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It is economic statistic problems. Notes: Results in column 1 are from baseline specifications as specified in equation (3). Column 2 reports a measurement-error-corrected estimate using the estimator described in Appendix B. Column 3 reports results using player fixed effects instead of own ability. The dependent variable is the golf score for the round. The ability variable is measured using the player\'s handicap. Standard errors are in parentheses and are clustered by playing group. All regressions include round fixed effects and weight each observation by the inverse of the sampling variance of estimated ability of each player. Consider the first regression (left hand column). What is the dependent variable? What is the hypothesis being tested? Which variable captures the hypothesis? What is its coefficient? What does the coefficient mean? Is it significant at the 5% level? What does this mean? Would you say that there is good evidence for peer effects? Explain. Is the coefficient on own ability Solution 2. golf score for round 3.H0: coefficient of regression equation are not significant H1: coefficient are significant 4.avrage ability of parteners captures hypothesis 5. coefficint if value of multiplier of independent variable in regression equation 6. this coefficient gives chenge in dependent variable as we change independent variabble by 1 unit 7. yes test is signifiacant at 5% level 8. means that means probability of H0 being true is mor than 95% 9.yes there is good evidence for peer effects because avrage ability of persons coefficient changes in each column. 10. yes coefficient on own ability significant at 5 % level 11. this is independent type of variable 12. estimate is nothing but estimator of population size which is always equal to mean of population.

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Fill in the blanks: 8.1. The magnitude of the correlation is indicated by the correlation _____ which can range from -1.00 to +1.00. 8.2. The most common and efficient way to present the correlations of several variables with each other is by using a(n) ______ table. 8.3. The correlation between two variables can be shown graphically by a ________. 8.4. The null hypothesis predicts that the correlation coefficient is equal to _______. 8.5. The Spearman rank order correlation is used when the variables to be correlated are measured on a(n) ______ scale. Circle the correct answer: 8.6. The hypothesis that states that r ¹ 0 is an example of a(n) alternative / null hypothesis. 8.7. When an increase in one variable is associated with a decrease in the other variable, the correlation between these two variables is positive/negative . 8.8. In order to use the Pearson product-moment correlation, the variables to be correlated should be measured on an ordinal/interval scale. 8.9. When the points on a scattergram go from the bottom left to the top right they represent a positive/negative correlation. 8.10. The true correlation between two variables may be underestimated when the variance of one of the variables is very high/very low . 8.11. When the null hypothesis is rejected at p <.001, it means that the chance that r =0 is very small/very high . 8.12. The null hypothesis is rejected when the obtained correlation coefficient is higher/lower than the critical value. Answer/compute the following questions: 8.13 Which correlation coefficient ( a or b ) shows a stronger relationship between the two variables being correlated? a. X 1 &Y 1 : r = .85 b. X 2 &Y 2 : r = -.94 8.14. Following are two scattergrams (in Figure A and in Figure B). Four different correlation coefficients are listed under each scattergram. Choose the coefficient that best matches each scattergram. Y Y · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · X X Figure A Figure B A1. r = .50 B1. r = -.57 A2. r = .78 B2. r = .92 A3. r = -.10 B3. r = .38 A4. r = -.89 B4. r = -.91 8.15 Following is a scattergram showing the scores of 8 statistics students on two tests, X and Y . Each of the first 7 students is represented by a dot and their scores are listed in the table that follows. Use the scattergram to find the scores of student #8 on test X and test Y . The location of this student on the scat.

