Micheál Martin has been impressed by your Tableau dashboard comparing the situation of Ireland and 9 other countries on the COVID-19 pandemic. The Taoiseach all the National Public Health Emergency Team (NPHET) has decided to promote your position as Senior Data Analyst.
NPHET asked you to conduct an academic research with statistical analyses to understand the factors behind the situation of Ireland and 9 other countries regarding the COVID-19 data using the data included in the document “df_country_data.csv”1 that has been sent to you by email for the previous assignment. NPHET expects you to publish an academic research paper presenting your results.
For your statistical analyses, you can also use the files “df_country_metadata.csv” and “df_global.csv” that has also been send to you for the previous assignment.
Your academic research paper should investigate between 3 and 4 hypotheses. Write an academic research paper, no more than 5 pages long, including:
• - A short abstract,
• - A short literature review with no more than 5 references which ends with your hypotheses,
• - A method section presenting your data. Your method section should present how the hypotheses are tested with the corresponding model representation and equation,
• - A result section including descriptive statistics and inferential statistics,
• - A discussion and conclusion section which examines the hypotheses and their results,
• - A reference section listing the academic papers used in your document.
The format of your academic research paper should follow the style from one of three templates proposed (see “template_1.pdf”, “template_2.pdf”, and “template_3.pdf” attached).
This research paper can be based on the same story that the one used in your previous tableau dashboard or be based on a completely different story.
Obtaining statistically significant results (i.e., p < 0.05), rather than not significant results, will not lead to a higher mark. The only assessment criteria are the following:
• Accuracy of variables, hypotheses, model, and equation description,
• Conformity of statistical analyses and results’ interpretations,
• Relevance of Tables and/or Figures,
• Overall presentation style.
1. On the MT5000 Loop page, section Assignment.
2. In a .pdf file.
3. By Wednesday April 14 , 2021.
Note, any page after the 5th page will not be assessed (whatever it is in the appendix or in the main document). Each data attributed to students are unique and will prevent copy-paste of results from another dataset attributed to another student.
A guide for academic research
In academic research paper all sections are linked:
Introduction ➡️ Literature Review ➡️ Method ➡️ Results ➡️ Discussion & Conclusion
To understand the statistics in the results section it is essential to identify the concepts presented in each section:
A variable …
• Is way of assigning values (numbers or characters) to labels
• Corresponds to a column in a spreadsheet
Challenge: Identify the Role and the Type of each variable
There are many type for variables but the only that we should care are … - Continuous: If values are numbers - Categorical: If values are characters
Note: Distinguish Categorical Nominal variables (e.g., Irish, French) vs. Categorical Ordinal variables (e.g., XS, S, M, L, XL)
A variable can have one or the other of these roles (no other role exist):
• Outcome: “to be explained” variable as Y (also called Dependent Variable or DV)
• Predictor: “doing the explaining” as X (also called Independent Variable or IV)
Note: A variable can be also both but in different hypotheses
Hypotheses are:
• Predictions supported by theory/literature
• Affirmations designed to precisely describe the relationships between variables
A hypothesis test consists of a test between two competing hypotheses:
• An alternative hypothesis Ha (also called H1) formulated by the researcher
• A null hypothesis H0 (pronounced “H-naught”) counter to every alternative hypothesis
Note: For H0, there is no relationship between the variables. Ha is the “challenger” hypothesis, it claims the existence of a relationship.
Only 2 kind of alternative hypotheses can be formulated:
• Main Effect Hypothesis: Relationship between 1 Predictor and 1 Outcome
• Interaction Effect Hypothesis: Relationship between 2+ Predictors and 1 Outcome
Challenge: Appropriate Formulation the hypothesis according to the type of the Predictor
The Outcome has to be Continuous but …
• Case 1: Predictor is Continuous
The {outcome} increases when {predictor} {increases/decreases/changes}
• Case 2: Predictor is Categorical (2 Categories)
The {outcome} of {predictor category 1} is {higher/lower/different} than the {outcome} of {predictor category 2}
• Case 3: Predictor is Categorical (3 or more Categories)
The {outcome} of at least one {predictor} category is {higher/lower/different} than the other {predictor} categories
The Outcome has to be Continuous and whatever the Predictor 1 is …
• Case 1: Predictor 2 is Continuous
The effect of {predictor 1} on {outcome} is {higher/lower/different} when {predictor 2} increases
• Case 2: Predictor 2 is Categorical (2 Categories)
The effect of {predictor 1} on {outcome} is {higher/lower/different} for {predictor 2 category 1} than for {category 2}
• Case 3: Predictor 2 is Categorical (3 or more Categories)
The effect of {predictor 1} on {outcome} is {higher/lower/different} for at least one of {predictor 2}
Notes:
1. Predictor 1 and 2 are commutable (can be inverted and produce the same hypothesis)
2. An interaction effect hypothesis is also called moderation effect
3. By default, an interaction effect involves the test of the main effect hypotheses of all Predictors involved
The basic structure of a statistical model is:
Outcome=Model+Error
where the Model is a series of predictors that are expressed in hypotheses related to the same outcome:
• Main effect hypotheses are indicated with the predictor name only
• Interaction effect hypotheses are indicated with all predictor names separated by ∗
Example:
Outcome=Pred1+Pred2+Pred1∗Pred2+Error
To evaluate their relationship with the outcome, each effect hypothesis is related with a coefficient called Estimate and represented with β as follow:
Outcome=β0+β1Pred1+β2Pred2+β3Pred1∗Pred2+Error
Note: β0 is the estimate related to the intercept. It is always included, always tested but has no interest in the analysis
Testing for the significance of the effect means evaluating if this estimate β value is significantly different, higher or lower than 0 as hypothesised in Ha:
• β≠0 means our hypothesis doesn’t precise the direction of the change, just that there is a change
• β>0 means our hypothesis indicates that the relationship increases or a group is higher than another group
• β<0 means our hypothesis indicates that the relationship decreases or a group is lower than another group
Note: H0 will always predict that β=0
The significance, called p-value, is the probability to consider H0 as True. This probability is between 0% and 100% which corresponds to a value between 0.0 and 1.0.
If the p-value:
• Is higher than 5% or 0.05, then H0 is accepted
• Is lower than 5% or 0.05, then H0 is rejected and Ha is considered as plausible
A graphic representation of the model’s hypothesised effects can be done: - All the arrows correspond to an hypothesis to be tested - All the tested hypotheses have to be represented with an arrow
A simple arrow is a main effect
PredictorOutcomeβ1
A crossing arrow is an interaction effect
Predictor 1β1OutcomePredictor 2β2β3
Note: By default, an interaction effect involves the test of the main effect hypotheses of all Predictors involved
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