# Statistics Introduction Chapter 1

## Terms

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- What is descriptive statistics?
- The use of graphs, charts and tables and the calculation of various statistical measures to organize and summarize information.
- Population
- The complete collection of individuals, items, or data under consideration in a statistical study.
- Sample
- The protion of the populaiton selected for analyais.
- Inferential Statistics
- Consists of techniques for reaching conclusions about a populaiton based upon information contained in a sample.
- Variable
- A characteristic of interest concerning the individual elements of a population or of a sample.
- Observation
- A particular element from the sample or population.
- Data Set
- The observations of a variable for the elements of the sample.
- Quanititative Variable
- when the decription of the variable results in a numerical number. ( It would be necessary to perform a count)
- Discrete Variable
- A quantitative variable whose values are countable. They usually result "from counting".
- Continuous Variable
- is a quanitifyable variable that can assume any numerical value over an interval or over several intervals. It ususally results from making a measurmennt of some type.
- Qualitative
- determined when the description of the charasteristic of interest results in a nonnumerical value. Maybe classified into two or more categories.
- Nominal scale
- data that areused for category identification. Type of measurement- characterised by names, labels or categories.* can not be used in an ordering scheme. Arithmetic operations are not performed for nominal data.
- Ordinal scale
- data that can be set in some type of order.
- What are the key features of a "Oridinal Level of Measurement"?
- 1. data that applies to categories that can be ranked. 2. data can be arranged in an ordering scheme.
- What are the key fetures of the "Nominal Scale"?
- 1. Differences between the data values can not be determined and are meaningless.
- interval scale
- data that can be arranged in some order and for which differences in data values are meaningful.
- What is the key feature of " interval level of measurment"?
- 1. results from counting or measuring.
- What is the key feature of interval scale data?
- the data can be arranged in an ordering scheme and the differences can be calculated and interpreted.
- Expalin what how the value of zero is used when using interval data.
- It is arbitrarily choosen and does not imply an absence of the characteristic being measured.
- Are ratio's meaningingful for interval data?
- No.
- What is an example of "interva data?"
- 1)The Standfor-Binet IQ Scores. IQ scores can be arrange in order. Differences can be calculated and interpreted.An IQ score of zero does not indicate a lack of intelligence.
- Name another example of "interval level data".
- Temperatures. Temperatures can be arranged in order.Differences maybe calculated and interpreted.Ratio's are not readily interpretable. A temp of 0 does not indicate a absence of warmth.
- Is a test score an example of " interval level data?"
- Yes. Test scores can be arranged in order.Differences maybe calculated and interpreted.Ratio's are not readily interpretable. A test of 0 does not indicate a absence of knowledge.
- What is a "ratio scale"?
- Applied to data that can be ranked, and all arithmeitc operationsincluding division can be performed.
- Is division by "0" excluded in a "ratio scale?"
- Yes
- What are the key features of "ratio scale data?"
- 1. Can be arranged in an ordering scheme. 2.Differences and rations can be calculated and interpreted. 3. Ratio level data has an absolute zero, a value of zero indicates a complete absence of the characteristic of interest..
- What doe "Σx" mean?
- Sigma. The sum of all scores of x
- What is "raw data"?
- information obtained by observing values of a variable.
- What are the main features of a frequency distribtuion for qualitative data.
- 1. lists all categories and the number of elements that belong to each of the categories.
- What are the steps to finding " relative frequency"?
- divide the frequency of a category by the frquency of all categories.
- What will the sum of all the relative frequencies be equal to?
- One
- How do you find the percentage for a category?
- Multiply the relative frequency for the category by 100. ( The sum of the percentage for all categories will always equal 100%.
- Where are the categories placed on a bar graph?
- On the horizontal axis.
- Where are the frquencies marked on a bar graph?
- on the vertical axis.
- What type of data is a pie chart used to graph?
- Qualitative data.
- What is a historgram?
- It is a graph that displays the classes on the horizontal axis and the frequencies of the classes on the verticle axis.
- How is a histogram different than a bar graph?
- A histogram utilizes classes or intervals while a bar graph utilizes categories and frequencies.
- What is the "cumulitive realtive frequency"?
- It gives the total number of values that fall below variuos class boundaries of a frequency distribution.
- How is a culmulative relative frequency obtained?
- It is obtained by dividing a cumulative frequency by the total nubmer of observations in the data set.
- How is the Cumulative Percentage obtained?
- 1. By multiplying cumulative relative frequencies by 100.
- What are the most common measures of centeral tendency?
- mean, median & mode
- What is the difference between raw (ungrouped data) and grouped data?
- Data that is presented in the form of frequency distribution is grouped, ungrouped data is just a list of data
- How do you figure out the mean?
- Devide the total scores by the number of scores observed.