Comparative intake of sugar in both male and female university students
Project 1
480463034
University of Sydney | DATA1001 | 8/2018
1 Executive Summary
The aim of this report is to compare the intake of sugar in calories (kcal), between female and male students at the University of Sydney. The report evaluated how much each genders caloric intake is sugars, as well as the comparison between each gender. This was done through posing relevant research questions and graphing the data. The main discoveries of the investigation were that firstly, both genders consumed much greater sugar than the recommended amount of 12 grams or 48 calories. Secondly, it was discovered that when comparing males and females the amount of sugar intake was comparable, fluctuating on different days. However, it was also found that males consumed more calories than females overall, which meant that proportionately women had higher caloric intake of sugar in their diets. These conclusions signify that university students need to be eating healthier and their needs to be increased information about not only the harms of excess sugars to health, but also the recommended intake. Whilst, the data quality is sound due to upholding the academic standards of the university, there are a few considerations that need to be made. Firstly, as all the data is based on gendered means of a sample, the data is susceptible to outliers and thus is not robust. Secondly, information about the sample size that the mean is based on was not available or accessible, so in the case that it was only very small means that it cannot be confidently generalised or indicate to the diets of the entire population of university students. Thirdly, another limitation of the data analysed, that doesn’t pertain to the data itself is the use of calories as a measure. This is as the validity of using calorie intake as a means to measure health is largely contested, and this is true for this report as through measuring sugars based on calories, there is no way to distinguish good sugars, from bad sugars e.g. refined sugars. It is on that note, that we recommend that future investigations into the health of university students use the data more holistically and take into consideration other factors, such as fat content or vitamin deficiencies.
2 Full Report
2.1 Initial Data Analysis (IDA)
data = read.csv("~/Desktop/intake24-totals-gender_150818111337.csv",header = T)Top 5 rows of data
head(data)## days.of.the.week Female.Energy..kcal. Female.Energy..kJ.
