10. What is the best point estimate of the GWA of all senior high school students?
A. 85 B. 87 C. 88 D. 89
11. At 99% confidence level, what is the confidence coefficient to be used?
  A. 1.282 B. 1.645 C. 1.96 D. 2.575
12. At 99% confidence level, what is the estimated standard error of the mean? (2 points)
  A. 0.2521 B. 0.2551 C. 0.2567 D. 0.2732
13. At 99% confidence level, what is the margin of error to estimate the GWA of all senior
high school students? (2 points)
  A. 0.3291 B. 0.4223 C. 0.6610 D. 0.6613
14. At 99% confidence level, what is the interval estimate of the GWA of all senior high
school students?
  A. 87.3556 - 88.6776 C. 87.5777 - 88.4223
  B. 87.4969 - 88.5031 D. 87.6709 - 88.3291
15. At 95% confidence level, what is the confidence coefficient to be used?
  A. 1.282 B. 1.645 C. 1.96 D. 2.576
16. At 95% confidence level, what is the estimated standard error? (2 points)
  A. 0.2521 B. 0.2551 C. 0.2567 D. 0.2732
17. At 95% confidence level, what is the margin of error to estimate the GWA of all senior
high school students? (2 points)
  A. 0.3291 B. 0.4223 C. 0.5031 D. 0.6613
18. At 95% confidence level, what is the interval estimate of the GWA of all senior high
school students?
  A. 87.3387 - 88.6613 C. 87.5777 - 88.4223
  B. 87.5135 - 88.5197 D. 87.6709 - 88.3291
For items 19-21, In preparation for the next school year, the school administrator wants to
estimate the GWA. If they want to estimate the GWA accurate within an error of 0.75 from
its true value,
19. What is the estimated population standard deviation?
  A. 2.93 B. 3.33 C. 3.43 D. 3.45
20. What should be the sample size at 99% confidence level? (2 points)
  A. 141 B. 200 D. 375 D. 425
21. What should be the sample size at 95% confidence level? (2 points)
  A. 65 B. 82 D. 92 D. 104

