STATISTICSDr. Omar Al Jadaan

Assistant Professor – Computer Science & Mathematics

QUALITATIVE DATA

Nominal data: are data that one can name and put to categories.

They are not measured but simply counted They are unordered Binary: yes/no, male/female, cured/not cured,

pregnant /not pregnant. Can have more than categories: blood group,

country origin, ethnic, eye color, marital status,

QUALITATIVE DATA

Ordinal data: if there are more than two categories of classification it may be possible to order them in some way.

After treatment a patient may be either improved, same or worse

Woman may never have conceived, conceived but spontaneously aborted, or given birth to a live infant.

Education can be none, elementary school, middle school, high school, college and above.

Rank where codes of the best, second best, …, worst.

QUANTITATIVE DATA(NUMERICAL)

Count data: are often count per unit (integer number)( time, month, attacks, person,….)

In dentistry we have decayed, filled, missing teeth (DFM).

QUANTITATIVE DATA(NUMERICAL)

Measured or continuous data: take any value in a given range.

Age, body mass index, years of mestruation. Divide a continuous variable into more than

two groups to ease the grouping of population.

INTERVAL AND RATIO SCALES

In interval scale (body temperature, calendar dates) the difference between two measurements has a meaning, but the ration does not.

The zero value has no meaning. In ratio scale ( body weight, 10% increase

implies the same weight increased whether expressed in Kg. or Pound.

The zero value has a meaning in ratio scale

HOW STATISTICIAN CAN HELP?

Investigator should seek the advice of statistician at the early stage of an investigation.

Where the medical statistician can help? Sample size and power considerations

Sample size ( finance , time, patients) Questionnaires Choice of sample and control subjects Design of study Laboratory experiments Displaying data Choosing of summary statistics and statistical

analysis

TERMINOLOGY AND SYMBOLS USED IN STATISTICS

Data set: collection of different values of all variables used to measure the characteristics of the sample or population.

Example Data set ={ age 1 = 60 years, 2= 74 years, 3 =

85 years, gender 1= male , 2 = female, 3= male, ….}

Variable: any characteristic that can be expressed with more than one value.

Example : Gender ( male, female)

Dependent variable: the variable that measure the effect of some other variables. Example Cancer

Independent variable: a variable that is expected to cause/ influence the value of another variable Example smoking

Extraneous variable : A variable that confound the relationship between the dependent variable and independent variable.

Example occupation, age, gender, blood group,…. Etc.

Dichotomous variable: a nominal variable having only two categories.

Example: yes/no, know/I do not know.

Continuous variable: a variable that can take on any value within a range.

Example: weight, height,….

Discrete variable: a variable that can take on any certain value.

Example: No. of children, blood pressure,

Hypothesis: a formal statement of the expected relationship between two or more of the variables.

Example there is a relationship between cancer and smoking.

Null hypothesis: the hypothesis that state two or more variables being compared will not be related to each other. Example: no significant relationship between

variables will be found.

Alternative hypothesis (Ha): the hypothesis that states a statistically significant relationship exist between the variables. It is opposite to the null hypothesis .

Negative relationship (inverse): as the value of one variable increase, the value of the other value will decrease.

Positive relationship (direct): as the value of one variable increase/decrease, the value of the another variable will increase/decrease.

Causal relationship: a relationship in which one or more variables is presumed to cause the change in another variable.

Population All the residents of UAE.

Target population: the entire group having some characteristics. Example all people with depression living in UAE.

Sample: a group selected from the population, in the hope that the smaller group will be representative of the entire population.

Randomization let each individual in the population have the equal chance/opportunity to be selected for the sample.

The purpose of the randomization is to ensure that the sample is representative of target population.

Experiment : a research study with the following characteristic:

the investigator provide an intervention to the selected participants into the study randomly,

random assignment of participants is called cases group and the other groups not received any intervention are called control group.

Generalization: the extend to which the research findings can be applied to situations beyond the immediate group that was studied.

Empirical studies: study based on observation or experience.

Measurement: the assignment of number to object or events.

Mean (µ): a measure of central tendency. It is the arithmetic average value of a set of dataExample : {2,3,4,2,5,6,7,4,3}

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Mean

Median: a measure of central tendency. It is the central point or middle value of an ordered set of data.

Example {2,3,2,4,7,8,5,6,7}

Answer : 1- order the data set,{2,2,3,4,5,6,7,7,8}Then median = 5

Example {2,3,2,4,7,8,5,6,7,11}Answer 1- order the data set,{2,2,3,4,5,6,7,7,8,11}

Then mean =5.5

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