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Table 2: Survival Status Disease Severity Donor’s Sex Alive Dead Total None Female 14 1 15 Male 21 2 23 Mild Female 17 1 18 Male 40 2 42 Moderate Female 15 1 16 Male 33 6 39 Severe Female 6 1 7 Male 16 17 33 Total 162 31 193 2. Using data in table 2, compute the common odds ratio of the association between donor’s sex and the survival status of the infant, after controlling for severity ?. A)Manually calculate a common odds ratio to test the hypothesis of no association between donor’s sex and the survival status of the infant, after the inclusion of the variable severity using the common odds ratio? B)Interpret the results. How does the common odds ratio differ from the simple odds ratio computed in part 1? What effect might it have on your decision from part 1 to reject or fail to reject the null hypothesis? C)Why is it important to know the effect of severity on the association of gender and survival? 3. Perform a simple logistic regression using SPSS and the Wk 6 Dataset (SPSS document). Answer the following questions based on your SPSS output A)Are the results of the simple logistic regression similar to or different from the results of the simple odds ratio ? B)How are they similar or different? Include output from SPSS and an interpretation of the OR and confidence intervals in your response? C)What can you do using logistic regression to duplicate the results from part 2 of this application (the use of CMH for common odds) ? Answer question 1 using data in table 1 below. Table 1: Survival Status Donor’s Sex Alive Dead Total Female 52 4 56 Male 110 27 137 Total 162 31 193 1. Compute the simple odds ratio of the association of donor’s sex and survival status of the infant. Be sure to answer all four parts to this question (a, b, c, and d), including manual calculation of the chi-square value. A)Manually calculate a simple odds ratio to test the hypothesis of no association between donor’s sex and the survival status of the infant, without the inclusion of the variable severity using a 2 x 2 table for sex and survival B)Manually calculate the confidence interval associated with that odds ratio using the appropriate formula C)Manually compute the Chi Square test statistic for this table 1. Interpret the results. Include an interpretation of the odds ratio, the confidence interval, and the Chi Square test statistic in your response PART3 Step-by-Step Guide to Assignment 6.3 Odds Ratios Problem 3. Perform a simple logistic regression using SPSS and the practice problem 6.3 data set. Answer the following questions based on your SPSS output Step 1. Open the practice_problem_6.3.sav dataset. Step 2. Go to Analyze ( Regression ( Binary logistic Step 3. Place Survival_Status in the Dependent box and place Gender in the Covariate(s) box. Click Options. Step 4. In the Logistic Regression: Options window, check Classification plots, Casewise listing of residuals, and CI for exp(B)..

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Running Head: Response ! 1 Final Project by Julia Fernandez Professor Heeyoung Kim Rider University August 6, 2018. Running Head: Response ! 2 Background: Manufacturing sector of USA is considered as the backbone of economy. This sector contributes a lot in earning foreign exchange and maintaining the GDP of the company. As it also plays an important role in inflation. If the goods that are produced under this sector sell in the market at a reasonable price than keeps stability in economy and if the products are sell at high prices than inflation arise that leads to create disability in the country. As we know that United States is renowned for its high quality manufacturing products (Timothy E. Zimmer, 2017). Therefore, US exported billion dollar products every year. It contributes a lot in earning foreign exchange that keep the reserves of country always high. Hence, the role of manufacturing sector plays a significant role in the development and progress of country towards prosperity. Description of problem: The problems that is intended to explore in this report is that, “how the expenditure of the manufacturing companies are affected by number of employees and its productivity”. As we know that these manufacturing sector are capital intensive and labor intensive companies that require a lot of money and man power to operated (census.gov, 2016). Hence, every manufacturing company struggles that how they can lower their expenditure are kept low by increasing the productivity of employees. Thus, this relationship is investigated in this report. If the companies are successful in lowering their expenses that they will be able to earn high profits that benefits the company and country as well. Why this topic interests me? Running Head: Response ! 3 There are many reason due to which I select this sector as this is the biggest sector of United States and my core interest is in the exploration of this sector. I want to explore those variables that contributes in the success of manufacturing sector. Moreover, I want to explore that whether number of employees have a direct impact on the productivity of company and on expenditure as well. This research will help in promoting growth in this sector. Methodology: The methodology that is selected to investigate this topic is, data about the manufacturing companies of United States is excessed through the US census that is the latest census which was conducted in 2016. Study unit: The unit of study is the main body that is being analyzed in this study. In this study the unit of analysis is the manu ...