        Students experience tremendous levels of stress throughout their education. A high level of stress may have a negative effect on the cognitive functioning and learning of students. The school administration of CSU - Laboratory High School - Senior High (CSU-LHS-SH) sought to determine the levels and primary sources of stress and its possible correlation with the General Weighted Average (GWA) in its department. As a statistician, your task is to conduct hypothesis testing to analyze the gathered data. Data were collected from students of CSU-LHS-SH during the first semester of the academic year 2021 - 2022.. The Student Stressor Questionnaire (SSQ) was used to collect the data.
            The SSQ contains 20 items that cover six subscales: (Academic-Related Stressors (ARS), Intrapersonal and Interpersonal Related Stressors (IRS), Learning-Related Stressors (LRS), Social-Related Stressors (SRS), Drive- and Desire-Related Stressors (DRS), and Group Activity-Related Stressors (GARS). Items in the SSQ represent 20 events that might be sources of stress (eg, “lack of time to review what has been learnt”, “heavy workload”). The responses were measured on a four-point Likert scale: “0=causing no stress”, “1=causing mild stress”, “2=causing high stress”, or “3=causing severe stress”.
            The findings of this study will contribute to the educational literature on the effect of stress on students’ academic performance, thus it must be accurate and timely. No. STRAND MODALITY GWA ARS IRS LRS SRS DRS GARS 1 ABM OBL 90 1 0 1 0 0 2 2 STEM OBL 90 2 0 2 1 0 1 3 STEM CBL 87 1 1 1 1 2 2 4 STEM OBL 90 1 0 1 0 1 1 5 STEM OBL 91 1 1 0 0 2 1 6 ABM CBL 90 1 0 1 1 0 1 7 STEM OBL 94 2 1 1 0 0 1 8 STEM OBL 93 2 0 2 1 0 1 9 ABM OBL 89 1 0 1 0 1 1 10 STEM CBL 87 2 2 1 1 2 2 11 ABM CBL 87 2 1 1 1 1 2 12 ABM OBL 89 2 0 1 0 0 2 13 ABM OBL 89 1 0 1 2 1 1 14 ABM CBL 83 3 2 3 2 3 3 15 STEM OBL 95 0 0 0 0 0 1 16 HUMSS OBL 90 1 0 1 1 0 1 17 HUMSS OBL 89 1 1 1 0 0 3 18 HUMSS OBL 89 1 0 1 1 1 1 19 STEM OBL 89 0 0 1 1 2 2 20 HUMSS CBL 90 1 0 1 2 0 1 21 HUMSS OBL 92 0 0 0 0 1 2 22 HUMSS CBL 84 3 2 2 1 3 2 23 HUMSS CBL 86 2 2 2 2 2 2 24 HUMSS OBL 90 1 0 1 0 1 1 25 STEM CBL 93 1 0 0 0 0 2 26 STEM CBL 95 1 0 1 0 1 2 27 STEM CBL 87 3 1 3 1 2 1 28 STEM OBL 94 0 1 0 1 0 0 29 HUMSS OBL 85 3 1 2 2 2 3 30 STEM OBL 88 2 0 1 0 1 3 31 ABM CBL 80 3 2 3 3 3 3 32 STEM OBL 88 3 0 1 0 1 2 33 HUMSS CBL 83 3 2 3 2 3 2 34 STEM OBL 88 2 1 1 1 1 2 35 ABM OBL 88 2 0 1 0 1 2 36 STEM CBL 85 3 1 3 2 2 2 37 STEM CBL 80 3 3 3 3 3 3 38 STEM CBL 82 3 3 3 2 3 3 39 STEM OBL 87 3 1 1 1 3 2 40 STEM OBL 88 2 0 1 0 0 2 41 STEM CBL 86 1 1 1 2 2 2 42 STEM OBL 92 1 1 0 1 0 1 43 HUMSS CBL 87 2 2 1 0 2 3 44 ABM OBL 90 2 1 0 0 1 1 45 STEM OBL 93 0 1 0 0 1 1 46 STEM OBL 92 1 1 0 0 0 2 47 STEM CBL 84 2 2 2 2 3 3 48 STEM CBL 84 3 3 2 2 3 3 49 ABM OBL 85 3 1 2 2 2 2 50 ABM CBL 86 2 1 1 2 2 2 51 STEM OBL 91 0 0 1 0 0 3 52 HUMSS CBL 82 3 3 3 3 3 3 53 HUMSS CBL 80 3 3 3 3 3 3 54 ABM OBL 85 3 1 2 2 2 2 55 STEM OBL 88 2 0 2 0 1 1 56 STEM OBL 87 2 1 1 1 2 1 57 STEM CBL 84 3 2 3 3 2 2 58 STEM OBL 91 1 0 0 1 0 3 59 ABM CBL 81 3 3 3 2 2 3 60 ABM OBL 90 1 0 1 0 0 1 61 ABM OBL 87 2 1 1 0 2 2 62 ABM OBL 87 2 1 1 1 2 2 63 STEM OBL 85 3 1 2 2 2 2 64 STEM OBL 86 2 