This graph illustrates the relationship between gender and daily alcohol intake. As shown, males abuse alcohol more than females by almost 79% in the harmful category, while females are at 20%. Interestingly, in the hazard section, female alcohol consumption is at 58% which is 19% more than males, who are at 39%. This depicts that while males abuse alcohol in the harmful section, daily intake by females in the hazard section greater than males. Moreover, from the abstainer to the low section, male alcohol level intake increases from 39% to 63%, while females decreased from 59% to 36%. This statistic presents that majority of males abuse alcohol than females, daily.
This bar chart illustrates if alcohol consumption affects the social life of males and females. It uses the time from of less than or exactly 12 months, more than 12 months, and a “no” option. Males are shown to have a negative and harmful effect. For males not past 12 months, 63% said that alcohol use affected them. Even past 12 months, there is 1% increase of 64%. However, 47% of males stated that alcohol did not have a negative effect in their life. This percentage is close to the 52% of females that also shared similar views. Females who have had consumed alcohol before and after 12 months, had low and consistent percentage rates of 37% and 35%.
This graph depicts if drugs have a harmful effect on a male or female’s financial position. Going with the previous graphs, males have a negative impact by drugs, by 70%, while females are at a low 30%. However, if we compare males who have not been negatively affected by drugs, at 53%, there is only a 17% difference. 45% of females have never had a problem with their financial position. This corresponds with the 30% of females that do have a problem with their financial position due to drugs, by 15%, which is low compared to males.
HYPOTHESIS 2: GENDER VS. INTAKE
Illustration Analysis
Gender plays a role in drug abuse and intake. This hypothesis focuses on how the amount of drug and alcohol consumption varies between genders. By examining the data, we will find out what gender succumbs to drugs and alcohol. An individual’s lifestyle and cultural values affects their relationship to substance abuse, however, gender can also factor in as well. It is important to fund our research because the relationship between gender and addiction has yet to be studied in such a depth manner. With the information we will retrieve, we will find out what gender consumes that most, at what age, and why.
Descriptive Numerical Statistics
Calculations
Male
Mean: 61.07
Standard dev: 20.14
Max: 79.4
Min: 39.5
Range: 39.9
Female
Mean: 38.93
Standard dev: 20.14
Max: 60.5
Min: 20.6
Range: 39.9
Analysis
The SDA summary statistics shows the results of mean and standard deviation for each column. For example, it provides the mean and standard deviation for each level of intake in relation to each gender. However, for a much deeper and overall analysis the mean, standard deviation and the range was calculated for the total of each gender. Therefore, the average amount of alcohol intake the male population consumes is 61.07% and the standard deviation is 20.14% This data shows that there is a large gap between the mean and standard deviation indicating that men are more likely to consume alcohol. Whereas, the average for female population is 38.93% and relatively close standard deviation of 20.14% indicating that in comparison to the male population females are less likely to consume alcohol.
Analysis:
The statistics show that the male population is highly likely to have harmful effects in their social circle due to alcohol consumption. The max for males represent 65% whereas only 35% of the females expressed negative effects on social circle. Both the genders compared to one another display opposite results, where males are more affected and females are not as likely to be affected, such a result displays a -2.04 flat peak of kurtosis, which makes the data gather together towards the peak instead of the tails. When comparing the mean for each gender in relation to “yes not past 12 months” and “yes past 12 months” (column) the mean and standard deviation is relatively low and identical in this case which means the data is very close to the mean.
Calculations:
Males: yes
Mean: 64.2
Max: 65
Min: 63.4
Females: yes
Mean: 35.8
Max: 36.6
Min: 35
Both genders
Kurtosis: -2.04 (flat peak)
Calculations:
Mean: 50
Kurtosis: 0.849 (peak)
Coefficient variable: 0.328
Standard deviation: 16.431
Analysis:
The data shows that the numbers are relatively similar to each other for both “never” and “ yes” category for each gender. The male population is 69% likely to be financially unstable whereas only 30% of females are unstable. The coefficient variable is 0.328 in relevance to the mean of 50, which means that it is not that significant. The low standard deviation in relevance to the mean indicates that both the genders are close to the mean. The standard deviation of 0.2 for each gender in relation to “never” and “yes” indicate the same calculations which means that they are equally close to the mean of 0.3 in terms of column calculation.
Graph #1
Graph #2
Graph #3
