Statistics Project Description

The goal of this culminating statistics project is to identify the factors that might have some influence on a chosen topic/variable. You will be collecting and analyzing data for three different variables using a secondary source. There will be one dependent variable and two independent variables for which data will be needed.

For example, what are the factors that affect climate change? If climate change were your dependent variable, what could you pick for your independent variable? You will choose two factors that will create models that you can compare, to see if you can find a high correlation. Your final report and summary presentation will include the following sections.

Section 1

https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1310071001

Dependent variable:Choose the main variable that you want to measure, versus other variables. This will go on the vertical axis of your scatter plot. Provide rationale for why you chose this variable and brainstorm different variables that may impact it. Research the variable on the internet and collect data for at least 10 years (the more the better) or over at least 10 different places (e.g., provinces and territories across Canada, countries in the world, teams in a sport) in the same year.

Note:Statistics Canada is our most reliable and prolific source of data. Be sure to cite your source in proper MLA or APA format. You may want to revisit your portfolio entry from Unit 5, Lesson 2 for the data that you found from Statistics Canada. You can also revisit the tutorial video in that lesson.

Dependent Variable: Mortality Rate per 1,000 Population

Rationale:

The chosen dependent variable for this study is the mortality rate per 1,000 population. This variable serves as a crucial indicator of the health and well-being of a population. Mortality rates provide insights into the overall state of public health, effectiveness of healthcare systems, and the socio-economic conditions within a given geographical area.

My brainstorming:

Healthcare Access:

I'm thinking about exploring how easy it is for people to get healthcare. This could involve looking at the number of hospitals, the ratio of healthcare professionals, and the availability of advanced medical facilities in different areas.

Socio-Economic Status:

It might be interesting to see how income levels, employment rates, and education levels relate to mortality rates. This way, we can understand if socio-economic conditions have an impact on health outcomes.

Lifestyle Factors:

Lifestyle choices could play a big role. I'm considering variables like smoking rates, obesity levels, and overall fitness in the population. It's worth exploring how these factors connect to mortality rates.

Environmental Conditions:

I'm curious about how the environment affects mortality rates. This could include factors like pollution levels, climate conditions, and whether people have access to clean water and proper sanitation.

Public Health Policies:

Public health policies might also be a key player. We can look at vaccination coverage, disease prevention programs, and public health awareness initiatives to see how they impact mortality rates.

Demographic Characteristics:

Age and gender might have a say in this. Breaking down mortality rates by different age groups and gender could give us a closer look at demographic influences.

Geographical Variances:

Regions might have different mortality rates. I'm thinking of comparing rates across provinces, territories, or even different countries to spot any regional patterns.

Nutritional Habits:

Nutrition could be a factor too. Looking at access to nutritious food, prevalence of malnutrition, and overall dietary habits could give us insights into the role of nutrition in mortality rates.

Healthcare Infrastructure:

Infrastructure matters. Assessing things like the number of hospital beds, availability of medical technology, and emergency response capabilities could tell us how healthcare infrastructure ties into mortality rates.

Prevalence of Chronic Diseases:

Chronic diseases might be contributing. Exploring data related to diseases like heart disease, diabetes, and cancer could help us understand their role in overall mortality rates.

Source citation:

Mortality Rates, by Age Group, Government of Canada, Statistics Canada, 27 Nov. 2023,www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1310071001&pickMembers%5B0%5D=1.1&pickMembers%5B1%5D=3.1&cubeTimeFrame.startYear=2013&cubeTimeFrame.endYear=2022&referencePeriods=20130101%2C20220101.

Section 2

Independent variables:Choose the independent variables that you want to measure against the dependent variable. You will find two different variables for which you can create a line of best fit to test correlation. This will be your biggest challenge of the assignment. You need to find variables that overlap with the data you chose for your dependent variable.

For example: If you found the years 1990 to 2010 for your dependent variable, you will need to find those

same years for each of your independent variables. If you had data across the provinces and territories for your independent variable for 2016, you will need data across the provinces and territories for 2016 for each of your independent variables.

My two independent variables:

Independent Variable 1:

Healthcare Access - Number of Hospitals per 1,000 Population

Explanation: The number of hospitals per 1,000 population is a key indicator of healthcare access within a given region. It reflects the availability of medical facilities to address the health needs of the population. In this study, this variable is chosen to explore the correlation between healthcare access and mortality rates.

To obtain this data, Statistics Canada (27 Nov. 2023) provides information on the number of hospitals per 1,000 population for the years 2013 to 2022.

Link to source: www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1310071001&pickMembers%5B0%5D=1.1&pickMembers%5B1%5D=3.1&cubeTimeFrame.startYear=2013&cubeTimeFrame.endYear=2022&referencePeriods=20130101%2C20220101.

The rationale behind choosing this independent variable is rooted in the understanding that the availability and accessibility of healthcare facilities can significantly impact health outcomes. If a population has better access to hospitals, clinics, and healthcare services, it is likely to experience lower mortality rates. Analyzing this variable alongside mortality rates by age group ensures a comprehensive examination of the relationship between healthcare access and different demographic segments.

Independent Variable 2: Socio-Economic Status

Explanation:

Another independent variable to explore is the unemployment rate. Socio-economic conditions play a significant role in determining the health of a population. Unemployment can lead to financial stress, affecting access to healthcare, nutrition, and overall well-being.

Link to source :

www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=1410000101&pickMembers%5B0%5D=1.1&pickMembers%5B1%5D=3.1&cubeTimeFrame.startYear=2013&cubeTimeFrame.endYear=2022&referencePeriods=20130101%2C20220101.

By examining the relationship between the unemployment rate and mortality rates within the same time frame and age groups, the study aims to uncover insights into how socio-economic factors contribute to health outcomes.

Both independent variables are essential in understanding the multifaceted factors influencing mortality rates, and by using Statistics Canada data, the study ensures reliability and consistency across the chosen variables and time frames.

Section 3

One variable measures with graphs and distributions: Describe each of your three variables (two independent and one dependent) by calculating and making reference to the measures ofcentral tendency andspread. Also create a histogram and box plots. Describe thedistribution of your data as well. Describe the meaning of each of the measures of each variable, as you state what they are.

Section 4

Two variable measures with linear models and correlations: Create two scatter plots, one for each of your independent variables. Create the linear model to describe the relationship. Describe the strength of the correlation and the meaning of the slopes and y-intercepts.

Section 5

Identify and remove relevant points: Identify one point on each scatter plot that has the greatest impact on the model by using residual plots. Here is a great little tutorial on how to see aresidual plot in Desmos. Describe the percentile that the point would be, if the data were normally distributed, by using the mean, standard deviation, and z-score. Describe the impact of the point by comparing the new linear model to the original.

Section 6

Classification of correlation type:For each of the models, classify the correlation type as cause and effect, reverse cause and effect, accidental, or common cause. Explain your choice and brainstorm possible common cause factors, where applicable.

Section 7

Conclusions from linear models: Make conclusions from the two variable analysis, taking into consideration the removal of points that are far from the line of best fit. Discuss the overall impact, if any, that the independent variables have on the dependent variable, by referring to other sections of the report.

what do i do for the rest of the project??