1 1 2 2 2 65 ABM OBL 86 2 1 3 2 2 2 66 ABM OBL 93 0 0 1 0 0 1 67 STEM CBL 87 2 0 1 1 2 2 68 STEM OBL 88 1 2 1 2 1 3 69 ABM OBL 89 0 1 2 0 0 1 70 ABM OBL 89 1 0 1 0 1 1 71 STEM OBL 91 2 0 2 0 0 1 72 STEM CBL 85 1 1 2 2 2 3 73 HUMSS OBL 93 1 0 2 0 0 1 74 STEM CBL 89 1 0 1 0 1 2 75 STEM OBL 86 2 1 1 2 2 2 76 STEM OBL 85 3 1 3 2 2 3 77 HUMSS OBL 85 2 1 2 2 3 2 78 STEM OBL 91 1 0 0 0 1 1 79 STEM OBL 86 2 1 1 2 2 2 80 STEM CBL 83 2 1 3 2 3 3 81 STEM OBL 94 0 1 0 0 0 1 82 ABM CBL 82 3 2 3 2 3 3 83 STEM OBL 89 1 1 1 0 1 1 84 ABM OBL 86 2 2 1 1 2 2 85 STEM OBL 87 2 1 2 0 2 2 86 STEM CBL 83 3 2 3 2 3 3 87 STEM OBL 85 2 1 2 2 2 2 88 STEM OBL 88 2 0 1 0 1 2 89 STEM CBL 85 3 2 2 2 2 2 90 STEM OBL 88 2 0 1 0 1 2 91 ABM OBL 94 0 1 0 1 0 1 92 ABM OBL 85 3 0 1 2 2 2 93 ABM CBL 88 2 0 2 0 1 2 94 STEM CBL 86 2 1 1 2 2 2 95 STEM OBL 85 3 2 2 1 3 2 96 STEM OBL 86 2 2 1 2 2 2 97 STEM OBL 85 3 1 2 2 2 2 98 STEM OBL 92 1 0 1 0 0 2 99 ABM CBL 90 0 1 2 0 1 3 100 STEM CBL 81 3 3 3 2 3 3 101 HUMSS OBL 89 1 0 2 0 1 1 102 STEM CBL 88 3 0 1 0 1 0 103 HUMSS CBL 85 3 2 3 2 2 2 104 STEM OBL 89 0 0 2 0 1 1 105 STEM OBL 87 2 2 1 1 1 3 106 STEM OBL 89 1 0 1 1 1 1 107 STEM OBL 95 1 0 0 0 1 0 108 HUMSS CBL 84 3 2 2 2 3 3 109 STEM CBL 81 3 3 3 2 2 1 110 STEM OBL 86 2 1 0 2 2 3 111 STEM OBL 89 1 1 1 0 1 1 112 STEM OBL 91 1 0 0 0 0 1 113 STEM OBL 91 1 1 0 0 0 1 114 STEM CBL 89 1 1 0 0 1 1 115 STEM CBL 83 3 2 3 2 3 3 116 STEM OBL 91 3 0 0 0 0 1 117 STEM OBL 95 1 0 1 0 0 1 118 STEM OBL 87 1 1 1 2 2 2 119 STEM OBL 87 2 1 1 1 2 2 120 STEM OBL 89 0 0 1 0 1 3 121 STEM OBL 88 1 0 0 1 1 2 122 STEM OBL 91 1 0 0 1 0 2 123 STEM OBL 92 0 0 0 1 0 3 124 ABM CBL 84 3 2 2 2 3 3 125 STEM OBL 84 3 3 2 2 3 3 126 STEM CBL 83 3 1 3 2 3 3 127 STEM CBL 88 2 0 1 0 1 2 128 STEM OBL 92 1 0 0 1 1 2 129 STEM OBL 85 3 1 2 2 2 2 130 STEM CBL 87 2 1 0 1 2 2 131 STEM OBL 91 0 0 2 0 1 1 132 STEM OBL 88 2 1 0 0 1 3 133 STEM OBL 89 1 0 1 0 1 1 134 STEM CBL 88 2 2 1 2 1 2 135 STEM CBL 82 3 2 3 3 3 3 136 STEM OBL 88 2 0 1 0 1 2 137 HUMSS OBL 89 1 0 1 0 1 2 138 STEM OBL 92 1 0 0 0 0 1 139 ABM OBL 92 0 1 1 0 0 1 140 STEM OBL 92 0 0 1 0 1 1 141 ABM OBL 84 3 2 2 2 3 3 142 ABM OBL 88 1 0 2 0 0 3 143 HUMSS OBL 88 2 1 1 1 1 1 144 STEM OBL 90 1 0 1 0 0 1 145 HUMSS OBL 90 0 1 1 0 0 2 146 HUMSS OBL 90 1 0 1 0 0 1 147 STEM OBL 89 1 0 1 0 1 1 148 HUMSS OBL 94 0 0 0 1 0 2 149 STEM OBL 86 2 1 1 2 2 2 150 STEM OBL 90 0 1 1 0 1 2 151 ABM OBL 86 3 1 1 2 2 2 152 STEM OBL 86 3 1 1 2 2 2 153 STEM OBL 92 0 1 0 0 0 2 154 STEM OBL 90 1 0 2 1 0 1 155 STEM OBL 92 1 1 1 1 1 1 156 HUMSS OBL 86 3 1 2 2 2 3 157 STEM OBL 93 1 0 1 0 0 2 158 ABM OBL 86 2 1 1 2 2 2 159 ABM OBL 91 2 2 0 0 0 1 160 ABM OBL 93 2 0 1 0 0 1 161 ABM OBL 87 1 1 1 2 1 3 162 ABM OBL 87 2 0 1 1 2 2 163 ABM OBL 95 0 1 1 0 0 2 164 ABM OBL 86 2 1 1 2 2 2 165 STEM OBL 91 1 0 0 0 0 1 166 ABM OBL 87 2 1 1 1 2 1 167 ABM OBL 88 2 0 1 0 1 2 168 ABM OBL 93 0 1 0 1 0 2 169 HUMSS OBL 87 2 1 0 1 2 2 170 HUMSS OBL 84 3 2 2 2 3 3 171 HUMSS OBL 93 1 1 0 0 0 1 172 STEM OBL 84 3 2 2 2 3 3 173 STEM OBL 92 1 1 0 0 0 2 174 ABM OBL 83 3 2 3 2 3 3 175 HUMSS OBL 94 1 0 0 0 1 1 176 ABM OBL 91 1 0 1 0 0 1 177 HUMSS OBL 85 3 1 2 1 2 2 178 ABM OBL 86 2 1 1 2 1 2 179 STEM OBL 90 1 0 1 0 2 2 180 STEM OBL 84 3 2 2 1 3 3 181 STEM OBL 88 2 0 1 1 2 2