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Section 1: Data File Description The fictional data represents a teacher's recording of student demographics and performance on quizzes and a final exam across three sections of the course. Each section consists of 35 students which totals to 105 students (sample size, N = 105). The dataset has 21 variables but in this case only two variables will be analyzed. These are: gender and gpa variables. The gender variable is categorized as nominal since the numbers are arbitrarily assigned to represent group membership. The ‘gpa’ variable belongs to the interval data group since it has a true zero which is meaningful. Alternatively, gender could be categorized as a categorical variable while ‘gpa’ ‘as a continuous variable. Section 2: Testing Assumptions 1. Articulate the assumptions of the statistical test. Paste SPSS output that tests those assumptions and interpret them. Properly integrate SPSS output wher1e appropriate. Do not string all output together at the beginning of the section. All statistical tests operate under a set of assumptions. For the t test, there are three assumptions: · The first assumption is independence of observations. · The outcome variable Y is normally distributed. · The variance of Y scores is approximately equal across groups (homogeneity of variance assumption) Figure 1: histogram of GPA The histogram above shows that the variable is probably not normally distributed. The bell shape is absent and two peaks are evident. Table 1: descriptives Descriptives Statistic Std. Error GPA Mean 2,78 ,075 95% Confidence Interval for Mean Lower Bound 2,63 Upper Bound 2,93 5% Trimmed Mean 2,80 Median 2,72 Variance ,583 Std. Deviation ,764 Minimum 1 Maximum 4 Range 3 Interquartile Range 1 Skewness -,052 ,236 Kurtosis -,811 ,467 With reference to the table 1 above, the ‘GPA’ variable is in the ideal range for skewness due to the fact that its absolute value for skewness are is less than .50 (approximately symmetric). The GPA variable is not ideal but acceptable since its kurtosis value is greater than .50 but less than 1. This new information gives mixed signals about the data being normal and only a normality test could iron out the differences. Table 2: Normality test Tests of Normality Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. GPA ,091 105 ,033 ,956 105 ,001 a. Lilliefors Significance Correction Looking at the table above, the p-value is less than 0.05. Therefore, the null hypothesis is rejected and thus it can be concluded that the variable is not normally distributed. However, since the sample size is sufficiently large, one does not need to worry about this violation. On the other hand, Levene’s test provides a p =0.566 (table 3) meaning that the null hypothesis should not be rejected. Thus the homogeneity of variances assumption is not violated. It’s also assumed that proper research procedures that maintain independence of observations were followed. Two of the th.

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Hypothesis Testing Definitions: A statistical hypothesis is a guess about a population parameter. The guess may or not be true. The null hypothesis, written H0, is a statistical hypothesis that states that there is no difference between a parameter and a specific value, or that there is no difference between two parameters. The alternative hypothesis, written H1 or HA, is a statistical hypothesis that specifies a specific difference between a parameter and a specific value, or that there is a difference between two parameters. Example 1: A medical researcher is interested in finding out whether a new medication will have undesirable side effects. She is particularly concerned with the pulse rate of patients who take the medication. The research question is, will the pulse rate increase, decrease, or remain the same after a patient takes the medication? Since the researcher knows that the mean pulse rate for the population under study is 82 beats per minute, the hypotheses for this study are: H0: µ = 82 HA: µ ≠ 82 The null hypothesis specifies that the mean will remain unchanged and the alternative hypothesis states that it will be different. This test is called a two-tailed test since the possible side effects could be to raise or lower the pulse rate. Notice that this is a non directional hypothesis. The rejection region lies in both tails. We divide the alpha in two and place half in each tail. Example 2: An entrepreneur invents an additive to increase the life of an automobile battery. If the mean lifetime of the automobile battery is 36 months, then his hypotheses are: H0: µ ≤ 36 HA: µ > 36 Here, the entrepreneur is only interested in increasing the lifetime of the batteries, so his alternative hypothesis is that the mean is greater than 36 months. The null hypothesis is that the mean is less than or equal to 36 months. This test is one-tailed since the interest is only in an increased lifetime. Notice that the direction of the inequality in the alternate hypothesis points to the right, same as the area of the curve that forms the rejection region. Example 3: A landlord who wants to lower heating bills in a large apartment complex is considering using a new type of insulation. If the current average of the monthly heating bills is $78, his hypotheses about heating costs with the new insulation are: H0: µ ≥ 78 HA: µ < 78 This test is also a one-tailed test since the landlord is interested only in lowering heating costs. Notice that the direction of the inequality in the alternate hypothesis points to the left, same as the area of the curve that forms the rejection region. Study Design: After stating the hypotheses, the researcher’s next step is to design the study. In designing the study, the researcher selects an appropriate statistical test, chooses a level of significance, and formulates a plan for conducting the study..

