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Grifin Berge

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1) A- The question addressed in this study: can the size of your brain indicate your intelligence? It is hypothesized that the size of your brain does indicate how intelligent you are. The null hypothesis: the size of your brain does not indicate how intelligent you are. This fascinating question has been addressed with varying answers for animals, mammals, primates and humans. In this recent study, a group of 40 college students (20 male/20 female) volunteered to have their brain size measured (pixels) by MRI scanning, and took an intelligence test. The intelligence test scores were measured from standard testing. Another variable was taken into account with this study which was body weight (lbs) of the individuals. It is predicted that, the larger your brain is, the higher your intelligence is. In this study, we predict to see higher scores on the intelligence test for individuals with larger brains, and lower scores for those with smaller brains.

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Variable Groups Distribution Central tendency Dispersion

Brain size (pixels) Male Normal Mean = 862654.6 SD = 55911.4

Female Normal Mean = 954855.4 SD = 55893.6

Weight (lbs) Male Normal Mean = 166.44 SD = 20.0


IQ Male Normal Mean = 111.6 SD =23.5


Table 1. Corresponding study variables with normality and descriptive statistics.

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Fig 1. Mean IQ scores for college students, both male and female (n = 40). Boxes show the first and third quartile with the median shown in center line. Hairlines show minimum and maximum values.

Fig. 2. The relationship between IQ score and the size of the brain, IQ score and weight of the student, and size of the brain and weight of the student. Values expressed for both males and females.

Fig. 3. The relationship between IQ scores and the size of the brain (pixels) for both male and female college students.

D-The IQ levels of volunteering college students (20 male/20 female) were assessed with

corresponding and different size (pixels) brains. The size of the brain for each individual was determined by pixel size after MRI scanning. The mean value of IQ scores for both males and females was very similar at 111, with minimum values of 72 and maximum values of 150 (Fig. 1). All variables (weight, IQ, brain size) had normal distribution (Table 1). Figure 2 shows a matrix scatterplot of the variables. There is a positive correlation for brain size and IQ; there is no correlation for student weight and IQ. In figure 2, we can visually see that the variable (weight of student) did not seem to have any correlation with the IQ of the student (Pearson correlation, r = 0.003, P>0.5, n = 40, fig. 4). Both variables, IQ and brain size, did have a strong correlation (Pearson correlation, r = 0.387, P>0.05, n = 40, fig. 4).

Fig. 4. Pearson correlation between variables: IQ, weight (lbs), and brain size (pixels).

A linear regression confirmed positive correlation between brain size and IQ for both males and females (Linear regression, R2 = 0.16 female, R2 = 0.32 male, n = 20 female, n = 20 male, Fig. 3).

Correlations

IQ BRAIN_SIZE WEIGHT

IQ Pearson Correlation 1 .387* .003

Sig. (2-tailed) .014 .988

N 40 40 38

BRAIN_SIZE Pearson Correlation .387* 1 .513**

Sig. (2-tailed) .014 .001

N 40 40 38

WEIGHT Pearson Correlation .003 .513** 1

Sig. (2-tailed) .988 .001

N 38 38 38

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

E- Based on the results that we obtained from the data set values, we can conclude

that the size of your brain can indicate your intelligence, and we concluded that the weight of a person cannot indicate your intelligence.

It was concluded that the size of a person’s brain can indicate how intelligent that person is, figure 3 is a linear regression test which indicates a positive correlation between size of brain and IQ, for both males and females. This means that we could predict a person’s IQ based on the size of their brain. Figure 4 shows the Pearson correlation value for IQ and size of brain, both of which having a “closer to +1” value, indicating a positive correlation.

It was concluded that the weight of a person has no relationship with how intelligent that person is. Figure 2 indicates that, in the 40 persons we studied, there is no real positive correlation between IQ and weight. This is also indicated in figure 4. The Pearson correlation value obtained for weight, correlating to IQ, was 0.003, indicating no positive correlation.