## 1 Monday 1360.425 5717.741
## 2 Tuesday 1352.858 5686.869
## 3 Wednesday 1475.575 6207.600
## 4 Thursday 1519.694 6389.660
## 5 Friday 1357.162 5705.963
## 6 Saturday 1571.407 6603.983
## Female.Water..g. Female.Protein..g. Female.Carbohydrate..g.
## 1 1784.315 60.79229 160.4112
## 2 1639.915 61.07474 160.6444
## 3 1568.602 65.20705 184.2654
## 4 1863.627 67.09179 185.5101
## 5 1839.690 55.15279 168.3406
## 6 1933.712 71.17219 183.8971
## Female.Alcohol..g. Female.Total.sugars..g. Female.Fat..g.
## 1 1.0259739 288.2679 53.05401
## 2 0.3617844 287.2416 52.52888
## 3 3.9410431 368.1820 50.86440
## 4 1.2908332 326.9380 56.65145
## 5 1.6526414 331.5764 51.28268
## 6 1.6183636 338.6673 61.02043
## Female.Satd.FA..g. Female.Vitamin.D..mg. Female.Vitamin.C..mg.
## 1 19.27282 3.146927 71.19099
## 2 19.93094 2.535090 71.79445
## 3 18.76283 2.430199 83.13516
## 4 20.70082 2.777643 75.67982
## 5 19.69907 2.929043 82.52717
## 6 22.22221 3.387955 69.55725
## Female.Vitamin.B6..mg. Female.Vitamin.B12..mg. Female.Sodium..mg.
## 1 1.662640 3.438939 1674.201
## 2 1.535133 3.014777 1584.658
## 3 1.674231 3.947933 1785.427
## 4 1.661654 2.715390 1938.649
## 5 1.585463 2.506024 1504.865
## 6 1.679094 3.353849 1797.612
## Female.Calcium..mg. Female.Iron..mg. Female.Zinc..mg.
## 1 539.2745 8.934824 7.384138
## 2 524.1590 8.706054 7.079461
## 3 511.1018 8.780419 7.947900
## 4 568.5565 8.826527 8.180472
## 5 532.1772 7.852642 6.711679
## 6 580.3153 8.893229 8.383766
## Female.Vitamin.A..mg. Female.Vitamin.E..mg. Male.Energy..kcal.
## 1 232.4896 2.453975 1825.372
## 2 219.7907 2.155784 1870.763
## 3 111.1282 1.523751 1823.060
## 4 179.9313 2.222800 1701.036
## 5 158.5053 1.871976 1675.363
## 6 171.7233 2.356636 1975.764
## Male.Energy..kJ. Male.Water..g. Male.Protein..g. Male.Carbohydrate..g.
## 1 7667.926 2034.681 95.66886 196.1649
## 2 7863.022 2005.133 102.30432 203.6631
## 3 7653.604 1638.727 89.07166 193.2130
## 4 7149.921 1880.805 81.72015 196.9333
## 5 7032.655 1560.516 79.41107 177.3332
## 6 8302.168 2028.828 102.98324 218.6641
## Male.Alcohol..g. Male.Total.sugars..g. Male.Fat..g. Male.Satd.FA..g.
## 1 2.410968 311.0862 72.32580 26.12519
## 2 1.319218 301.7380 71.75226 24.60986
## 3 1.697199 281.6967 76.72426 26.13038
## 4 0.458188 328.3969 65.87068 22.85110
## 5 1.019822 279.9382 72.30501 23.16460
## 6 1.514731 326.7528 76.60640 27.30130
## Male.Vitamin.D..mg. Male.Vitamin.C..mg. Male.Vitamin.B6..mg.
## 1 3.038315 70.02212 2.063354
## 2 3.516690 80.38795 2.376358
## 3 3.757056 73.19810 2.066385
## 4 3.090036 71.75958 2.276550
## 5 3.726005 47.84052 1.822489
## 6 3.175136 90.42554 2.239613
## Male.Vitamin.B12..mg. Male.Sodium..mg. Male.Calcium..mg. Male.Iron..mg.
## 1 4.063371 2429.171 677.7575 11.633525
## 2 3.797200 2442.557 604.4186 12.814718
## 3 3.340551 2161.759 675.1944 11.513690
## 4 3.084704 2128.141 664.7688 10.527824
## 5 3.478113 2113.964 507.9684 9.275915
## 6 6.371103 2540.312 737.5739 13.273172
## Male.Zinc..mg. Male.Vitamin.A..mg. Male.Vitamin.E..mg. Number.of.Female
## 1 11.018596 198.3722 2.689690 334
## 2 12.196690 265.0726 2.665822 238
## 3 10.138582 211.0397 2.220571 229
## 4 9.561304 230.3426 3.120912 192
## 5 9.219210 193.7868 4.282661 114
## 6 12.663791 176.7076 2.553495 172
## Number.of.Male Day
## 1 157 0
## 2 106 1
## 3 90 2
## 4 86 3
## 5 65 4
## 6 67 5The size of the data
dim(data)## [1] 7 42R’s classification of the data
class(data)## [1] "data.frame"R’s classification of the variables
data = read.csv("~/Desktop/intake24-totals-gender_150818111337.csv",header = T)
str(data)## 'data.frame': 7 obs. of 42 variables:
