Introduction to Statistics Chapter 1 Introduction to Statistics 1-1 Overview 1- 2 Types of Data 1- 3 Abuses of Statistics 1- 4 Design of Experiments

Statistics (Definition) 1-1 Overview Statistics (Definition) A collection of methods for planning experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data

Definitions Population Sample The complete collection of all data to be studied. Sample The subcollection data drawn from the population.

Example Identify the population and sample in the study A quality-control manager randomly selects 50 bottles of Coca-Cola to assess the calibration of the filing machine. Emphasize that a population is determined by the researcher, and a sample is a subcollection of that pre-determined group. For example, if I collect the ages from a section of elementary statistics students, that data would be a sample if I am interested in studying ages of all elementary statistics students. However, if I am studying only the ages of the specific section of elementary statistics, the data would be a population.

Definitions Statistics Descriptive Statistics Broken into 2 areas Inferencial Statistics

Definitions Descriptive Statistics Inferencial Statistics Describes data usually through the use of graphs, charts and pictures. Simple calculations like mean, range, mode, etc., may also be used. Inferencial Statistics Uses sample data to make inferences (draw conclusions) about an entire population Emphasize that a population is determined by the researcher, and a sample is a subcollection of that pre-determined group. For example, if I collect the ages from a section of elementary statistics students, that data would be a sample if I am interested in studying ages of all elementary statistics students. However, if I am studying only the ages of the specific section of elementary statistics, the data would be a population. Test Question

1-2 Types of Data Parameter vs. Statistic Quantitative Data vs. Qualitative Data Discrete Data vs. Continuous Data

Definitions Parameter population parameter a numerical measurement describing some characteristic of a population population parameter

Definitions Statistic sample statistic a numerical measurement describing some characteristic of a sample sample statistic

Examples Parameter Statistic 51% of the entire population of the US is Female Statistic Based on a sample from the US population is was determined that 35% consider themselves overweight.

Definitions Quantitative data Numbers representing counts or measurements Qualitative (or categorical or attribute) data Can be separated into different categories that are distinguished by some nonnumeric characteristics

Examples Quantitative data The number of FLC students with blue eyes Qualitative (or categorical or attribute) data The eye color of FLC students

Definitions We further describe quantitative data by distinguishing between discrete and continuous data Discrete Quantitative Data Continuous

Definitions Discrete Continuous data result when the number of possible values is either a finite number or a ‘countable’ number of possible values 0, 1, 2, 3, . . . Continuous (numerical) data result from infinitely many possible values that correspond to some continuous scale or interval that covers a range of values without gaps, interruptions, or jumps Understanding the difference between discrete versus continuous data will be important in Chapters 4 and 5. When measuring data that is continuous, the result will be only as precise as the measuring device being used to measure. 2 3

Examples Discrete The number of eggs that hens lay; for example, 3 eggs a day. Continuous The amounts of milk that cows produce; for example, 2.343115 gallons a day.

Definitions Univariate Data Bivariate Data Involves the use of one variable (X) Does not deal with causes and relationship Bivariate Data Involves the use of two variables (X and Y) Deals with causes and relationships Understanding the difference between discrete versus continuous data will be important in Chapters 4 and 5. When measuring data that is continuous, the result will be only as precise as the measuring device being used to measure.

Example Univariate Data Bivariate Data How many first year students attend FLC? Bivariate Data Is there a relationship between then number of females in Computer Programming and their scores in Mathematics? Understanding the difference between discrete versus continuous data will be important in Chapters 4 and 5. When measuring data that is continuous, the result will be only as precise as the measuring device being used to measure.

