Statistics Assignment: Descriptive, Inferential and Variables

This statistics assignment delves into key statistical concepts, including descriptive and inferential statistics. It explains the difference between population and sample, and how a sample is used to make inferences about the population. The assignment explores the types of variables, distinguishing between continuous and categorical variables, with examples of each. It then examines independent and dependent variables within a given scenario. The assignment also briefly discusses the importance of frequency distribution, and how it is used to display the values of a single variable. This is a great resource for students looking to understand the fundamentals of statistical analysis.

STATISTICS
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STATISTICS
 Population: It can be described through a complete set of similar kind of items that exist
or/and considered for statistical study. Further, it comprises of a large number of
individuals that have something in common. Example would include citizens of the
Australia or total number of members of Machinists Union of a country Sample:
Population data are generally very large and quite difficult to analyse and thus, a definite
number of samples are drawn from the given population so as to determine or predict the
various aspects/parameter regarding the population. In simple word, an extracted part of
population that is analysed to make decisions about population is termed as sample.
Simple random sample is most widely used sampling technique to collect sample from
population. Descriptive statistics: It is a technique to organise and summarise the given
set of information for analysis purpose. Further, it is performed so as to describe the main
basic features of the collected data (sample) in a more manageable way. The main aim of
descriptive statistics is to represent large data in quantitative terms. Inferential statistics:
Various conclusions are drawn about the population based on the provided sample data in
inferential statistics. It includes techniques such as hypothesis testing, confidence interval
determining.
 Example of continuous variables and categorical variables is highlighted below:
Continuous variable- Height, Temperature, Weight
The above variables are continuous since the values of the above variables can assume
decimal values and not just restricted to integral values. For instance temperature can be
100.2 F, weight can be 78.2 lbs and height can be 156.35 cm. If decimal values were not
possible, then these variables would be dicrete.
Categorical variables- Colour, Religion, Gender
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STATISTICS
The above variables are categorical since their values are non-numerical and essentially are in
the form of label. For instance colour can be black or white. Similarly, gender can be male or
female.
 The independent and dependent variable of given scenario is highlighted below:
Independent variable –The treatments offered
Dependent variable – Measures of depression \
The dependent variable is measures of depression since this would depend on the two
different treatments that would be given. Here, the researcher would modify the treatment so
to determine the outcome i.e. measure of depression. Thus, the treatment would be the
independent variable influencing the outcome which the researchers would measure.
 Simple frequency distribution: The more appropriate way to show a single variable is
frequency distribution. Based on the type of variables the frequency distribution has been
made of the group values after dividing the variables int different categories. For
example, age, price, country and so forth. Generally, frequency distribution is depicted in
two main ways i.e. tabular form and graphical form.
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