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STATS - Ch. 1 Review

Nightingale Intro to Statistics, Week 1 Summary of Sampling&Data Formulae

Statistics is the science of collecting, organizing, and interpreting data.
Statistics are the data (numbers or other pieces of information) that describe or summarize something.
Descriptive Statistics presents summarized data
Inferential Statistics attempts to describe a population after viewing only a small portion
The population in a statistical study is the compete set of people or things being studied.
A population parameter summarizes the value of a specific variable for a population.
A sample is a subset of the population from which data are actually obtained.
A sample statistic summarizes the value of a specific variable for sample data.
A statistical study suffers from bias if its design or conduct tends to favor certain results.
A representative sample is a sample in which the relevant characteristics of the sample members are generally the same as the characteristics of the population.
A census is the collection of data from every member of a population. (not a sample)
A random sampling occurs if a sample is drawn in such a way that each time an item is selected, each item has an equal chance of being drawn.
When a sample is obtained by drawing every nth item on a list or production line, the sample is a systematic sample.
A cluster sample is sometimes referred to as an area sample because it is frequently applied on a geographical basis, or location.
Stratified sampling involves dividing the population by characteristics called stratifying factors such as gender, race, religion, or income.
Convenience sampling uses data that are easily or readily obtained, and can be extremely biased, and so not a representative sample.
Qualitative data is grouped into a category or group. Sums, products or other numerical calculations do not mean anything.
Quantitative Data has a value or a numerical measurement for which you can calculate sums, products and other numerical calculations.
Nominal: Data is put in categories (names), i.e. Color like: blue, red, yellow
Binary: Specific kind of Nominal, values are True/False, 1/0, Yes/No, or other similar values.
Ordinal: Nominal plus the data is put in ordered categories (ranks), i.e. Sizes like: small, medium, large
Discrete data can take on only particular, distinct values and not values in between (can be interval or ratio)
Continuous data can take on any value in a given interval (can be interval or ratio)
Interval: Ordinal plus the interval is meaningful, but ratios are not (the zero location is arbitrary), i.e. temperature in Fahrenheit or Celsius, Shoe size, women’s clothing size
Ratio: Interval plus the data have an absolute zero point (ratios are meaningful), i.e. height, length, etc.
Frequency Distribution is a chart which includes how many (frequency) of each piece of data (category)
Category (column), can be class, bin, range, etc.
Frequency (column) is how often the data occurs.
Tally (optional) sometimes used to make it easier to sort the data.
Total (row) is the total of each column.
Cumulative Frequency (column) is the sum of the data to that row, so the last data row should have the same value as the Total row.The cumulative frequency for row 1 is the frequency of row 1.
The cumulative frequency for row 2 is the frequency of row 1 + row 2. The cumulative frequency for row 3 is the Cumulative Frequency from row 2 + row 3. The cumulative frequency for row n is the Cumulative Frequency from row n-1 + row n.
Relative Frequency (column) of a row is the percent of that row compared to all the data. frequency for now/total frequency
Cumulative Relative Frequency (column), or Relative Cumulative Frequency you can either sum the Relative Column, or find the Relative Frequency of the Cumulative Column (both are the same)
Created by: alishalynne93
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