Important Characteristics of Data 1. 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 or how data is dispersed 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 Most important characteristics necessary to describe, explore, and compare data sets. page 34 of text

Uses of Statistics Almost all fields of study benefit from the application  of statistical methods Sociology, Genetics, Insurance, Biology, Polling, Retirement Planning, automobile fatality rates, and many more too numerous to mention. page 11 of text

1-3 Abuses of Statistics Bad Samples Small Samples Loaded Questions Misleading Graphs Pictographs Precise Numbers Distorted Percentages Partial Pictures Deliberate Distortions

Abuses of Statistics Bad Samples Inappropriate methods to collect data. BIAS (on test) Example: using phone books to sample data. Small Samples (will have example on exam) We will talk about same size later in the course. Even large samples can be bad samples. Loaded Questions Survey questions can be worked to elicit a desired response

Abuses of Statistics Bad Samples Small Samples Loaded Questions Misleading Graphs Pictographs Precise Numbers Distorted Percentages Partial Pictures Deliberate Distortions

Salaries of People with Bachelor’s Degrees and with High School Diplomas $40,500 $40,500 $40,000 $40,000 35,000 30,000 $24,400 30,000 20,000 $24,400 page 11 of text Graphs whose vertical scales do not start at 0 will give a misleading representation of the differences in heights of the bars. 25,000 10,000 20,000 Bachelor High School Degree Diploma Bachelor High School Degree Diploma (a) (test question) (b)

We should analyze the numerical information given in the graph instead of being mislead by its general shape.

Abuses of Statistics Bad Samples Small Samples Loaded Questions Misleading Graphs Pictographs Precise Numbers Distorted Percentages Partial Pictures Deliberate Distortions

Double the length, width, and height of a cube, and the volume increases by a factor of eight What is actually intended here? 2 times or 8 times? page 14 of text

Abuses of Statistics Bad Samples Small Samples Loaded Questions Misleading Graphs Pictographs Precise Numbers Distorted Percentages Partial Pictures Deliberate Distortions

Abuses of Statistics Precise Numbers There are 103,215,027 households in the US. This is actually an estimate and it would be best to say there are about 103 million households. Distorted Percentages 100% improvement doesn’t mean perfect. Deliberate Distortions Lies, Lies, all Lies

Abuses of Statistics Bad Samples Small Samples Loaded Questions Misleading Graphs Pictographs Precise Numbers Distorted Percentages Partial Pictures Deliberate Distortions

Abuses of Statistics Partial Pictures “Ninety percent of all our cars sold in this country in the last 10 years are still on the road.” Problem: What if the 90% were sold in the last 3 years?

1-4 Design of Experiments

Definition Experiment Event apply some treatment (Action) observe its effects on the subject(s) (Observe) Example: Experiment: Toss a coin Event: Observe a tail

Designing an Experiment Identify your objective Collect sample data Use a random procedure that avoids bias Analyze the data and form conclusions

Methods of Sampling Random (type discussed in this class) Systematic Convenience Stratified Cluster review of the 5 different types of sampling

Definitions Random Sample Simple Random Sample (of size n) members of the population are selected in such a way that each has an equal chance of being selected (if not then sample is biased) Simple Random Sample (of size n) subjects selected in such a way that every possible sample of size n has the same chance of being chosen

Random Sampling - selection so that each has an equal chance of being selected page 19 of text

Systematic Sampling Select some starting point and then select every K th element in the population

use results that are easy to get Convenience Sampling use results that are easy to get

subdivide the population into at 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)

Cluster Sampling - divide the population into sections (or clusters); randomly select some of those clusters; choose all members from selected clusters Students will most often confuse stratified sampling with cluster sampling. Both break the population into strata or sections. With stratified a few are selected from each strata. With cluster, choose a few of the strata and choose all the member from the chosen strata.

Definitions Sampling Error Nonsampling Error the difference between a sample result and the true population result; such an error results from chance sample fluctuations. Nonsampling Error sample data that are incorrectly collected, recorded, or analyzed (such as by selecting a biased sample, using a defective instrument, or copying the data incorrectly). page 23 of text

Using Formulas Factorial Notation Order of Operations 8! = 8x7x6x5x4x3x2x1 Order of Operations ( ) POWERS MULT. & DIV. ADD & SUBT. READ LIKE A BOOK Keep number in calculator as long a possible page 23 of text