Chapter 1
Statistics:
- science of collecting, organizing, analyzing, interpreting & drawing data conclusions in order to
make decisions
Probability:
- the chance of an event occurring
Data:
- consists of information coming from observations, counts, measurements, or survey responses
Population:
- collection of all outcomes, responses, measurements, or counts that are of interest
Sample:
- a subset, or part, of the population
Parameter:
- a numerical description of a population characteristic
Statistic:
- a numerical description of a sample characteristic
Types of statistics
Descriptive statistics:
- involves organizing, summarizing, and displaying data. ex.(tables, charts, averages)
Inferential statistics:
- involves using sample data to draw conclusions about a population
Types of data
Variable:
- characteristic or attribute that can assume different values, such as age, blood pressure, weight, or
income
Qualitative data:
- attributes, labels, or non-numerical entries
Quantitative data:
- numerical measurements or counts
Discrete data:
- when the data values are quantitive & the number of values is finite, ex. (no. of children, no. of
siblings, no. of accidents)
Continuous data:
- result from infinitely many possible quantitative values that vary and can take on any value
between values, ex. (distance, weight, cholesterol level)
, Levels of measurement
Nominal: Ordinal:
- qualitative data only - qualitative or quantitate
- names, labels, or qualities - data can be arranged in order, or ranked
- no mathematical computations - differences between data entries is not
- ex. gender, blood type meaningful
- ex. course grades
Interval:
Ratio:
- quantitative data - similar to interval
- can be ordered - zero has meaning
- differences between data entries is meaningful - a ratio of two data values can be formed
- zero only represents a position on the scale - ex. weight, height, distance
- ex. temperature, IQ, sea level
Data collection & sampling
Census:
- a count or measure of an entire population
Sampling:
- a count or measure of a part of a population & is more commonly used in statistical studies
Methods to obtain unbiased samples:
Simple random sample:
- every possible sample of the same size has the same chance of being selected
Systematic sample:
- obtained by choosing a starting value at random, the crossing every kth member of the population
Stratified sample:
- obtained by dividing the population into groups according to some characteristic that is important
to the study, then sampling from each group
Cluster sample:
- obtained by dividing the population into clusters & selecting all the members in one or more but
not all clusters
Convenience sample:
- sample involves using results that are readily available
Sample size:
too small:
- may include a disproportionate number of individuals which are outliers & anomalies, this will
skew the results & you won’t get a fair picture of the whole population
too big:
- the whole study becomes too complex, expensive, and time-consuming to run, the results are
more accurate but benefits don’t outweigh costs.
Classifications of Statistical Studies
Observational study:
- researcher simply observes the subjects without interfering
Experimental study:
- researcher manipulates the sample population
Statistics:
- science of collecting, organizing, analyzing, interpreting & drawing data conclusions in order to
make decisions
Probability:
- the chance of an event occurring
Data:
- consists of information coming from observations, counts, measurements, or survey responses
Population:
- collection of all outcomes, responses, measurements, or counts that are of interest
Sample:
- a subset, or part, of the population
Parameter:
- a numerical description of a population characteristic
Statistic:
- a numerical description of a sample characteristic
Types of statistics
Descriptive statistics:
- involves organizing, summarizing, and displaying data. ex.(tables, charts, averages)
Inferential statistics:
- involves using sample data to draw conclusions about a population
Types of data
Variable:
- characteristic or attribute that can assume different values, such as age, blood pressure, weight, or
income
Qualitative data:
- attributes, labels, or non-numerical entries
Quantitative data:
- numerical measurements or counts
Discrete data:
- when the data values are quantitive & the number of values is finite, ex. (no. of children, no. of
siblings, no. of accidents)
Continuous data:
- result from infinitely many possible quantitative values that vary and can take on any value
between values, ex. (distance, weight, cholesterol level)
, Levels of measurement
Nominal: Ordinal:
- qualitative data only - qualitative or quantitate
- names, labels, or qualities - data can be arranged in order, or ranked
- no mathematical computations - differences between data entries is not
- ex. gender, blood type meaningful
- ex. course grades
Interval:
Ratio:
- quantitative data - similar to interval
- can be ordered - zero has meaning
- differences between data entries is meaningful - a ratio of two data values can be formed
- zero only represents a position on the scale - ex. weight, height, distance
- ex. temperature, IQ, sea level
Data collection & sampling
Census:
- a count or measure of an entire population
Sampling:
- a count or measure of a part of a population & is more commonly used in statistical studies
Methods to obtain unbiased samples:
Simple random sample:
- every possible sample of the same size has the same chance of being selected
Systematic sample:
- obtained by choosing a starting value at random, the crossing every kth member of the population
Stratified sample:
- obtained by dividing the population into groups according to some characteristic that is important
to the study, then sampling from each group
Cluster sample:
- obtained by dividing the population into clusters & selecting all the members in one or more but
not all clusters
Convenience sample:
- sample involves using results that are readily available
Sample size:
too small:
- may include a disproportionate number of individuals which are outliers & anomalies, this will
skew the results & you won’t get a fair picture of the whole population
too big:
- the whole study becomes too complex, expensive, and time-consuming to run, the results are
more accurate but benefits don’t outweigh costs.
Classifications of Statistical Studies
Observational study:
- researcher simply observes the subjects without interfering
Experimental study:
- researcher manipulates the sample population