## $ days.of.the.week : Factor w/ 7 levels "Friday","Monday",..: 2 6 7 5 1 3 4
## $ Female.Energy..kcal. : num 1360 1353 1476 1520 1357 ...
## $ Female.Energy..kJ. : num 5718 5687 6208 6390 5706 ...
## $ Female.Water..g. : num 1784 1640 1569 1864 1840 ...
## $ Female.Protein..g. : num 60.8 61.1 65.2 67.1 55.2 ...
## $ Female.Carbohydrate..g.: num 160 161 184 186 168 ...
## $ Female.Alcohol..g. : num 1.026 0.362 3.941 1.291 1.653 ...
## $ Female.Total.sugars..g.: num 288 287 368 327 332 ...
## $ Female.Fat..g. : num 53.1 52.5 50.9 56.7 51.3 ...
## $ Female.Satd.FA..g. : num 19.3 19.9 18.8 20.7 19.7 ...
## $ Female.Vitamin.D..mg. : num 3.15 2.54 2.43 2.78 2.93 ...
## $ Female.Vitamin.C..mg. : num 71.2 71.8 83.1 75.7 82.5 ...
## $ Female.Vitamin.B6..mg. : num 1.66 1.54 1.67 1.66 1.59 ...
## $ Female.Vitamin.B12..mg.: num 3.44 3.01 3.95 2.72 2.51 ...
## $ Female.Sodium..mg. : num 1674 1585 1785 1939 1505 ...
## $ Female.Calcium..mg. : num 539 524 511 569 532 ...
## $ Female.Iron..mg. : num 8.93 8.71 8.78 8.83 7.85 ...
## $ Female.Zinc..mg. : num 7.38 7.08 7.95 8.18 6.71 ...
## $ Female.Vitamin.A..mg. : num 232 220 111 180 159 ...
## $ Female.Vitamin.E..mg. : num 2.45 2.16 1.52 2.22 1.87 ...
## $ Male.Energy..kcal. : num 1825 1871 1823 1701 1675 ...
## $ Male.Energy..kJ. : num 7668 7863 7654 7150 7033 ...
## $ Male.Water..g. : num 2035 2005 1639 1881 1561 ...
## $ Male.Protein..g. : num 95.7 102.3 89.1 81.7 79.4 ...
## $ Male.Carbohydrate..g. : num 196 204 193 197 177 ...
## $ Male.Alcohol..g. : num 2.411 1.319 1.697 0.458 1.02 ...
## $ Male.Total.sugars..g. : num 311 302 282 328 280 ...
## $ Male.Fat..g. : num 72.3 71.8 76.7 65.9 72.3 ...
## $ Male.Satd.FA..g. : num 26.1 24.6 26.1 22.9 23.2 ...
## $ Male.Vitamin.D..mg. : num 3.04 3.52 3.76 3.09 3.73 ...
## $ Male.Vitamin.C..mg. : num 70 80.4 73.2 71.8 47.8 ...
## $ Male.Vitamin.B6..mg. : num 2.06 2.38 2.07 2.28 1.82 ...
## $ Male.Vitamin.B12..mg. : num 4.06 3.8 3.34 3.08 3.48 ...
## $ Male.Sodium..mg. : num 2429 2443 2162 2128 2114 ...
## $ Male.Calcium..mg. : num 678 604 675 665 508 ...
## $ Male.Iron..mg. : num 11.63 12.81 11.51 10.53 9.28 ...
## $ Male.Zinc..mg. : num 11.02 12.2 10.14 9.56 9.22 ...
## $ Male.Vitamin.A..mg. : num 198 265 211 230 194 ...
## $ Male.Vitamin.E..mg. : num 2.69 2.67 2.22 3.12 4.28 ...
## $ Number.of.Female : int 334 238 229 192 114 172 246
## $ Number.of.Male : int 157 106 90 86 65 67 115
## $ Day : int 0 1 2 3 4 5 6sapply(data, class)## days.of.the.week Female.Energy..kcal. Female.Energy..kJ.
## "factor" "numeric" "numeric"
## Female.Water..g. Female.Protein..g. Female.Carbohydrate..g.
## "numeric" "numeric" "numeric"
## Female.Alcohol..g. Female.Total.sugars..g. Female.Fat..g.
## "numeric" "numeric" "numeric"
## Female.Satd.FA..g. Female.Vitamin.D..mg. Female.Vitamin.C..mg.
## "numeric" "numeric" "numeric"
## Female.Vitamin.B6..mg. Female.Vitamin.B12..mg. Female.Sodium..mg.
## "numeric" "numeric" "numeric"
## Female.Calcium..mg. Female.Iron..mg. Female.Zinc..mg.
## "numeric" "numeric" "numeric"
## Female.Vitamin.A..mg. Female.Vitamin.E..mg. Male.Energy..kcal.
## "numeric" "numeric" "numeric"
## Male.Energy..kJ. Male.Water..g. Male.Protein..g.
## "numeric" "numeric" "numeric"
## Male.Carbohydrate..g. Male.Alcohol..g. Male.Total.sugars..g.
## "numeric" "numeric" "numeric"
## Male.Fat..g. Male.Satd.FA..g. Male.Vitamin.D..mg.
## "numeric" "numeric" "numeric"
## Male.Vitamin.C..mg. Male.Vitamin.B6..mg. Male.Vitamin.B12..mg.
## "numeric" "numeric" "numeric"
## Male.Sodium..mg. Male.Calcium..mg. Male.Iron..mg.
## "numeric" "numeric" "numeric"
## Male.Zinc..mg. Male.Vitamin.A..mg. Male.Vitamin.E..mg.
## "numeric" "numeric" "numeric"
## Number.of.Female Number.of.Male Day
## "integer" "integer" "integer"data = read.csv("~/Desktop/intake24-totals-gender_150818111337.csv",header = T)
names(data)## [1] "days.of.the.week" "Female.Energy..kcal."
