WEIGHT IAT VARIABLES FOR QUESTION 3Quantitative Variable IAT-Weight-Score: Score on the Weight IATCategorical Variable (choose one) Birth-Gender: 1=Male; 2=Female OR Ethnicity: 1=Hispanic or Latinx; 2=Not Hispanic or Latinx (ignore 3=unknown)Weight IAT variable descriptions (opens in a new tab).QUESTION 1Which IAT data set are you working with?Use StatCrunch and your unique IAT sample to estimate the mean age for the population of IAT participants at the 90% confidence level. Copy the contents of your StatCrunch output window and paste it into your response. (No need to type anything here - just copy and paste the contents of your StatCrunch output window.)Are conditions met to estimate the mean age for the population of IAT participants? (Be sure to support your answer as demonstrated throughout Unit 8.)State the confidence interval, and then interpret the confidence interval in context.What is the margin of error (MOE)? Which would be more accurate, the 90% confidence interval you found or an 85% confidence interval? Briefly explain. Which would be more precise? Briefly explain.QUESTION 2At the end of 2020, the mean age of the U.S. population was estimated to be 38.5 years old. You are tasked with determining whether the mean age of the population of IAT participants (for your chosen IAT data set) differs from the mean age of the U.S. population.Let be the mean age of the population of IAT participants for your chosen IAT data set. State the hypotheses (symbolically and in words). Include a clear description of the populations and the variable.Use StatCrunch to create a histogram for the age distribution in your unique IAT sample. Download the StatCrunch output window and embed your histogram with your response. Can we safely use the t-test with your IAT sample? Explain.Perform the t-test using StatCrunch. Copy the information from the StatCrunch output window and paste it into your response.Based on the P-value, state your conclusions in context. Use a 5% level of significance. (NOTE: Since StatCrunch correctly calculates the P-value for either a one-tailed or a two-tailed test, you do not need to change the StatCrunch P-value.) Using the context of this scenario, explain the meaning of each of following items from the StatCrunch output for the hypothesis test. Std. Err (standard error) T-stat (T-score) P-valueQUESTION 3PREPARE TO WORK THROUGH QUESTION 3See the Variables for Question 3 Only section above to learn which variables to use for your chosen IAT data set.Choose only one of the categorical variables listed for your chosen IAT data set and note the two groups for your chosen categorical variable.Here is the question to be investigated for the two groups in your chosen categorical variable. Do the population of IAT participants in one group score higher on the IAT test than the population of IAT participants in the other group? TO ANSWER THE QUESTION WE ARE INVESTIGATING, RESPOND TO EACH OF THE FOLLOWINGUse StatCrunch to determine the sample size and sample mean, , for the two groups in your chosen categorical variable (directions).Copy your StatCrunch output table and paste into your response. Then relabel the categorical variable groups with the appropriate word(s), i.e. replace the group numbers with the appropriate word(s) to describe the group (see the Categorical Variable section above for your data set).Which of the two groups has the smallest sample mean? Which has the largest sample mean? Let 𝜇1μ1 be the population mean for the group with the largest sample mean. Let 𝜇2μ2 be the population mean for the group with the smallest sample mean. State the null and alternative hypotheses (symbolically and in words). Include a clear description of the populations and the variable. To test your hypotheses, is it appropriate to use the two-sample T-test? If so, explain why and provide any supporting evidence. If not, start over and choose the other categorical variable listed with your chosen IAT data set.WARNING: If it is not appropriate to use the two-sample T-test for either of the categorical variables listed with your IAT data set, something may be wrong. Stop and contact me as soon as possible.Proceed only if it is appropriate to use the two-sample T-test. Use StatCrunch to carry out the test. (directions) Copy the content in the StatCrunch output window (text and the table) and paste it into the textbox as your response to this item. Use a 5% significance level to state a conclusion. Be sure to state your conclusion in context and in terms of what a “more positive score” on the IAT means for your data set. (Hint: in the variables section above, click on the variable descriptions link for your data set. The variable descriptions page opens in a new window. Review the Interpreting the IAT section at the top of that page.)