2) A-

Animals use energy in three main properties of their life-history: growth, maintenance, and reproduction. Because energy is limited, most species show evidence for this idea of life-history tradeoffs, where the animal focuses a lot of attention in one area of energy utilization, resulting in deficits on other areas.

This study used 5 groups of fruit flies, with 25 males in each group. Group 1 included only the males, no females were present (control group). Group 2 included one previously mated female to every one male (studies show female fruit flies do not mate again following their first mating). Group 3 included eight previously mated females to every one male. Group 4 included one virgin female to every one male and group 5 included eight virgin females to every one male. Other data was collected during this study including the size of the male’s thorax (mm) and percent of the day spent sleeping for each male. It is possible the last two variables could influence male longevity or mating.

The question addressed in this study asks whether there is evidence for life-history tradeoffs between survival and reproduction in fruit flies, specifically whether energy spent on mating alters a male’s longevity? In other terms, if a male fruit fly exerts more energy in his life focusing on reproduction, will he limit how long he actually lives? It is hypothesized that if a male fruit fly exerts more energy in mating, another main aspect of his life, like lifespan, will be affected. The null hypothesis is: if a male fruit fly exerts more energy in his life to mating, his lifespan will not be altered or affected. We predict to see the lifespan of male fruit flies reduced when more of their energy is spent mating. We would expect to see the lowest lifespan (days) in males who are housed with a higher number of virgin females.

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Group Variable Distribution Central Tendency Dispersion#1control

Longevity normal Mean =63.56 SD =16.45

Thorax size (mm) normal Mean = 0.84 SD =0.08

Sleep Skewed left Median =23.00 Range =82.00

#2(1) mated female

Longevity normal Mean =64.80 Range =15.65

Thorax size (mm) normal Mean =0.83 SD =0.07

Sleep (days) normal Mean =21.56 SD =12.46

#3(8) mated females



Sleep Skewed left Median =21.00 Range =62

#4(1) virgin female



Sleep Skewed left Median =21.00 Range =68

#5(8) virgin females



Sleep normal Mean =20.76 SD =10.74

Fig. 5. Mean longevity of 5 groups of male fruit flies used in study, error bars present.

Fig. 6. Mean longevity (days) of male fruit flies compared to amount of sleep (%) in a day.

Fig. 7. Mean thorax size in each of 5 groups of male fruit flies, error bars present.

Fig. 8. Mean longevity (days) in male fruit flies compared to the size of their thorax (mm).

Fig. 9. Mean longevity (days) in male fruit flies compared to what type of female fruit fly was housed with it (already mated, virgin, no female).

Fig. 10. Mean longevity (days) in male fruit flies with females housed with them, error bars present. Females housed were either, 0 (none), 1 or 8.

D-The longevity (days) of male fruit flies (n = 125) was assessed when percent of

the day sleeping, size of their thorax, number of females present, and status (mated/virgin) of females present was taken into account. The males were divided into five different groups (no female, 1 mated female, 8 mated females, 1 virgin female, 8 virgin females). Most all of the variables had normal distribution, with the exception of group 1 sleep, group 3 sleep, and group 4 sleep.

The average longevity (days) of groups 1-3 was very close together, approximately 64 days, while the average for group 4 dropped to 57 days, and group 5 dropped to 39 days (figure 5). There was significant longevity differences only between fruit flies in group 5 (Bonferroni corrected post-hoc analysis. P > 0.001, table 3). This would make sense because group 5 included 8 virgin females, and according to our predictions, the more non-mated females which may be present can create shorter lifespans for the male fruit flies.

Multiple Comparisons

longevity

Bonferroni

(I) Group (J) Group Mean

Difference (I-J) Std. Error Sig.

