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Differentiate between one tiled and two tiled hypotheses
Hypothesis examination often helps in determining if an assertion is false or true.
Two tiled hypothesis testing allocates semi alpha to determining the statistical impertinences in a
given direction and half of alpha to determining statistical importance to another direction. It
insinuates that 0.025 will always be used to make a determination of the statistics (Benerjee,
2010). When applying the two-tiled test, not considering the direction of the union being
hypothesized, two tiled tests, examines the union in equal directions.
hypnosis test; it allocates all of the alpha to determine statistical importance in a given single
route of interest. One tiled test is used to determine the likeliness of a union in a single direction
and totally ignoring the likeliness of a union in the other direction.
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Differentiate between casual and non-causal relationship
Causal relationship; has a single variable influencing directly the other variables.
A causal relationship can only provide an assertion of how information was acquired. The
information values themselves have information that may aid in decision making (Benerjee,
2010). Double variables that casually relate will insinuate that any changes in either of the
variables will have a direct impact on the other variable.
Non-causal relationship; there is no single variable that influences the others, two
variables may be connected to one another without either of the variables directly manipulating
the standards of the other. If there are two variables that have a non-casual relationship a change
in either of the variables does not directly insinuate change in the other variable.
What are the independent, dependent, confounding/ extraneous variables, and population?
Independent variables; are variables that are capable to operate or stand single-
handedly and are not manipulated by a researcher’s actions. Independent variables always have
an impact on the dependent variable. Race is an independent variable.
Dependent variables are variables that are influenced and change with the
alteration of the independent variable. It insinuates that dependent variables measure the degree
of effectiveness of an independent variable (Benerjee, 2010). Income is a dependent variable.
Confounding variable; it is an outside element that implicates the independent and
dependent variable. The outside influence is used to manipulate the results of a study. It
insinuates that if confounding variables are not well handled then the outcome of a study could
be jeopardized. Date of birth is a confounding variable.
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Population; it is the whole pool from which a statistical sample is taken. It may
insinuate a whole group or a sample of people, objects, or events under study. Faculty members
of SUNO provides the population.
What are the predictor and criterion variables?
Predictor variables; they are variables that are used to synthesizes variations in
response. Predictor variables are also called input variables or independent variables. Race is a
Criterion variable; it is the variable that is being forecasted. The criterion variable
is also called a dependent variable. Income is a criterion variable.
Differentiate between univariate, bivariate, and multivariate analysis
Univariate analysis; it is the easiest method of information analysis; data
understudy has a single variable. Since the information under study is a single variable it
therefore, does not handle unions or causes (Benerjee, 2010). Its primary function is to define the
information and get designs that exist within it. Univariate data can be presented through
frequency distribution tables, pie charts, and histograms among many other ways.
Bivariate analysis; its primary faction is to synthesizes and deduce if there is a
union between two various variables.
Multivariate analysis; analyses three or more variables. There are several ways to
partake multivariate studies like; cluster analysis, canonical correlation analysis, factor analysis,
and additive tree.
Differentiate between inferential and descriptive analysis
Descriptive analysis; uses the information to give a definition of a population,
either via tables, graphs, or mathematical calculations. Descriptive analysis only provides an
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insight into the information understudy and does not permits us to make conclusions past the
information we have analyzed or make a determination of any hypothesis we may have
generated (Benerjee, 2010). Descriptive analysis plays a critical role in the presentation of
information in a more important manner.
Inferential analysis; makes forecasts and implications about a population with
regards to the sample of information derived from the population under study. Inferential
analysis provides techniques that are critical in using samples to make assumptions about a
population from which the sample was taken.
Give examples of an exploratory, descriptive, and explanatory purposes of study
Exploratory study; it is the primary study into a theoretical or hypothetical notion.
It insinuates that a researcher has a clue, noted, or seen something and he would like to keenly
understand the issue. Exploratory study basically provides a basic foundation for future
exploration (mccombes, 2019). Examples of exploratory study include fresh topics or fresh
Descriptive study; it is an effort to explain and explore a topic and at the same
time giving extra information about a subject. It insinuating that a study provides an in-depth
description of activities taking place in a more synchronized manner. At this stage lots of
information are gathered and scrutinized deeply.
Explanatory study; it is a study that is partaken to aid in getting the grievance that
was not looked at before in totality. The explanatory study is not applied to help achieve some
convincing proof but to aid in getting the knowledge about a grievance and how it can be
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Three adjective use to describe the research design
A descriptive study can apply both quantitative and qualitative research
techniques. The techniques should be carefully instituted to ensure that the outcomes are reliable
Survey; allows the collection of data in large volumes that can be synthesized for
pattern, frequencies, and averages (mccombes, 2019). Observation; allows for data collection on
phenomena and behaviors without depending on the accuracy and honesty of the respondents.
Case study; tells a trend of a given subject. Instead of collecting a bigger volume
of information to deduce a trend in a given location or a period of time. Case study collects
detailed information to identify the trends in a particular subject.
Relationship between designs and statistics.
The handling and research designs provide the good techniques of statistical
analysis and the foundation of evaluating the correctness of the treatment means. The method of
correctness attained, either as a confidence interval, standard error should be conveyed for all
information on which a ruling is deduced.
What is the significance of the number of groups in a research study?
Governed groups have basics that provide the similar features of the group under
study. The groups provide a good platform for the study of one variable at a time that helps in the
provision of accurate information (mccombes, 2019). Group numbers also determine the
research method to be used. The population also permits the researcher to partake in a study and
deduce a conclusion that will cut across the whole population.
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Benerjee. (2010). Statistics without tears: Populations and samples. NCBI.
mccombes, S. (2019). Descriptive research . Mccombes.