namjaaaa

I will pay someone to help me finish this project

TO START: First, select a classification methodology to learn in your project. Select either Classification Trees or Support Vector Machines. Those topics are covered in the following chapters in the ISLR text:

(a) Chapter 8, pages, 303-331 covers Tree-Based Methods (or CART, short for classification and regression trees). Only show results for classification trees, not regression trees.

(b) Chapter 9, pages, 337-368 covers SVM (“support vector machines”);

In terms of coverage, if you pick Chapter 8, Tree-Based Methods, get through boosting.

In Chapter 9, SVM, get through the section on support vector machines.

Appendix. After making your choice from above, get introduced to it by walking through the lab example provided in the ISLR text. Show that work in an Appendix, including any graphs. On those graphs, insert your name into the titles. To actually get the data used in the ISLR examples, you will likely need to download an R package called ISLR; it contains the data sets used in the text.

[20 points] Begin by introducing your reader to the corporation from which your stock data comes. Tell the reader something you learned about that corporation that you found interesting, something which would demonstrate to a recruiter that you possess curiosity and the ability to employ it.

Explain to the reader why we took two different transformations of the price data, return and risk. Illustrate your discussion with before-and-after graphics. Review LN2.A and your LN2 homework as a refresher.

Then, using trimmed screenshots where needed from Excel, sketch out for the reader how you converted your Yahoo-sourced stock data into lagged stock risk data set since 2006.

[20] First, draw a random sample of size n=300 without replacement from your stock return data set. Recall that your stock return data contains a HiLo return column and standardized log lag1 and log lag2 return columns. I will call this your n=300 stock return data set. Show and explain how this is done.

Second, draw another random sample of size 300 without replacement from your stock return data set. Recall that your stock risk data contains a HiLo risk column and standardized log lag1 and log lag2 columns. I will call this your n=300 stock risk data set.

Next, using your n=300 stock return data set, walk through the steps covered by the ISLR text for your chosen method, SVM or CART, explaining in your own words what you are doing. Put your name into the title of any graphs you show. Where you are unclear as to what is happening or why it is being done, say so, and document your efforts to work towards understanding. Do not waste time trying to fake comprehension via plagiarism.

