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A Sample Do My Statistics Homework Assignment

Correlation Between Food And Health Variables

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Increasing advancements in technology and the overall changes in the general health of the citizens, state indicators are needed. Focus is directed to food accessibility, food insecurity, the food prices and how health is affected in various counties and regions. Food is essential to the general health and the interactions affect the food chosen and quality of diet (Nestle, 2002). The objectives of the Food Environment Atlas are;

  • To gather statistics on food indicates increase research o what determines the choice and quality of food.
  • To give a general overview of the community capability to have healthy food and how it has accomplished it.

The food choice is an indicator of how readily the community can access healthy food. This is determined by the grocery store proximity, food stores and restaurants available, expenses on fast food, participation in food and nutrition assistance programs, price of food and how available the local foods are. Another indicator is on community characteristics that affect the food environment. These include natural facilities, centers for fitness and recreation, poverty and income, loss of population and the demographic organization. Health and the well being of the community is an indicator of how preserving the healthy diets can be accomplished. This is through the level of physical activities, rates of diabetes and obesity and food insecurity[3]

Access and proximity to grocery stores

The indicator is the population and low access to the grocery stores. The data shows the how the location of a county either in urban or rural areas affects the accessibility of the grocery stores. The percentage was determined by calculating the distances to the nearest supermarkets and large groceries for each grid and then aggregating the number of people who lived 1 mile from the store in urban areas and more than 10 miles in rural areas to the county level and then divided by the county population. This gave the percentage of the population located in more than 1 or 10 miles from the main stores. The income earned by the individuals the individuals affected the accessibility of the stores.

The income level is defined by the annual income of the Federal poverty threshold based on the size of the family. One is said to have a low income if the annual income of the family is less or equal to 200% of the federal poverty threshold. The accessibility to the stores by children and also that of the seniors has an impact on the health of the community. The children who cannot access the stores suffer from nutrition deficiency. The aged do not have access to the store located far from their homes. Children are considered to be individuals in a community who are age 18 years and below. The seniors are individuals aged 65 years and above. The households, with no car and low access are another indicator. Housing units located far from the groceries and have no car will have low access them.

Store availability

The first indicator is the grocery stores. These are supermarkets and smaller grocery stores that retail food products. The food includes fresh fruits, canned and frozen food, and fresh meat. The number of grocery stores per the population of the county are an indicator of how easily an individual can acquire the food (Wahlqvist, 1987). If the population is high compared to the number of the groceries, the demand is not met. The supercenters and the club stores are other indicators. They are involved in retailing the line groceries and merchandise. The population of the county per the number of these facilities will determine if the demand is met. Convenience stores retail goods that are limited such as snacks, milk and bread. Specialized stores retail foods as meat and seafood markets, bakeries and produce markets. The SNAP authorized stores and WIC authorized stores are available.

Restaurant availability and expenditure

The fast food restaurants deliver food and drinks to the consumer location. The services and food are paid for before consuming. In full service restaurants, the consumer pays after eating and is served while seated. In the expenditure per capita indicator, the patron chooses to pay for the picked items before eating. The amount of money that one has determines what one gets. The reduced price of lunch meals to students is essential to make food affordable to all therefore a healthy society (Frumkin, 2006).

Health and physical activity

High schoolers physically active (%) indicator shows that the number of high school students who do physical activities are healthier compared to those who do not. The rate of diabetes and obesity in adults in relation to the food accessibility is well indicated. The diseases may limit the person’s movement making the accessibility to the required dietary impossible. The weight and the height is also an indicator of the rate of obesity to adults. The children are also considered to be obese if their body mass index is greater than kilograms pr meter squared.

Social economic characteristics

The origin of the population whether white or black indicates their lifestyles and hence the health of the county. The median household income divides the household of the county to income above and below the median to individuals aged 15 years and above. Poverty hinders one from accessing proper diet. Most county resident’s income is below poverty threshold (Baer, 2003).


It is important to follow a sequential procedure so as to get accurate results. The procedure for this project is as follows;

  • Read and interpret the data provided. The state indicator in each category is established and the also the geographical category.
  • Select the variables of interest for sampling. Since our population sample is large, we only take 10% of the population. For small samples, 30% of the population is recommendable.
  • Tables of the selected data is drawn in an excel spread sheet.
  • Determine the dependent and the independent variable.
  • Draw a scatter graph to represent the data.
  • Draw the best line of fit to determine the r value and the type of correlation whether positive or negative.
  • Analyze the graphs.
  • Give a summary and conclusion of the results obtained.

Data organization

  • Graph of children obesity versus the fast food restaurants.
  • Graph senior’s obesity versus the food accessibility.
  • Graph of food assistance versus the social economic characteristics.
  • Graph of farm vegetables against the grocery stores.

Comparison to US indicators

The relation of various variables in US are shown in the graphs below. The scatter plots show the relationship or correlation between these indicators. In a positive correlation, both variables move in the same direction while in a negative correlation, the variables move in the opposite directions. If the best line of fit is vertical, horizontal or it cannot be drawn due to lack of pattern in the data points, there is little or no correlation. For a strong correlation value ranges between ±0.85 to ±1. r values for moderate correlation will range between ±0.75 and ±0.85. Weak correlation has r ranging from ±0.6 to 0.74 (Devore, 1986).


FFRPTH11= fast food restaurant/1000 2011

FFR11= fast food restaurant 2011

r =0.0259. There is no co-relationship between the obesity of the children with access to fast food restaurants. However the gradient of the graph indicates that the more the children access the fast food, the more they are prone to being obese.

