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**Chapter 1 Getting Started**

What is Statistics?

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**Individuals vs. Variables**

People or objects included in the study Characteristic of the individual to be measured or observed

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**Quantitative vs. Qualitative**

Quantitative Variables Qualitative Variables Have value or numerical measurement for which operations such as addition or averaging make sense Describes an individual by placing the individual into a category or group, such as male or female

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**Population vs. Sample Population Data Sample Data**

Data is from every individual of interest Population Parameters are numerical measures that describe an aspect of a population The data are from only some of the individuals of interest Sample Statistics are numerical measures that describe an aspect of a sample

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**Levels of Measurement Nominal – Names, Labels, Categories**

Ordinal – Arranged in meaningful mathematical order Interval – Differences are meaningful Ratio – Division or percentage comparisons make sense; zero point

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**Chapter 1 Getting Started**

1.2 Random Samples

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**Simple Random Sample (SRS)**

A simple random sample of n measurements from a population is a subset of the population selected in such a manner that every sample of size n from the population has an equal chance of being selected.

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**Random Number Tables (RNT)**

Used to help secure a SRS Steps: Number all members of the population sequentially. Drop a pin on the RNT to pick a starting point Pull digits n at a time, discarding non-used numbers Repetition?

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Do Now With a partner, discuss how a Random Number Table or Random Number Generator could be used to generate the answer key for a multiple choice test (assume 10 questions on quiz and 5 choices per question). Rephrased: How can a RNT or RNG be used to determine next to which letter the correct answer to each question should be placed?

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**Other Methods to Secure a Sample**

Systematic Stratified Cluster Multistage Convenience

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**Systematic Sampling Population is numbered**

Select a starting point at random and pick every kth member

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Convenience Sampling Create sample by selecting population members which are easily available

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Stratified Sampling Divide population into distinct subgroups based on specific characteristics Draw random samples from each strata

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Cluster Sampling Divide population into pre-existing segments or clusters (often geographic). Make a random selection of clusters. All members of cluster are chosen.

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Multistage Sampling Use a variety of sampling methods to create successively smaller groups at each stage. Final sample is made of clusters.

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Do Now Copy the Blue Box from page 21 into your notebooks. This is the beginning of Section 1.3 “Introduction to Experimental Design”

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Census vs. Sample Census – measurements from observations from the entire population are used. Sample – measurements from observations from part of the population are used

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**Observational Study vs. Experiment**

Observational Study – observations and measurements of individuals are conducted in a way that doesn’t change the response or the variable being measured Experiment – a treatment is deliberately imposed on the individuals in order to observe a possible change in the response or variable being measured

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Within Experiments: Placebo Effect – occurs when a subject receives no treatment but (incorrectly) believes he or she is in fact receiving treatment and responds favorably Control Group – those who receive the placebo treatment Treatment Group – those who receive the actual treatment Completely Randomized Experiment – one in which a random process is used to assign each individual to one of the treatments

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**Completely Randomized Experiment**

C.R.E. – is one in which a random process is used to assign each individual to one of the treatments

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**Characteristics of a Well-Designed Experiment**

Block – a group of individuals sharing some common features that might affect the treatment Randomized Block Experiment – individuals are first sorted into blocks, and then a random process is used to assign each individual in the block to one of the treatments

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**Characteristics of a Well-Designed Experiment**

Control Groups – used to account for the influence of other known or unknown variables that might be an underlying cause of change in response in the experimental group. Lurking or Confounding Variables – such variables

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**Characteristics of a Well-Designed Experiment**

Randomization – used to assign individuals to the two treatment groups. Helps to prevent bias in selecting members to the groups Replication – on many patients reduces the possibility that the differences in occurred by chance alone.

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**Potential Pitfalls of Surveys**

Nonresponse Truthfulness of Response Faulty Recall Hidden Bias Vague Wording Interview Influence Voluntary Response

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**Data Collection Techniques (Summary)**

Census Samples Experiments Observational Studies Surveys Simulations (previously)

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