You are welcome to read up on from other sources concerning your chosen method, including from textbooks, academic articles, and the web. However, carefully credit the sources from which you gain insight, and do not plagiarize them! It is natural that you will not understand much of what you read, but you can start wrestling with it and you can document that wrestling.

Finally, run the program on your n=300 stock risk data set and compare the performance to that of your n=300 stock return data set. Include use of the chi-square test. Discuss the differences, the reasons that these would happen, and the lessons learned about the nature of the stock market.

[20] Select one of the tuning parameters or decision criteria that lie beneath the surface of your chosen methodology, CART or CART. Engage with it by researching beyond the ISLR text. Then experiment with it. Experiment with your data and with other data sets. Try decreasing or increasing n. Look at other sources for help, documenting the sources. When borrowing text, use quotation marks and footnote the source. Do not plagiarize text or graphics.

Here are some examples of possibilities from Chapter 8 on CART. On page 312, the Gini index is defined, but what is it? Can you compute it yourself? How is the G statistic used after it is computed? What is it compared against? Is that a parameter that you can tune? Also, at the top of that page, the text says that the "...classification error rate is not sufficiently sensitive." See if you can demonstrate a lack of sensitivity! See if you can figure out what is meant in this context by "sensitive?" To what is it not sensitive? The text also says that entropy is an alternative to Gini and gives similar results. Do you find that to be true?

Here is an example from Chapter 9 on SVM. On page 346, a parameter denoted C is introduced, but what does it do? Demonstrate that you worked diligently on this problem: the goal is to engage in with the issue, not to produce miracles of comprehension or a plagiarism dump! Take a hands-on, practical approach: that requires experimentation.

[20] Create classification space plots for both of your n=300 data sets, using your chosen methodology, SVM or CART. Be sure to explain how you went about this. Create the plot using the same techniques that we did in our plots for other methods. Work it out yourself, step by step.

If you are doing SVM, you will find that the SVM software automatically outputs classification space plots. Do not show me those plots, as they will count ZERO on this assignment. I am requiring that you create a classification space yourself, step by step. As we know from trying to do that ourselves, it is not easy. Evidence of thoughtful and diligent work is more important than getting your plots to work perfectly.

[20] Prepare a comparative study of knn, naive Bayes, logistic regression, and your selected method. Make this comparison on your two n=300 data sets, splitting the data randomly in half to get the training and testing sets.

Explain what you are doing as you go along, explain what you understand about what distinguishes the methods, discuss reasons why the results vary, and why there might be systematic differences in performance between return data and risk data. Show classification space plots for knn, naïve Bayes, and logistic regression. At the end, show a single table in which you summarize the overall correct forecast rate for the stock returns for the four methods; then another table summarizing the performance on the stock risk data.

tricia

One marketing strategy used by several companies is to send personalized emails or newsletters to their customers to encourage them to buy their product. However, the opening rate of these emails is very low when the targeted customers do not feel concerned by the email. This article 1 (https://www.crop.ca/en/blog/2019/258/) will allow us to answer the following questions. Here’s an excerpt from the article :

« A quick Google search for email and newsletter opening rates leads to dozens of sources, with rates ranging from 15% to 45%. Some even claim that opening rates higher than 25% are a resounding success. But seriously, can brands afford such poor performances ? And most importantly, why are opening rates so low ?

We asked the main stakeholders : consumers. 2,189 Canadians were ques- tioned about their attitudes and behaviours regarding branded newsletters and emails. The results follow. Of all branded communications received personally, Canadians report an average opening rate of 45%. »

(a) Do the results from the survey in the article in reference allow us to affirm that on average, Canadian consumers open more than 25 % of business communications sent by email and newsletters ? Suppose that the standard deviation in the population is 20 %. Use a significance level of α = 0, 05.

(b) From what average sample rate do we reject the null hypothesis of the test carried out in (a) ?

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