CE 190 Chap 1

65問 • 2年前

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Collections of observations, such as measurements, genders, or survey responsis

Data

The science of planning studies and experiments; obtaining data; and organizing, summarizing, presenting, analyzing, and interpreting those data and then drawing conclusions based on them.

Statistics

The complete collection of all measurements or data that are being considered. Typically, a population is the complete collection of data that we would like to make inferences about.

Population

The collection of data from every member of a population

Census

A subcollection of members selected from a population

Sample

A researcher must determine the questions) he or she wants answered. The question(s) must be detailed so that it identifies the population that is to be studied.

Identify The Research Objective

Conducting research on an entire population is often difficult and expensive, so we typically look at a sample. This step is vital to the statistical process, because if the data are not collected correctly, the conclusions drawn are meaningless. Do not overlook the importance of appropriate data collection.Conducting research on an entire population is often difficult and expensive, so we typically look at a sample. This step is vital to the statistical process, because if the data are not collected correctly, the conclusions drawn are meaningless. Do not overlook the importance of appropriate data collection.

Collect The Data Needed

Descriptive statistics allow the researcher to obtain an overview of the data and can help determine the type of statistical methods the researcher should use.

Describe The Data

Apply the appropriate techniques to extend the results obtained from the sample to the population and report a level of reliability of the results.

Perform Interference

The final step in our statistical process involves conclusions, and we should develop an ability to distinguish between statistical significance and practical significance.

Making Conclusions

is achieved in a study if the likelihood of an event occurring by chance is 5% or less.

Statistical Significance

It is possible that some treatment or finding is effective, but common sense might suggest that the treatment of finding does not make enough of a difference to justify its use or to be practical.

Practical Significance

a numerical measurement describing some characteristic of a population

Parameter

a numerical measurement describing some characteristic of a sample

Statistic

are the characteristics of the individuals within the population.

Variables

variables allow for classification of individuals based on some attribute or characteristic.

Qualitative

variables provide numerical measures of individuals. The values of a quantitative variable can be added or subtracted and provide meaningful results.

Quantitative

is a quantitative variable that has either a finite number of possible values or a countable number of possible values. The term countable means that the values result from counting, such as 0, 1, Z, 3, and so on.

Discrete Variable

a quantitative variable that has an infinite number of possible values that are not countable.

Continuous Variable

if the values of the variable name, label, or categorize. In addition, the naming scheme does not allow for the values of the variable to be arranged in a ranked or specific order.

Nominal

if it has the properties of the nominal level of measurement, however, the naming scheme allows for the values of the variable to be arranged in a ranked or specific order.

Ordinal

if it has the properties of the ordinal level of measurement and the differences in the values of the variable have meaning. A value of zero does not mean the absence of the quantity. Arithmetic operations such as addition and subtraction can be performed on values of the variable.

Interval

if it has the properties of the interval level of measurement and the ratios of the values of the variable have meaning. A value of zero means the absence of the quantity. Arithmetic operations such as multiplication and division can be performed on the values of the variable.

Ratio

observe and measure specific characteristics without attempting to modify the individuals being studied

Observational Study

apply some treatment and then proceed to observe its effects on the individuals. (The individuals in experiments are called experimental units, and they are often called subjects when they are people.)

Experiment

is potentially a major problem with observational studies. Often, the cause of confounding is a lurking variable.

Confounding

in a study occurs when the effects of two or more explanatory variables are not separated. Therefore, any relation that may exist between an explanatory variable and the response variable may be due to some other variable or variables not accounted for in the study.

Confounding

is an explanatory variable that was not considered in a study, but that affects the value of the response variable in the study. In addition, lurking variables are typically related to explanatory variables considered in the study.

Lurking Variable

collect information about individuals at a specific point in time of over a very short period of time.collect information about individuals at a specific point in time of over a very short period of time.

Cross-Sectional Studies

retrospective studies, meaning that they require individuals to look back in time or require the researcher to look at existing records.

Case-Control Studies

first identifies a group of individuals to participate in the study (the cohort). The cohort is then observed over a long period of time

Cohort Studies

•is the process of using chance to select individuals from a population to be included in the sample. • For the results of a survey to be reliable, the characteristics of the individuals in the sample must be representative of the characteristics of the individuals in the population. The key to obtaining a sample representative of a population is to let chance or randomness play a role in dictating which individuals are in the sample, rather than convenience. If convenience is used to obtain a sample, the results of the survey are meaningless.

Random Sampling

The most basic sample survey design is simple random sampling. A sample of size n from a population of size N is obtained through simple random sampling if every possible sample of size n has an equally likely chance of occurring. The sample is then called a simple random sample.