R2= -0.05. The r value is below small which shows that there is no between the obesity in the adults (senior) and their accessibility to food. The senior’s obesity does not affect the accessibility of food in the county. The likelihood of high obesity rate can occur with high accessibility to food just as low rate of obesity can occur with high accessibility. Senior’s obesity does not affect their accessibility to food.

PCT_NHWHITE10, PCT_NHBLACK10, PCT_HISP10, PCT_NHASIAN10, PCT_NHNA10, PCT_NPI10 refers to socioeconomic characteristics of white, black, Hispanic, Asian, American Indian and Hawaiian or Pacific Islander in 2010 respectively.

Their R2 values are 0.11, 0.0056, 0.142, 0.0261, 0.0001 and 0.0017 respectively. Both the values of PCT_NHWHITE (0.11) and PCT_HISP10 (0.142) shows that there is no correlation between the socioeconomic characteristic and food assistance. The participation in food assistance programs has no effect on the socioeconomic characteristics of the county.

R2= -0.069. There is no co-relationship between the numbers of farms and vegetables harvested for fresh market with the number of grocery store available. This shows that as the number of farm harvested increases, the number of grocery stores reduces by a magnitude of 0.069. We would expect the number of grocery stores to increase proportionally but considering people will be able to get the vegetables from the farms as the number of farms rises, then there is little need for stores.

Summary and Recommendations

The data given is not reliable as there is no correlation of the given variables. Food is essential to health and therefore appropriate data should be collected to show the relationship. The obesity in children is independent of the food offered on the restaurants. This however is contrary to what we would expect; obesity to vary depending on what the restaurants offer. The seniors` obesity varies independently with the food accessibility. We would have expected that obesity will make it impossible for the seniors to walk to the food stores but this does not happen. This might be due to other means of accessing the grocery instead of walking. Food assistance programs have no effect on the socioeconomic characteristics of the counties’ population.


Baer, D. (2003). State handbook of economic, demographic & fiscal indicators (5th ed.). Washington, DC: Public Policy Institute, AARP.

Devore, J., & Peck, R. (1986). Statistics: The exploration and analysis of data. St. Paul: West Pub.

Food environment atlas. (2000). Washington, D.C.?: U.S. Dept. of Agriculture, Economic Research Service.

Frumkin, N. (2006). Guide to economic indicators (4th ed.). Armonk, N.Y.: M.E. Sharpe.

Nestle, M. (2002). Food politics: How the food industry influences nutrition and health. Berkeley: University of California Press.

Wahlqvist, M. (1987). Food & health: Issues and directions. London: J. Libbey.

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Sample College Statistics Assignment Essay


Applying Statistics to Research

The article discusses two studies conducted. One to analyze the extent to which journal articles include quantitative references in their research and second to analyze the frequency of the referencing by researchers of the articles found in quantitative journal publications (Mills, Abdulla & Cribbie, 2010).The study analyzed 1161 articles from different psychology articles. Only 1149 articles from the six journals which include JAP, JCCP, JPSP, CD, PB and IJOP got organized into statistically relevant forms by tabulation and by the use of the frequency distribution charts for study one. Subsequently, the researchers used the statistical tools of measures for central tendency and sample paired t-test to analyze further the data (Mills, Abdulla & Cribbie, 2010).

In the first study, according to the data collected, the distribution charts revealed some outliers. Outliers relate to the articles referencing more of the articles than the average. The frequency distribution shows the relative referencing of both the non-quantitative references and quantitative references (Mills, Abdulla & Cribbie, 2010).In the normal language, the charts reveal the frequency with which researchers use the non-quantitative research and quantitative research references. The measures of the central tendency reveal that majority of the references used by psychologist researchers are non-quantitative. This is because the mean and mode for non-quantitative research use are 2.2 and 0 about the 65.7 and 43. The mode relates to the most frequent number of references used while mean the average number of references used by the researchers. The t-paired test revealed significant differences between the quantitative references and the non-quantitative references (Mills, Abdulla & Cribbie, 2010). The results of the first study thus rejected the null hypothesis that the number of quantitative references used in research is many.

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The second study evaluated the extent to which the quantitative research methodologies are used by both quantitative and non-quantitative researchers. The study utilized articles from four journals majoring in quantitative research which include Psychometrika, British Journal of Mathematical and Statistical Psychology, Journal of Educational and Behavioral Statistics as well as the Journal of Psychological Methods (Mills, Abdulla & Cribbie, 2010). The frequency of the referencing of the articles was monitored by the Scientific Information’s Web. The data was organized and analyzed according to the year of referencing and the authors. The results then got compared using the Welch test on ranked data. The test helps in determination of the references across the duration in question, for instance, in this case, it is for the years 1993, 1994, 2003 and 2004. The result showed more references in the 90s relative to the 2003 and 2004 figures. The research also conducted t-test and p-test to investigate the differences in data (Mills, Abdulla & Cribbie, 2010).

Notably, the statistical tools used in the analysis of data for both studies were accurate and relevant. The research was well designed and randomized to enhance the precision of the conclusions made. Further, the conclusion made reflect the quantitative data analysis utilized by the researchers. The major weakness of the research is that it points to the various articles where non-of the appropriate and scholarly statistical research methods are not utilized but fail to give the specific examples of such articles. Further, the assumption of the research that the articles chosen are the real representation of the real situation could be limited. Also, the years of the research may not necessarily represent the robustness of the data expected. According to the conclusion, there is the conclusion that most of the psychologist researchers fail to utilize the statistically advanced research methodologies in relevant journal articles.


Mills, L., Abdulla, E., & Cribbie, R. A. (2010). Quantitative Methodology Research: Is it on Psychologists’ Reading Lists? Tutorials in Quantitative Methods for Psychology, 6(2), 52-60.

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