## [3] "Female.Energy..kJ." "Female.Water..g."
## [5] "Female.Protein..g." "Female.Carbohydrate..g."
## [7] "Female.Alcohol..g." "Female.Total.sugars..g."
## [9] "Female.Fat..g." "Female.Satd.FA..g."
## [11] "Female.Vitamin.D..mg." "Female.Vitamin.C..mg."
## [13] "Female.Vitamin.B6..mg." "Female.Vitamin.B12..mg."
## [15] "Female.Sodium..mg." "Female.Calcium..mg."
## [17] "Female.Iron..mg." "Female.Zinc..mg."
## [19] "Female.Vitamin.A..mg." "Female.Vitamin.E..mg."
## [21] "Male.Energy..kcal." "Male.Energy..kJ."
## [23] "Male.Water..g." "Male.Protein..g."
## [25] "Male.Carbohydrate..g." "Male.Alcohol..g."
## [27] "Male.Total.sugars..g." "Male.Fat..g."
## [29] "Male.Satd.FA..g." "Male.Vitamin.D..mg."
## [31] "Male.Vitamin.C..mg." "Male.Vitamin.B6..mg."
## [33] "Male.Vitamin.B12..mg." "Male.Sodium..mg."
## [35] "Male.Calcium..mg." "Male.Iron..mg."
## [37] "Male.Zinc..mg." "Male.Vitamin.A..mg."
## [39] "Male.Vitamin.E..mg." "Number.of.Female"
## [41] "Number.of.Male" "Day"As this report endeavours to discover the comparative intake of sugar of male and female students, the relevant variables are Female.Energy..kcal., Male.Energy..kcal., Female.Total.sugars..g., and Male.Total.sugars..g. R classifies all of these variables as numeric and hence, from this it can be understood that these variables are quantitative. As all the values within the relevant variables are measured in calories (kcal) or grams (g), they are floating point numbers and are therefore the variables are continuous, rather than discrete. As all the data was collected and recored on a daily basis, the days.of.the.week variable is significant to this investigation and is classified by R as a factor varible. It can be further understood as a categorical factor variable, as the different days of the week, order the data into their corresponding groups.
The data assessed in this report, is a part of a wider investigation into the nutritional intake of both male and female university students. The data was sourced from a 2018 formal study conducted by the University of Sydney, and was facilitated by researchers Phillip Gough and Emma Simpson of the University of Sydney. As the study was conducted in a formal manner, with the intentions to not only assist students fulfilling assessment requirments, but also as a reference for further studies, the study was conducted to the experimental and academic integrity standards of the university. On top of the data being from a legitimite source, the validity of the data is also well founded as the data was collected using INTAKE24, which is a program that was developed to collect, store and process large amounts of raw data pertaining to dietary information (Simpson, E. et al. 2017). Each row in the data represents a different day in the week in which the experiment was conducted. Each column represents a different variable calculated from the INTAKE24 program. Conversely, the validity of the data is threatened by the the accuracy and honesty of its participants who, when being observed tend to suffer from the Hawthorne effect, and as a result eat healthier and less caloric foods (McCambridge, J. et al. 2014). It is plausible that the Hawthorne effect impacted the data collected, as on average people consumed less calories than the average recommended person. Meaning that people consiously recorded or ate less, because they they thought this would be favourable for the experiment.
The raw data from was converted from sugar in grams to the caloric value of x amount of grams of sugar in order to answer the research questions, to do this it was increased by a factor of 4.
As this data was collected with the intention to be a reference for further studies and usage, the significant stakeholders for this data are: The University of Sydney, other research institutions, insurance agencies, dietitians and food product marketers. The University of Sydney and other research institiutions may be interested in using the data in further diet related or health studies, if it relates to their topic of investigation. Insurance agencies may be interested in this data to assess whether their current pricing model and cover is adapted to the diets and health of their customers. Dieticians may be interested in this data to assist them in creating diet plans for their clients who are attending university. Finally, food product marketers may have an interest in this data for an indication of how many calories a university student consumes daily, so that they can adjust their branding of products in terms of percentage caloric intake and total caloric values.