95% Confidence Interval

Lower Bound Upper Bound

dimension2

1

dimension3

2 -1.240 4.188 1.000 -13.22 10.74

3 .200 4.188 1.000 -11.78 12.18

4 6.800 4.188 1.000 -5.18 18.78

5 24.840* 4.188 .000 12.86 36.82

2

dimension3

1 1.240 4.188 1.000 -10.74 13.22

3 1.440 4.188 1.000 -10.54 13.42

4 8.040 4.188 .573 -3.94 20.02

5 26.080* 4.188 .000 14.10 38.06

3

dimension3

1 -.200 4.188 1.000 -12.18 11.78

2 -1.440 4.188 1.000 -13.42 10.54

4 6.600 4.188 1.000 -5.38 18.58

5 24.640* 4.188 .000 12.66 36.62

4

dimension3

1 -6.800 4.188 1.000 -18.78 5.18

2 -8.040 4.188 .573 -20.02 3.94

3 -6.600 4.188 1.000 -18.58 5.38

5 18.040* 4.188 .000 6.06 30.02

5

dimension3

1 -24.840* 4.188 .000 -36.82 -12.86

2 -26.080* 4.188 .000 -38.06 -14.10

3 -24.640* 4.188 .000 -36.62 -12.66

4 -18.040* 4.188 .000 -30.02 -6.06

*. The mean difference is significant at the 0.05 level.Table 3. Post-hoc test in ANOVA. Differences in longevity between groups.

Correlations

longevity thorax

longevity Pearson Correlation 1 .636**

Sig. (2-tailed) .000

N 125 125

thorax Pearson Correlation .636** 1

Sig. (2-tailed) .000

N 125 125


Table 4. Pearson correlation with significant correlation

between thorax size and longevity.

There was significant correlation between the lifespan of the fruit fly and the thorax size (Pearson correlation r = 0.636, P <0.001, n = 125 male flies, table 4).

The percent of sleep in a day for males did not seem to have any significant correlation with their lifespan (Spearman rank correlation, r2 = -0.047, P > 0.5, n = 125, table 5).

Correlations

longevity sleep

Spearman's rho longevity Correlation Coefficient 1.000 -.047

Sig. (2-tailed) . .600

N 125 125

sleep Correlation Coefficient -.047 1.000

Sig. (2-tailed) .600 .

N 125 125

Table 5. Spearman correlation between sleep (% of day) and longevity (days) in male fruit flies.

E-Based on the results we obtained from various statistical analysis, we can conclude that

energy spent on mating does affect longevity. Though this model used fruit flies as an example, it could be potentially applied to other species as well. The analysis showed that there was significant lifespan differences in those males housed with virgin females, and we can conclude that the more virgin females which are present can reduce the male’s lifespan even more. The lifespan of the flies with no females present, one mated female present and eight females present all had about the same mean lifespan of 64 days. The average male lifespan, when eight virgin females were present was about 35 days.

Another factor which influenced the lifespan of the fly was his thorax size. After analysis, we concluded that the larger the male’s thorax (mm), the longer he lived. This is really interesting data; I would have not predicted in the beginning that there would be much difference in longevity dependent on thorax size.

The amount of sleep that the fly got in one day showed not to influence his lifespan. Analysis showed that there were many flies who slept a lot and died early, and a lot who received no sleep and died later.

In conclusion, there is evidence for life-history trade-offs in the case of reproductive behaviors and survival. This study showed us that when more time is spent on reproducing with virgin female fruit flies, longevity of the male will be diminished.

3) A-The human lifespan has a large influence on a large variety of factors. Meaning, there are many things in this world which may cause us to live short, medium, or long lives. Two major factors which play a role in our lifespan are availability of resources: specifically television sets and medical care doctors.

This study takes a look at these two factors, and whether they have some relationship in how long we live. Data was collected in 1990 for each of the 40 largest countries in the world. Data is given for life expectancy at birth (years) in the country, number of televisions per person in the country, and number of people per physician in the country. Male and female life expectancies are given as we would expect to see slight differences.

