Homework SourseDescribe in your own word by designing an experiment that us
Solution
Soltion:
Qualitative and Quantitative Variables
An important distinction between variables is between qualitative variables andquantitative variables. Qualitative variables are those that express a qualitative attribute such as hair color, eye color, religion, favorite movie, gender, and so on. The values of a qualitative variable do not imply a numerical ordering. Values of the variable “religion” differ qualitatively; no ordering of religions is implied. Qualitative variables are sometimes referred to as categorical variables.Quantitative variables are those variables that are measured in terms of numbers. Some examples of quantitative variables are height, weight, and shoe size.
Example #1: Can blueberries slow down aging? A study indicates that antioxidants found in blueberries may slow down the process of aging. In this study, 19-month-old rats (equivalent to 60-year-old humans) were fed either their standard diet or a diet supplemented by either blueberry, strawberry, or spinach powder. After eight weeks, the rats were given memory and motor skills tests. Although all supplemented rats showed improvement, those supplemented with blueberry powder showed the most notable improvement.
In the study on the effect of diet discussed above, the independent variable was type of supplement: none, strawberry, blueberry, and spinach. The variable \"type of supplement\" is a qualitative variable; there is nothing quantitative about it. In contrast, the dependent variable \"memory test\" is a quantitative variable since memory performance was measured on a quantitative scale (number correct).
Quantitative data are measures of values or counts and are expressed as numbers.
Quantitative data are data about numeric variables (e.g. how many; how much; or how often).
Qualitative data are measures of \'types\' and may be represented by a name, symbol, or a number code.
Qualitative data are data about categorical variables (e.g. what type).
Descriptive (summary) statistics:
Statistics that describe or summarise can be produced for quantitative data and to a lesser extent for qualitative data.
As quantitative data are always numeric they can be ordered, added together, and the frequency of an observation can be counted. Therefore, all descriptive statistics can be calculated using quantitative data.
As qualitative data represent individual (mutually exclusive) categories, the descriptive statistics that can be calculated are limited, as many of these techniques require numeric values which can be logically ordered from lowest to highest and which express a count.
Mode can be calculated, as it it the most frequency observed value. Median, measures of shape, measures of spread such as the range and interquartile range require an ordered data set with a logical low-end value and high-end value. Variance and standard deviation require the mean to be calculated, which is not appropriate for categorical variables as they have no numerical value.
Inferential statistics:
By making inferences about quantitative data from a sample, estimates or projections for the total population can be produced.
Quantitative data can be used to inform broader understandings of a population, or to consider how that population may change or progress into the future.
Qualitative data are not compatible with inferential statistics as all techniques are based on numeric values.
Frequency counts:
The number of times an observation occurs (frequency) for a data item (variable) can be shown for both quantitative and qualitative data.