2.2 How much of the female students the University of Sydney caloric intake is sugars (kcal)?
Key: Green line = mean female overall calories (kcal)
Black line = mean female sugar intake in calories (kcal)
Red dotted line = mean calories
library(ggplot2)
data = read.csv("~/Desktop/intake24-totals-gender_150818111337.csv",header = T)
data = data.frame(data)
femalecal = data$Female.Energy..kcal.
femalesugar = data$Female.Total.sugars..g.
femalesugar = femalesugar
days.of.the.week = data$days.of.the.week
data$days.of.the.week = factor(data$days.of.the.week, levels= c( "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
data = data.frame(femalecal = data$Female.Energy..kcal.,
femalesugar = data$Female.Total.sugars..g.,
days.of.the.week = data$days.of.the.week)
femalecal = c(data$Female.Energy..kcal.)
femalesugar = c(data$Female.Total.sugars..g.)
ggplot(data, aes(x = days.of.the.week, group =1)) +
geom_path(aes(y = mean(femalecal)), colour="red", lty = 2) +
geom_path(aes(y = femalecal), colour="green") +
geom_path(aes(y = femalesugar), colour = "black") +
ylab(label="Calories consumed (kcal)") +
xlab("Day of the week")
Summary:
This graph represents the daily calorie intake of female subjects over a one week period, where the y axis represents the amount of calories consumed (kcal), and the x axis is the qualitative categorical separation of calorie intake into their corresponding day of the week. The green line expresses the mean female overall calories, and the black line expresses the mean female sugar intake in calories. The principal discovery that can be extrapolated from this graph is the alarmingly high percentage of energy that females consume is derived from sugars. The recommended percentage of energy derived from added sugars is 10%, the females in the survey recorded consuming well over double this amount, with a mean of 21.91616% over the seven day survey period. Hence this data reveals that even young people who have largely been educated about nutrition and the dangers of sugar still consume a dangerous amount, which may impact their health in later years if this diet is maintained.
a = mean(data$femalecal)
b = mean(data$femalesugar)
(b/a)*100## [1] 21.916162.3 How much of the female students the University of Sydney caloric intake is sugars (kcal)?
Key:
Blue line = mean male overall calories
Orange line = mean male sugar intake in calories
Red dotted line = mean calories
library(ggplot2)
data = read.csv("~/Desktop/intake24-totals-gender_150818111337.csv",header = T)
data = data.frame(data)
malecal = data$Male.Energy.Kal
malesugar = data$Male.Total.sugars..g.
days.of.the.week = data$days.of.the.week
data$days.of.the.week = factor(data$days.of.the.week, levels= c( "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
data = data.frame(malecal = data$Male.Energy..kcal.,
malesugar = data$Male.Total.sugars..g.,
days.of.the.week = data$days.of.the.week)
malecal = c(data$Male.Energy..kcal.)
malesugar = c(data$Male.Total.sugars..g.)
ggplot(data, aes(x = days.of.the.week, group =1)) + geom_path(aes(y = mean(malecal)), colour="red", lty = 2) +
geom_path(aes(y = malecal), colour="blue") +
geom_path(aes(y = malesugar), colour = "orange") +
ylab(label="Calories consumed (kcal)") +
xlab("Day of the week") 
Summary:
The second graph measures the caloric intake of the male subjects over a one week period, with the y axis being calorie intake (kcal) and the x axis signifying the relevant day of the week. The primary discovery of this graph was that whilst the mean quantity of sugar consumed by the male participants was comparable to that of the females, the overall caloric intake was significantly greater, meaning that added sugars made up a smaller percentage of their diet. Regardless, the male participants also consume well above the recommended amount of added sugars per day. When the weight of sugar was converted into calories, it was discovered that the average male from the survey had a diet consisting of over three times the recommended amount of sugar intake of 12 grams a day or 48 calories. The conclusions made regarding this data set are similar to those made in the female case, with increased risk of health effects such as obesity, heart disease and cancer, which are all directly linked to high sugar consumption.
a = mean(data$malecal)
b = mean(data$malesugar)
(b/a)*100## [1] 16.945382.4 Comparatively how much of female and male students at the University of Sydney’s caloric intake is sugar (kcal)?