Simple Random Sampling

is obtained by separating the population into nonoverlapping groups called strata and then obtaining a simple random sample from each stratum. The individuals within each stratum should be homogeneous (or similar) in some way.

Stratified Sample

is obtained by selecting every kth individual from the population. The first individual selected corresponds to a random number between 1 and k.

Systematic Sampling

is obtained by selecting all individuals within a randomly selected collection or group of individuals.

Cluster Sampling

methods are inappropriate, in which the individuals are not randomly selected.

Non-Random Sampling

the individuals are easily obtained and not based on randomness.

Convenience Sampling

the individuals themselves decide to participate in a survey. These are also called voluntary response samples.

Self-Selected Samples

yield unreliable results because the individuals participating in the survey are not chosen using random sampling. Instead, the interviewer or participant selects who is in the survey.

Convenience Samples

If the results of the sample are not representative of the population, then the sample has ______

Bias

• this means that the technique used to obtain the sample's individuals tends to favor one part of the population over another. Any convenience sample has sampling bias because the individuals are not chosen through a random sample. • also results due to undercoverage, which occurs when the proportion of one segment of the population is lower in a sample than it is in the population. Undercoverage can result if the frame used to obtain the sample is incomplete or not representative of the population.

Sampling Bias

exists when individuals selected to be in the sample who do not respond to the survey have different opinions from those who do. Nonresponse can occur because individuals selected for the sample do not wish to respond or the interviewer was unable to contact them

Nonresponse Bias

exists when the answers on a survey do not reflect the true feelings of the respondent. Response bias can occur in a number of ways.

Response Bias

• An experiment is a controlled study conducted to determine the effect varying one or more explanatory variables or factors has on a response variable. Any combination of the values of the factors is called a treatment. • In an experiment, the experimental unit is a person, object, or some other well-defined item upon which a treatment is applied. We often refer to the experimental unit as a subject when he or she is a person. The subject is analogous to the individual in a survey

Designed Experiment

serves as a baseline treatment that can be used to compare to other treatments.

Control Group

is a controlled study conducted to determine the effect varying one or more explanatory variables or factors has on a response variable.is a controlled study conducted to determine the effect varying one or more explanatory variables or factors has on a response variable.

Experiment

Any combination of the values of the factors is called aAny combination of the values of the factors is called a

Treatment

is a person, object, or some other well-defined item upon which a treatment is applied.

Experimental Unit

refers to nondisclosure of the treatment an experimental unit is receiving.

Blinding

the experimental unit (or subject) does not know which treatment he or she is receiving.

Single-Blind Experiment

neither the experimental unit nor the researcher in contact with the experimental unit knows which treatment the experimental unit is receiving.

Double-Blind Experiments

is one in which each experimental unit is randomly assigned to a treatment.

Completely Randomized Experiment

is an experimental design in which the experimental units are paired up. The pairs are selected so that they are related in some way (that is, the same person before and after a treatment, twins, husband and wife, same geographical location, and so on). There are only two levels of treatment in a matched-pairs design.

Matched-Pair Design

is used when the experimental units are divided into homogeneous groups called blocks. Within each block, the experimental units are randomly assigned to treatments.

Randomized Block Design

helps engineers create better products faster and cheaper. It involves testing different variables to find the most important ones, then adjusting them to improve the process. The goal is to optimize the system for the best performance.

Experimental Design

the original hypothesis that motivates the experiment.

Conjecture

the test performed to investigate the conjecture.test performed to investigate the conjecture.

Experiment

the statistical analysis of the data from the experiment.

Analysis

what has been learned about the original conjecture from the experiment. Often the experiment will lead to a revised conjecture and a new experiment, and so forth.

Conclusion

To ensure validity, experiments must randomize treatments (including control). This avoids biases in the outcomes. Experimental units are part of the experiment. Random assignment is crucial.

Randomization

is essential for statistics. It helps us measure and control uncertainty in our estimates. We can reduce uncertainty by increasing n or blocking to lower the error variance.

Replication

reduces unwanted variation in experiments by controlling for nuisance factors, such as gender or age in human studies. This helps avoid biases and noise that may affect the treatment effect. Blocking factors are not the main interest of the experiment, but they need to be included to minimize error variance.

Blocking

are more efficient than one factor at a time experiments. They can reveal the effects of primary, blocking and interacting factors on the response.

Multi-Factor Design

is the deliberate mixing of effects that are not of interest with those that are. It can help reduce the cost and complexity of experiments by allowing us to focus on the main effects and ignore the interactions. This technique is useful for multiple factor experiments.

Confounding

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Lance Margaux Sampayan · 50問 · 2年前

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