letters (names) of each network affiliate. Interval level of measurement Quantitative data only Data can be ordered Meaningful difference between data entries. Zero represents a position on a scale (not an inherent zero – zero does not imply “none”) Quantitative data. Can find a difference between two dates, but a ratio does not make sense. (Source: Major League Baseball) Ratio level of measurement Similar to interval WITH zero entry as an inherent zero (implies “none”) A ratio of 2 data values can be formed One data value can meaningfully be expressed as a multiple of another. Can find differences. Can write ratios. “ Twice as much” makes sense

10 Summary of Four Levels of Measurement Level of Measurement Put data in categories Arrange data in order Subtract data values Determine if one data value is a multiple of another NominalYesNo OrdinalYes No IntervalYes No RatioYes 10 There is a good summary of the levels with examples on P. 14 of your book.

11 Section 1.3 Experimental Design Topics Data Collection Techniques  observation  Experiment  Simulation  survey Designing a statistical study (*) Designing an experiment (*) 11 Goal: Collect data and use the data to make decisions Sampling Techniques  Random  Stratified  Cluster  Systematic  Biased (*) In a study the researcher does not influence the responses In an experiment, the researcher applies a ‘treatment’, then observes the responses. You may never need to develop a statistical study, BUT… You will likely need to interpret the results of one, AND… You WILL need to determine if the results are valid!

12 Designing a Statistical Study 3.Collect the data. 4.Describe the data using descriptive statistics techniques. 5.Interpret the data and make decisions about the population using inferential statistics. 6.Identify any possible errors. 1.Identify the variable(s) of interest (the focus) and the population of the study. 2.Develop a detailed plan for collecting data. If you use a sample, make sure the sample is representative of the population. 12

13 Data Collection Observational study A researcher observes and measures characteristics of interest of part of a population but does not change existing conditions. Example: Researchers observed and recorded the mouthing behavior on nonfood objects of children up to three years old. Source: Pediatric Magazine) 13 Experiment A treatment is applied to part of a population & responses are observed. Another part of the population may be used as ‘control’ group w/ a ‘placebo’ ( no treatment ) Example: A group of diabetics took cinnamon extract daily while a control group took none. After 40 days, diabetics who had cinnamon reduced their risk of heart disease while the control group experienced no change.(Source: Diabetes Care) Simulation Uses a math or physical model to reproduce conditions of a situation or process. - often involves the use of computers. (reproduction dangerous or impractical) Example: Automobile manufacturers use simulations with dummies to study the effects of crashes on humans. Survey An investigation of one or more characteristics of a population, commonly done by interview, mail or telephone. (Survey questions must be without bias) Example: A survey is conducted on a sample of female physicians to determine whether the primary reason for their career choice is financial stability.

14 Example: Methods of Data Collection Which method of data collection would you use for each study? 1.A study of the effect of changing flight patterns on the number of airplane accidents. Solution: Simulation (Impractical to create this situation) 14 2.A study of the effect of eating oatmeal on lowering blood pressure. Solution: Experiment (Measure the effect of a treatment – eating oatmeal) 3.A study of how fourth grade students solve a puzzle. Solution: Observational study (observe and measure certain characteristics of part of a population) 4.A study of U.S. residents’ approval rating of the U.S. president. Solution: Survey (Ask “Do you approve of the way the president is handling his job?”)

15 Key Elements of Experimental Design ControlRandomizationReplication 15 Control for effects other than the one being measured. 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. 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. (Researchers prefer this)

16 Key Elements of Experimental Design Randomization - randomly assigning subjects to different treatment groups. Completely randomized design  Subjects are assigned to different treatment groups through random selection. Randomized block design  Divide subjects with similar characteristics into blocks, and then within each block, randomly assign subjects to treatment groups. Example: An experimenter testing the effects of a new weight loss drink may first divide the subjects into age categories. Then within each age group, randomly assign subjects to either the treatment group or control group. 16 ControlRandomizationReplication Matched Pairs Design  Subjects are paired up according to a similarity. One subject in the pair is randomly selected to receive one treatment while the other subject receives a different treatment.

17 Key Elements of Experimental Design Replication is the repetition of an experiment using a large group of subjects. Example: To test a vaccine against a strain of influenza, 10,000 people are given the vaccine and another 10,000 people are given a placebo. Because of the sample size, the effectiveness of the vaccine would most likely be observed. 17 ControlRandomizationReplication Another Key Element: Sample Size The number of subjects in a study is very important and will be discussed at Various times throughout the course.

18 Example: Experimental Design A company wants to test the effectiveness of a new gum developed to help people quit smoking. Identify a potential problem with the given experimental design and suggest a way to improve it. The company identifies one thousand adults who are heavy smokers. The subjects are divided into blocks according to gender. After two months, the female group has a significant number of subjects who have quit smoking. 18 Problem: The groups are not similar. The new gum may have a greater effect on women than men, or vice versa. Correction: The subjects can be divided into blocks according to gender, but then within each block, they must be randomly assigned to be in the treatment group or the control group.

19 Sampling Techniques Simple Random Sample Every possible sample of the same size has the same chance of being selected. xx x x x x x x x x x x x x x x x xx x x x x x xx x x x x x x x x x x x x x x xx x x x x x xx x x x x x x x x x x x x x x xx x x x x x xx x x x x x xx x x x x x x xx x x x x x xx x x x x x x xx x x x x x x x 19 Random numbers can be generated by a random number table, a software program or a calculator. Assign a number to each member of the population. Members of the population that correspond to these numbers become members of the sample.

20 Simple Random Sample Step3: Read the digits in groups of three Step4: Ignore numbers greater than 731 The students assigned numbers 719, 662, 650, 4, 53, 589, 403, and 129 would make up the sample. 20 Example: There are 731 students currently enrolled in statistics at your school. You wish to form a sample of eight students to answer some survey questions. Select the students who will belong to the simple random sample. Step1: Assign numbers 1 to 731 to each student taking statistics. Step2: On the table of random numbers, choose a starting place at random (suppose you start in the third row, second column.)

21 Other Sampling Techniques Divide a population into groups (strata) and select a random sample from each group. To collect a stratified sample of the number of people who live in West Ridge County households, you could divide the households into socioeconomic levels and then randomly select households from each level. 21 Larson/Farber Stratified Cluster Systematic Divide the population into groups (clusters) and select all of the members in one or more, but not all, of the clusters. In the West Ridge County example you could divide the households into clusters according to zip codes, then select all the households in one or more, but not all, zip codes. Choose a starting value at random. Then choose every k th member of the population. In the West Ridge County example you could assign a different number to each household, randomly choose a starting number, then select every 100 th household.

22 Example: Identifying Sampling Techniques You are doing a study to determine the opinion of students at your school regarding stem cell research. Identify the sampling technique used. 1.You divide the student population with respect to majors and randomly select and question some students in each major. Solution: Stratified sampling (the students are divided into strata (majors) and a sample is selected from each major) 22 2.You assign each student a number and generate random numbers. You then question each student whose number is randomly selected. Solution: Simple random sample (each sample of the same size has an equal chance of being selected and each student has an equal chance of being selected.)

What is the science of collecting organizing presenting analyzing and interpreting data?

What is the science of collecting and analyzing?

What is the science of collecting and analysing numerical data?