Blog Post
Reflect on your growth and understanding of Data Analysis and Statistics at this point in the course. What has been new for you? What connections can you make to previous knowledge? What themes are emerging in the course? How are concepts connected to each other? To other areas of mathematics and the world around us? What growth do you see in your own mathematical thinking? What connections do you see to your teaching practice? What questions have are you still pondering? Compose a blog entry to share with at least 500 words. This piece should be more formal than a weekly reflection, with clear connections to the content of Data Analysis & Statistics. It should be specific to your perspectives in the course – your growth, your questions, big ideas and connections you see. It is a synthesis of your experiences in the past six weeks of the course, with an emphasis on your work in Weeks 3 – 6.
RUBRIC
DOCUMENTS TO USE….BELOW
· https://my.lesley.edu/images/ci/icons/generic_updown.gif Module Four: Quantifying Spread
https://my.lesley.edu/images/ci/icons/generic_updown.gif Module Five: Normal Distributions & Hypothesis Testing
· Module Six: Association of Quantitative Variables
· Thread:
· Baines-Initial Post
· Post:
· RE: Baines-Initial Post
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 8, 2014 11:29 PM
· Status:
· Published
· I agree with this because the higher the life expectancy the smaller the number of people per TV and thus there is a relationship between the number of people per TV and life expectancy. Life expectancy is affected by factors like health care, nutrition and hygiene and thus with low life expectancy must be developing countries and those that have high life expectancy are developed countries.
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Sparks-initial response
· Post:
· RE: Sparks-initial response
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 8, 2014 11:28 PM
· Status:
· Published
· I agree with this because countries with low life expectancy have low GDP which means most of the population is made of poor. Thus they are not able to afford TV thus that why there are large number of people per TV. Country like Yemen which has low life expectancy and small number of people per TV when removed the correlation coefficient increases towards the negative.
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· E5
· Post:
· RE: E5
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 8, 2014 11:26 PM
· Status:
· Published
· I agree with this because in this example population of a country was not put into consideration. High life expectancy was associated with developed countries while low life expectancy was associated with developing countries. Health facilities and care, food and economy should be put into consideration instead of TV.
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· DOW#6- Exercise B4- Stacy Ribolini
· Post:
· RE: DOW#6- Exercise B4- Stacy Ribolini
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 8, 2014 11:23 PM
· Status:
· Published
· I agree with this because the correlation coefficient was -0.8…. which is a strong negative. There is one cluster on the left where there is high life expectancy and small number of people per TV. There was one outliner which was for the country called Yemen; it had low life expectancy and small number of people per TV as well. After removing Yemen, the correlation coefficient increased towards the negative side and thus proving that it was an outliner.
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· B3 and B4 – Pam
· Post:
· RE: B3 and B4 – Pam
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 8, 2014 11:21 PM
· Status:
· Published
· Hi Pam,
· As I looked at your last section:
· As far as calculating a coefficient for DoW #6, I do not see where we were given instructions on how to do that. There are instructions later in the module. I will answer this in the general form that we have been working with so far. There is a negative correlation for DoW #6, but it is not strongly negative. On my scatter plot, the data seems to have two clusters. One is on the left where the life expectancy is high (in the 70’s) and the People/TV is low (in the single digits). The other is on the right where the life expectancy is low and the People/TV is high. I did not see any outliers.
· I disagree with this because the correlation coefficient was -0.8…. which is a strong negative. There is one cluster on the left where there is high life expectancy and small number of people per TV. There was one outliner which was for the country called Yemen; it had low life expectancy and small number of people per TV as well.
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Sparks-initial response-B3
· Post:
· RE: Sparks-initial response-B3
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 8, 2014 11:16 PM
· Status:
· Published
· I agree with this. As the correlation coefficient value becomes closer to 1 it becomes stronger because scattered data points show that the correlation is weak.
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· E5
· Post:
· E5
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 5, 2014 10:43 PM
· Status:
· Published
· Yes, there is a relationship because the countries with higher life expectancy have few people per Tv compared to the countries with low life expectancy.
· There is a negative correlation between the life expectancy and the number of people per TV in the given countries.
· This is because there is a higher number of people per TV in the countries with lower life expectancy. Could the country’s population affect the number of people per TV?
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
· There is a negative correction coefficient from this data. Countries with low life expectancy have more people per TV.
· From this data the developed countries have a higher life expectancy compared to the developing countries.
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
·
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· B4–Nerlande
· Post:
· B4–Nerlande
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 5, 2014 10:35 PM
· Status:
· Published
· -0.80380974
· the correlation coefficient is a negative value which justifies our conclusion that the higher the life expectancy the lower the number of people per TV.
·
·
·
·
·
·
·
·
·
·
·
·
·
· -0.85729528
· the value of correlation coefficient changes slightly to a stronger negative value after excluding the outlier.