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It is important for managers to combine \"intuition\" with the systematic study of organizational behavior and management, including evidence-based management. Why is this important? How does this differ from the idea that management is just \"common sense\"? Solution For me Managers are the ones who control manage and direct the whole team working under him/her Organizations are always looking for Leaders and Managers are the ones who have leadership skills. Management from people in the trenches, as opposed to the latest esoteric musings from the.

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It is during the design step of the Appreciative Inquiry process that the participants identify interest areas for focus and dialogue. True False Solution Appreciative Inquiry (AI) is a change methodology grounded in theories from the disciplines of organization behavior and the sciences of sociology and psychology, with a good dose of metaphyics. Those of us who practice AI refer to it as both a way of being and doing. Appreciative Inquiry is a perspective on the world that invites us to see ourselves and the world through an appreciative or valuing eye. We are made aware that how we use language, how we ask questions, and what stories we tell shape our own and collective destinies. Process of Appreciative Inquiry The process of Appreciative Inquiry is named the 4-D Cycle – Discover, Dream, Design, Destiny. The four steps in the iterative cycle: Step 1: Discover Step 2: Dream Step 3: Design Step 4: Destiny Affirmative Topic Choice Topic selection begins the adventure into appreciative inquiry. It requires searching for the positive description of what is desired by the organization. Because individually and collectively we move towards whatever we study, the selection of the AI topics is critical. It is the foundation of the entire process. Hence, the topic choice process should be an affirmative act— In a nutshell, the topics should be bold—something that stretches the organization beyond the status quo, desired—something that you really want to see happen, and stimulating—something that has the potential to energize people, mobilize forces, and be strategic. Preparing for Topic Selection The selection of the AI topics can be supported through an exercise. As an initial exploration, each member of the AI team or summit will complete the following exercise by recording their responses on a piece of paper— Once each person has completed the exercise, the AI team divides itself into pairs. Each person takes a turn at interviewing and being interviewed. The questions just answered are used with the interviewer probing deeper into the answers, getting excited, being surprised, curiously inquiring. Listen intently. Pause before asking questions. Each pair joins to form a group of 8-12 people. The interviewers report to the group what was learned from the interview. A list is created on newsprint/poster boards..

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It is believed that 4% of children have a gene linked to juvenile diabetes. Researchers would like to test new monitoring equipment for diabetes. They hoped to have at least 21 children for their study, the researchers test 731 newborns for the presence of the gene linked to diabetes. what is the probability that they find enough subjects for their study? Solution use normal approximation to binomial distributon probability that they find enough subjects for their study=p(x>=20)=p(z>((20/731)- 0.04)/sqrt(0.04*0.96/731))=p(z>-1.74)=0.95907.