Blue line = mean male overall calories
Green line = mean female overall calories
Orange line = mean male sugar intake in calories
Black line = mean female sugar intake in calories
Red dotted line = mean calories
library(ggplot2)
data = read.csv("~/Desktop/intake24-totals-gender_150818111337.csv",header = T)
data = data.frame(data)
femalecal = data$Female.Energy..kcal.
femalesugar = data$Female.Total.sugars..g.
malecal = data$Male.Energy.Kal
malesugar = data$Male.Total.sugars..g.
days.of.the.week = data$days.of.the.week
data$days.of.the.week = factor(data$days.of.the.week, levels= c( "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday"))
data = data.frame(malecal = data$Male.Energy..kcal.,
malesugar = data$Male.Total.sugars..g.,
femalecal = data$Female.Energy..kcal.,
femalesugar = data$Female.Total.sugars..g.,
days.of.the.week = data$days.of.the.week)
malecal = c(data$Male.Energy..kcal.)
malesugar = c(data$Male.Total.sugars..g.)
ggplot(data, aes(x = days.of.the.week, group =1)) +
geom_path(aes(y = malecal), colour="blue") +
geom_path(aes(y = mean(malecal)), colour="red", lty = 2) +
geom_path(aes(y = malesugar), colour = "orange") +
geom_path(aes(y = femalecal), colour="green") +
geom_path(aes(y = mean(femalecal)), colour="red", lty = 2) +
geom_path(aes(y = femalesugar), colour = "black") +
ylab(label="Calories consumed (kcal)") +
xlab("Day of the week")
Summary: The final graph graph compares the caloric intake of male and female subjects over a one week period, with the y axis being calories consumed (kcal) and the x axis being days of the week. The primary discovery of this graph was that the mean quantity of sugar consumed by the male participants was comparable and similar to that of the females. However, the overall caloric intake was significantly greater in males, which means that added sugars proportionally make up an extra 4.970776% of female diet. Thus, it can be concluded, in reference to the research question, that females consumption of sugars (kcal) is proportionally greater than males, however, this result is only gained because males consume more calories overall.
a = mean(data$femalecal)
b = mean(data$femalesugar)
z = (b/a)*100
c = mean(data$malecal)
d = mean(data$malesugar)
y = (d/c)*100
z-y## [1] 4.9707763 Conclusions
The insights provided by our research questions show that both men and women are eating considerably more sugar than the recomended daily intake. It was also possible to conclude that men are eating a mean of 363.7256 more calories than women daily and an extra mean 2546.079 calores in a week. It can also be seen that for both genders, saturday was the highest intake in sugar and overall calories. Most pertinently, as previously stated, women proportionally consume more sugars in regards to their calorie intake but comparitively, in regards to sugar intake alone, both genders consume about as much sugar as eachother.
a = mean(data$femalecal)
c = mean(data$malecal)
c-a## [1] 363.7256(c-a)*7## [1] 2546.0794 References
Gough, P. and Simpson, E. (2018). intake24-totals-gender_150818111337. [ebook] Sydney: The University of Sydney, pp.1-1. Available at: https://livingdata-sydney-data.s3.ap-southeast-2.amazonaws.com/deployment_1/cohort/intake24-totals-gender_150818111337.csv?AWSAccessKeyId=AKIAJDGTKKU6RMHXQEAQ&Expires=1534915179&Signature=9yPy9v7yUtR1y8373xebiH7LdtM%3D [Accessed 21 Aug. 2018].
Simpson, E. Bradley, J. Poliakov, I. Jackson, D. Oliver, P. Adamson, A. Foster, E. (2017) Iterative Development of an Online DIetary Recall Tool: INTAKE24. Nutrients, 9(2): 118.
McCambridge, J. Witton, J. Elbourne, D. (2014) Systematic review of the Hawthorne Effect: New concepts are needed to study research participation effects. Journal of Clinical Epidemiology, 67(3): 267-277
5 Personal reflection on group work
The way I contributed to the group was by writing the executive summary, as well as editing and proofing the report. This ended up being more extensive as I thought as there were a lot of errors through out the report, including classification of variables, what x and y were representing, and what information and conclusions could be extrapolated from the data. I also expanded upon the IDA because it previously wasn’t identifying and analysing all the key aspects, In the end, it meant that I rewrote the entire report so it would be more cohesive. However, the work done by the other members was definitely valuable, notably the coding done by Tom.
What I learnt about group work was that having people who are specialising in different areas of knowledge in a group project is more than valuable. This meant for our group we were able to incorporate health science and biology knowledge, psychology knowledge when referring to the Hawthorne Effect, and also mathematical knowledge.