So, the question addressed in this study: Is there a relationship between life expectancy and the number of television per person, and is there a relationship between life expectancy between the number people per physician? We hypothesize that there is a difference in the lifespan of people in the case that there are more or less available TV’s and/or physicians. We predict to see reduced lifespans in instances where there are fewer television and fewer physicians per person.

B-Variable Distribution Central tendency Dispersion

Male life expectancy Normal Mean = 65.210 SD = 6.901Female life expectancy Not normal Median = 72.500 Range = 29.000TV per person Not normal Median = 0.159 Range = 0.768Doctor per person Not normal Median = 0.001 Range = 0.004


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Fig. 11. Male life expectancy (years) compared to how many people per doctor.

Fig. 12. Female life expectancy (years) compared to how many people per doctor.

Fig. 13. Female life expectancy (years) compared to how many televisions per person.

Fig. 14. Male life expectancy (years) compared to how many televisions per person.

D-Results show that for both males and females, life expectancy does have a relationship with how many televisions there were per person and how many people there were per doctor. Table 6 shows that all variables except “male life expectancy” had non-normal distribution.

Male life expectancy had a positive correlation with the number of people per doctor (Pearson correlation, r = 0.667, P < 0.001, n = 40, Table 7). Male life expectancy also had a positive correlation with the number of televisions per person (Pearson correlation, r = 0.748, P < 0.001, n = 38, Table 7). Female life expectancy had non-normal distribution, and did have a positive correlation with televisions (Spearman rank r2 = 0.759, P < 0.001, n = 38, Table 8) and also number of people per doctor (Spearman rank, r2 = 0.836, P < 0.001, n = 40, Table 8).

Correlations

MALE_LIFE_E

XP

DOC_PER_PE

RSON

TV_PER_PER

SON

MALE_LIFE_EXP Pearson Correlation 1 .667** .748**

Sig. (2-tailed) .000 .000

N 40 40 38

DOC_PER_PERSON Pearson Correlation .667** 1 .616**

Sig. (2-tailed) .000 .000

N 40 40 38

TV_PER_PERSON Pearson Correlation .748** .616** 1

Sig. (2-tailed) .000 .000

N 38 38 38


Table 7. Pearson correlation of male life expectancy (years) to the number of people per doctor and the number of televisions per person.

Correlations

DOC_PER_

PERSON

TV_PER_PE

RSON

FEMALE_LIF

E_EXP

Spearman's

rho

DOC_PER_PERS

ON

Correlation

Coefficient

1.000 .759** .836**

Sig. (2-tailed) . .000 .000

N 40 38 40

TV_PER_PERSON Correlation

Coefficient

.759** 1.000 .899**

Sig. (2-tailed) .000 . .000

N 38 38 38

FEMALE_LIFE_EX

P

Correlation

Coefficient

.836** .899** 1.000

Sig. (2-tailed) .000 .000 .

N 40 38 40


Table 8. Spearman rank correlation between female life expectancy (years) to the number of people per doctor and the number of televisions per person.

E-In conclusion, our findings support our predictions that with fewer number of television per person and fewer doctors per person, life expectancy goes down. The values given in this data set represent people picked from 40 of the largest countries in the world. We chose to simply group all of these people together and just make male and female comparisons, rather than attain results separately from the different countries.

Scatterplots of both male/female life expectancies and their relationships to the number of television and doctors per person were best used visually. In figures 11, 12, 13, 14, there are relationships which show that as there are more doctors and/or televisions per person, life expectancy increases. Our predictions for this are that the more doctors that there are available increase the amount of patients that can be treated. Also, the more television there are per person, may have to impact on the persons living in that countries intelligence. It is possible that a person can learn more through television about their health to improve their own life expectancy. Pearson and Spearman rank

correlation tests were used to confirm positive correlation between life expectancies and the other two variables.

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