Part I: For this part, you need to explore the bank data (bankdata_csv_all.csv), available in attachments, and an accompanying description (bankdataDescription.doc) of the attributes and their values.The dataset contains attributes on each person’s demographics and banking information in order to determine they will want to obtain the new PEP (Personal Equity Plan). Your goal is to perform Association Rule discovery on the dataset using R. First perform the necessary preprocessing steps required for association rule mining, specifically the id field needs to be removed and a number of numeric fields need discretization or otherwise converted to nominal. Next, set PEP as the right hand side of the rules, and see what rules are generated. Select the top 5 most “interesting” rules and for each specify the following: Support, Confidence and Lift values An explanation of the pattern and why you believe it is interesting based on the business objectives of the company. Any recommendations based on the discovered rule that might help the company to better understand behavior of its customers or to develop a business opportunity. Note that the top 5 most interesting rules are most likely not the top 5 in the strong rules.They are rules, that in addition to having high lift and confidence, also provide some non-trivial, actionable knowledge based on underlying business objectives. To complete this assignment, write a short report describing your association rule mining process and the resulting 5 interesting rules, each with their three items of explanation and recommendations.For at least one of the rules, discuss the support, confidence and lift values and how they are interpreted in this data set. You should write your answers as if you are working for a client who knows little about data mining. Your report should give your client some insightful and reliable suggestions on what kinds of potential buyers your client should contact, and convince your client that your suggestions are reliable based on the evidence gathered from your experiment results. In more detail, your answers should include: Description of preprocessing steps Description of parameters and experiments in order to obtain strong rules Give the top 5 most interesting rules and the 3 items listed above for each rule. Part II: In this part of homework, you are expected to Apply decision tree induction tree algorithm to solve a mystery in history: who wrote the disputed essays, Hamilton or Madison? About the Federalist Papers About the disputed authorshipComputational approach for authorship attribution Quote from the Library of Congress http://www.loc.gov/rr/program/bib/ourdocs/federalist.html The Federalist Papers were a series of eighty-five essays urging the citizens of New York to ratify the new United States Constitution. Written by Alexander Hamilton, James Madison, and John Jay, the essays originally appeared anonymously in New York newspapers in 1787 and 1788 under the pen name "Publius." A bound edition of the essays was first published in 1788, but it was not until the 1818 edition published by the printer Jacob Gideon that the authors of each essay were identified by name. The Federalist Papers are considered one of the most important sources for interpreting and understanding the original intent of the Constitution. The original essays can be downloaded from the Library of Congress. http://thomas.loc.gov/home/histdox/fedpapers.html In the author column, you will find 74 essays with identified authors: 51 essays written by Hamilton, 15 by Madison, 3 by Hamilton and Madison, 5 by Jay. The remaining 11 essays, however, is authored by “Hamilton or Madison”. These are the famous essays with disputed authorship. Hamilton wrote to claim the authorship before he was killed in a duel. Later Madison also claimed authorship. Historians were trying to find out which one was the real author. In 1960s, statistician Mosteller and Wallace analyzed the frequency distributions of common function words in the Federalist Papers, and drew their conclusions. This is a pioneering work on using mathematical approaches for authorship attribution. Nowadays, authorship attribution has become a classic problem in the data mining field, with applications in forensics (e.g. deception detection), and information organization. The Federalist Paper data set (fedPapers85.csv) is provided in LMS. The features are a set of “function words”, for example, “upon”. The feature value is the percentage of the word occurrence in an essay. For example, for the essay “Hamilton_fed_31.txt”, if the function word “upon” appeared 3 times, and the total number of words in this essay is 1000, the feature value is 3/1000=0.3% Organize your report using the following template: Section 1: Data preparation You will need to separate the original data set to training and testing data for classification experiments. Describe what examples in your training and what in your test data. Section 2: Build and tune decision tree models First build a DT model using the default setting, and then tune the parameters to see if better model can be generated. Compare these models using appropriate evaluation measures. Describe and compare the patterns learned in these models. Section 3: Prediction After building the classification model, apply it to the disputed papers to find out the authorship and report the performance accuracy of your models.