Grade Girls Are Taller Than Grades Girls

2. Sort the data by Gen or Gen 1 (into males and females) and find the mean and standard deviation for each gender for the following variables: Use the descriptive stats function for one gender and the Fx functions (average and stdev) for the

The results are as follows: the mean age of the sexual minority youth was 16.24 years old, female sexual minority youth were

*these questions will vary depending on how the student decides to create the bar graph.

If the experiment could be done again, the subjects could all be boys' upper limb length and height or could all be girls’ upper limb length and height. Then, the student could see the difference between the girls and boys upper limb length and height. An error that could have happened is that

Both data sets contain outliers, as clearly shown in the box and whisker plots. The control group contains two outliers, both of which are 70. The experimental group contains four outliers: 0, 10, 40 and 70. In all calculations, the outliers have been omitted in order to ensure more accurate calculations. The median test score/center of the data for both the control group and experimental group is around 90 percent. The mean or average test score for the experimental group is 93.5 and for the control group, the average test score is an 89.3. The standard deviation of the group that chewed gum is 6.895 (about 7) and the group that did not chew gum had a standard deviation of 7.164 (about 7.2.) This shows that the experimental group has less variance in comparison to the control group. Without doing an actual hypothesis test, just based off of the graphs and raw data, we can guess that our original hypothesis (those who chew gum will have better test scores than those who do not) might be correct and the conclusion will be that we fail to reject the null hypothesis. Based on the dotplot and histogram, the assumption of normality is plausible for the control group but not for the experimental

Figure 2: Bar chart for mean change in body mass by the sex of the participant.

The difference between boys (35.5) and girls (64.5) was significant on the selection of goal to be popular. Likewise, the difference between selections of goals was significant between girls (33.3) and boys (66.7) on the option of being good in sports.

B. Once the calculations were completed in each category the mean turned out to be the same as before.

-Quantitative data were described as means (SD) or medians, as appropriate. They were tested for normality by Kolmogorov-Smirnov test. In the normally distributed variables, one way

An early unit testifies the Z score times the standard deviation divided by the square root of the sample size. Therefore, the Z score remains known as plus/minus of the level of confidence based on the data in the normal distribution. Through likeness, the standard deviation remains characterized as a typical distance of an observation from the distribution center or middle value (Barde & Barde, 2012). In other words, the diagram exhibits a standard deviation of 0.301 for male students and 0.287 for the female students. The results computed display a standard error of 0.04 overall (for both groups). Consequently, the diagram analysis offers a computing test statistic of -4.54. In addition, the direction of the test includes three different areas of study. The first exists as a two-tailed test, the second as the upper-tail test, and the last as the lower-tail test. The chart computes the two-tailed test lower critical value of -1.9600, the upper-tailed test not available and the lower-tail test yielding a -1.6449.

The standard deviation for the females was smaller than that of males, although not significant. This proposes that the number of songs for males are farther away from their mean and the females number of songs are closer to their mean in their spreads of distribution. Furthermore, the mean and standard deviation can show explicit results about symmetrical data, however, the histograms show the data more vividly symmetrical or skewed, in this case its roughly symmetrical.

Figure 2 uses a logarithmic scale to show the data collected in a way that can be visualized. If the graph does not have a logarithmic scale, most of the data bunches up onto the left side of the graph. This makes it very hard to see what the data is showing.

Our graphs show that the number of students for each semester is pretty even, but there is a slight difference since semester 10 displayed more students than 9, 12, and 11. Our total sample size was 812 students and there was an average of 10.46 with a standard deviation of 1.12 and standard error of 0.03912. The next variable we measured was the number of years in which each student has been attending the university and it varied from 1 to 5. A large majority of the students were in year 1 or 2, which skewed the data to the left. The average year was 1.58 with a standard deviation of 0.86 and the standard error of 0.03. For our third variable, we constructed a histogram for height (in.). Our graph almost represented a normal curve distribution since most of students are found in the range 60-75 inches tall. The average height of the students was 67.57 inches with a standard deviation of 4.30 inches, and a standard error that was as low as 0.15 inches. We also used their weight to construct a separate histogram, but they were a few blank spaces due to not everyone wanting to participate in this portion of the survey. Our graph shows that the data is skewed to the right due to a majority of the students in the range of 100-200 pounds, which seems relatively normal. The average weight of the students was 150.65 pounds with a standard deviation of 33.06 pounds which is quite large actually, since there was a variety of weight

6. What is seen in the histogram created for the heights of students in this class (include the shape)? Explain your answer.

6. What is seen in the histogram created for the heights of students in this class (include the shape)? Explain your answer.