·
·
·
·
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Bobby’s Scores
· Post:
· RE: Bobby’s Scores
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 5, 2014 11:38 AM
· Status:
· Published
· Hi Catherine,
· I basically came up with the same thing. I had a graph that could not be uploaded.
· -3 σ
· -2 σ
· -1 σ
· μ
· 1 σ
· 2 σ
· 3 σ
· 374
· 548
· 722
· 896
· 1070
· 1244
· 1418
· Ideally his score is slightly above 1 σ from the μ. The probability that somebody scores better than him is about 16% representing the 13.5% plus 2.5% of the data that lies above slightly more than 1 σ of the μ.
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Bobby’s Scores- Stacy Ribolini
· Post:
· RE: Bobby’s Scores- Stacy Ribolini
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 5, 2014 11:34 AM
· Status:
· Published
· Hi Stacy,
· 1. Bobby’s ACT score is actually located approximately 2 σ to the right of the μ. His SAT score is however slightly above 1 σ above the μ. The further right (above the μ) his score is the more gets towards the top of the class. The people who performed better than him in the ACT are approximately 2.5% of the class by the Three-Sigma Rule implying he did better than approximately more than 97.5% of his classmates. As for he did better than approximately 85% of the students with whom he took the SAT test. His ACT score is thus better than his SAT and should thus send it. Nerlande
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Week 5: SAT Test Discussion
· Post:
· RE: Week 5: SAT Test Discussion
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 5, 2014 11:31 AM
· Status:
· Published
· Hi Nitha,
· 1. In the face of the available information I think there is no sufficient evidence that SAT prep improves SAT scores. The random sample of 50 students may of be sufficient to point at the characteristics of the entire population. But, based on the plots containing the means collected from 355 samples of 50 SAT scores, it points at an unlikelihood that no students would score 1000 yet we know there are those who scored above this. Nerlande
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Week 5: SAT Test Discussion
· Post:
· RE: Week 5: SAT Test Discussion
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 5, 2014 11:29 AM
· Status:
· Published
· Hi Catherine,
· 1. I do agree with you there, from the graph of the means from 355 samples of 50SAT scores, there is a high unlikelihood that 50 randomly selected students could average 1000. Actually, none of the samples hits this score. With the given probability of 0.00118%, the SAT program is significantly effective.
· Nerlande
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Week 5: SAT Test Discussion
· Post:
· RE: Week 5: SAT Test Discussion–Nerlande
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 5, 2014 8:27 AM
· Status:
· Published
· In the face of the available information I think there is no sufficient evidence that SAT prep improves SAT scores. The random sample of 50 students may of be sufficient to point at the characteristics of the entire population. But, based on the plots containing the means collected from 355 samples of 50 SAT scores, it points at an unlikelihood that no students would score 1000 yet we know there are those who scored above this.
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· C3
· Post:
· RE: C3
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· August 4, 2014 8:54 AM
· Status:
· Published
· Sorry guys, it has been an hectic back to school, I hope this clarifies my explanation.
· Exercise C3: Bobby’s Test Scores: In DoW #5
· For SAT:
· The first half of the normal distribution is 50% and is corresponding to 896, one standard deviation to the right of this is 34% away which is 1070 which is still less than what Bobby could have scored. This is 10(1080-1070) scores less than what Bobby scored. 1080 falls in the 2-standard deviation (which represent 13.5%) but its exact position assuming symmetry of the scores is given by:
· 50% + 34% + = 84.78%
· For ACT
· The first half of the normal distribution is 50% and is corresponding to 20.6, one standard deviation to the right of this is 34% away which is 25.8 which is still less than what Bobby could have scored. This is 4.2(30-25.8) scores less than what Bobby scored. 30 falls in the 2-standard deviation (which represent 13.5%) but its exact position assuming symmetry of the scores is given by:
· 50% + 34% + (30-25.8) / 5.2 * 13.5% = 94.91%
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Week 5: SAT Test Discussion
· Post:
· RE: Week 5: SAT Test Discussion–Nerlande
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 29, 2014 2:39 PM
· Status:
· Published
· I added E2 because that is why I ended up with E3. I could not paste my pic for E2.
· Exercise E2:Complete the Central Limit Theorem
· PART I
· The data is randomly distributed and seem to have a large standard deviation. Again, there is no symmetry of data on either side of the mean.
· PART II
· The distribution of the sample means of the SAT scores are considerably near normal. Compared with the distribution in Part I, this one are concentrated around the mean, 896. There are no large deviations from this value and the data to the right of the mean looks quite similar in number to those on the left side.
· The mean of the sample mean is 895.587 and the standard deviation is 24.80.
· The mean is statistically the same as the population mean. The standard deviation is smaller than that of the population. The standard deviation is unique to the sample.
· The mean score of 1000 would fall on the red bold line on the graph as shown below.
· The result that the sample of the 50 students has mean scores of 1000 is very likely. This is likely if the sample selection was biased in favor of those with better performances. But the mean of 1000 is not statistically different from 896.
· Exercise E3: In Exercise E2
· Yes I think there is sufficient evidence.
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· C3
· Post:
· C3
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 29, 2014 2:29 PM
· Status:
· Published
· Exercise C3: Bobby’s Test Scores: In DoW #5
· For SAT:
· 50% + 34% + = 84.78%
· For ACT
· 50% + 34% + (30-25.8) / 5.2 * 13.5% = 94.91%
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· E3 – Pam
· Post:
· RE: E3 – Pam
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 29, 2014 2:19 PM
· Status:
· Published
· Hi Pam,
· Great explanation, I love the details you provided. A lot of your explanations make sense to me.
· Nerlande
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Activity E4
· Post:
· RE: Activity E4
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 29, 2014 2:17 PM
· Status:
· Published
· Hi Richard,
· I think class C was pretty understandable. Great job on your explanation.
· Nerlande
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Cummins – Variation
· Post:
· RE: Cummins – Variation
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 28, 2014 12:05 AM
· Status:
· Published
· Hi Cummins,
· I agree with your review but I am left with one particular question which is how did you come up with the conclusion that a small range and IQR means that the data is very concentrated around the center. I can fully understand how the large range and IQR mathe data more spread out.
· Nerlande
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· A6
· Post:
· RE: A6
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 28, 2014 12:00 AM
· Status:
· Published
· Hi Christine,
· I also thought that Class I shows the highest variation and Class C shows the least variation. It was hard for to determine the least variance even when I ended up with C.
· Nerlande
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Mean and Mads
· Post:
· RE: Mean and Mads
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 27, 2014 11:43 PM
· Status:
· Published
· Hi Nitha,
· Great review on the article. MAD really got my attention and I also wonder the same question as you and hope Mr. Deeter will probably dosome clarification on that.
· Nerlande
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Deviation – Cummins
· Post:
· RE: Deviation – Cummins
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 27, 2014 11:37 PM
· Status:
· Published
· Hi Cummins,
· This is my first year in middle school b ut not teaching Math. I can see why the six graders have to be exposed to it and really do the concept in 7th grade. I also do believe technology makes a lot of things easier.
· Nerlande
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Deviations and MAD
· Post:
· Deviations and MAD
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 25, 2014 9:13 AM
· Status:
· Published
· These concepts are not new to me. I have not worked with them though but I am of the apprehension that they are applied when analyzing the course results and many other statistical applications. These concepts are important to my application as a student since in handling data it is important to be of the understanding of how they are distributed especially when equity is the objective of the distribution.
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· E3
· Post:
· E3
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 23, 2014 11:45 PM
· Status:
· Published
· The answer is most clear for Class C, Class G and Class H. The less the Range and IQR, the more clearly the question can be answered.
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· A6—-NERLANDE
· Post:
· A6—-NERLANDE
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 21, 2014 7:15 AM
· Edited Date:
· July 23, 2014 8:20 PM
· Status:
· Published
· Class I shows the highest variation and Class C shows the least variation.
· For Class F, Class G and Class H it is less clear as to how their variation compares with each other and that to the other classes.
· Range and Histogram best allows to see the variation in the data set.
· The range doesn’t tell us about how the middle values of the data are distributed. The IQR doesn’t tell us about the extreme values of the data.
· We need to calculate variance and standard deviation for better understanding of the spread of the data in DoW #4.
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Week 3 Research Draft
· Post:
· RE: Week 3 Research Draft
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 21, 2014 12:05 AM
· Status:
· Published
· Hi Clayton,
· I am not a dancer nor I was never one, but I can tell you right now that your research will be interesting to read. Also, I thing you might be able to check on past and current articles. Try to find retired dancers, you will be surprised to the amount of info you will find.
· Nerlande
· Tags: None
· Reply Quote Mark as Unread
·
· Thread:
· Week 3 Research Draft
· Post:
· RE: Week 3 Research Draft
· Author:
· Access the profile card for user: Nerlande Monfort https://my.lesley.edu/images/ci/ng/avatar_150.gif Nerlande Monfort
· Posted Date:
· July 21, 2014 12:01 AM
· Status:
· Published
· Hi Nitha,
· Your plan is well done. Your strategy for assessment using a scoring system will be very effective.
· nerlande
· Tags: None
· Reply Quote Mark as Unread
· Select: All None
· List Actions
· Mark (Click to see options)
· Read
· Unread
·
· OK
· Logger
·
discussion_board
NERLANDE MONF
forum
course