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It is assumed that the probability of a success, p, is constant over the entire set of trials for binomial random variables. Can the binomial probability distribution be a model for determining the likelihood of any discrete random variable with two mutually exclusive and collectively exhaustive outcomes? Can you think of any real-world business scenarios where meeting the assumption that the probability of success might be difficult to meet? Solution Bayes\' Theorem Bayes\' Theorem is a result that allows new information to be used to update the conditional probability of an event. Using the multiplication rule, gives Bayes\' Theorem in its simplest form: P(A | B) = P(A n B)/P(B) = P(B | A).P(A)/P(B) Using the Law of Total Probability: P(A | B) = P(B | A).P(A)P(B | A).P(A) + P(B | A\').P(A\') where: P(A) = probability that event A occurs P(B) = probability that event B occurs P(A\') = probability that event A does not occur P(A | B) = probability that event A occurs given that event B has occurred already P(B | A) = probability that event B occurs given that event A has occurred already P(B | A\') = probability that event B occurs given that event A has not occurred already Bayes\' theorem deals with the role of new information in revising probability estimates. The theorem assumes that the probability of a hypothesis (the posterior probability) is a function of new evidence (the likelihood) and previous knowledge (prior probability). The theorem is named after Thomas Bayes (1702–1761), a nonconformist minister who had an interest in mathematics. The basis of the theorem is contained in as essay published in the Philosophical Transactions of the Royal Society of London in 1763. Bayes\' theorem is a logical consequence of the product rule of probability, which is the probability (P) of two events (A and B) happening— P(A,B)—is equal to the conditional probability of one event occurring given that the other has already occurred—P(A|B)—multiplied by the probability of the other event happening—P(B). The derivation of the theorem is as follows: P(A,B) = P(A|B)×P(B) = P(B|A)×P(A) Thus: P(A|B) = P(B|A)×P(A)/P(B). Bayes\' theorem has been frequently used in the areas of diagnostic testing and in the determination of genetic predisposition. For example, if one wants to know the probability that a person with a particular genetic profile (B) will develop a particular tumour type (A)—that is, P(A|B). Previous knowledge leads to the assumption that the probability that any individual will develop the specific tumour (P(A)) is 0.1 and the probability that an individual has the particular genetic profile (P(B)) is 0.2. New evidence establishes that the probability that an individual with the tumor—P(B|A)—has the genetic profile of interest is 0.5. Thus: P(A|B) = 0.1×0.5/0.2 = 0.25 The adoption of Bayes\' theorem has led to the development of Bayesian methods for data analysis. Bayesian methods have been defined as \"the explicit use of external evidence .

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It has been stated that 40% of the general population donate their time and energy to working on community projects. Suppose 15 people have been randomly sampled from a community and asked if they donated their time and energy to community projects. Let x be the number of them that do donate their time and energy to community projects. Find the probability that more than seven of the 15 do donate their time and energy to community projects Solution Given X~Binomial(n=15, p=0.4) P(X=x)=xC15*(0.4^x)*(0.6^(15-x)) SO the probability is P(X>7)=P(X=8)+P(X=9)+...+P(X=15)=0.2131032.

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It came a big suprise when Apple\'s touchscrren Iphone 4 was found to have a problem. Users compalined of weak reception, and sometimes dropped calls. A quick survey at a local store found that 2 % of iphone 4 users experienced this reception problem. A. State true or false. The population is all Iphone 4 users? B. Does 2 % indiciate the population parameter or sample statistic ? Solution A. False. Problems are mostly in iPhone 5. B. Population parameter.

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It happened one Sunday morning. Police Solution 1. Ethical concerns on this type of experiments are primarily due to the different precedents in history that we have seen. If abuse is a driver in this type of experiment, one should determine strong foundations to stop this type of behaviors once it arises. No experiment should abide the fact that one is being mistreated or abused. 2. From my point of view, the guard position is the one with the highest probability to suffer less and not be mistreated. The role would engage with a bad conduct from the prisoners, however, the lack of power from the prisoner.

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Is turbidity a direct measurement of particle concentrations or is it an indicator? Explain briefly. Solution No turbidity is not a direct measurement of particle concentrations but it is an indicator. The turbidity of water is based on the amount of light scattered by the particles present in the water. The water will appear more turbid if there are more particles present in the water making it cloudy. It may be due to suspended solids and dissolved cloured materials. Hence it does not meaures particle concentration but acts as an indicator of clarity of water..

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Is there an integral domain containing exactly 10 elements? As always, completely justify your answer. You may not use the fact that every finite field has prime power order. Solution Let x be a nonzero element of the domain E, and consider the multiplication map f : E to E, by f(y) = xy. Since E is a domain, if xy = 0, then x = 0. Hence, f is injective, and since it is finite it is also surjective. So, 1 is in f(E); that is, there is some y in E such that xy = 1. So x is a invertible and D is a field. Since the only fields of finite have order p^k, we know that an integral domain with 10 elements does not exist..

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Is the following an observational study or an experiment? (Explain your answer). A survey of 275,811 first-year college students revealed that 32.4% of these students had an A average in high school. Solution This is a study The fundamental difference is that an experiment requires you to observe what happens when you make some sort of change. An observational study finds the relationship between changes that already exist..

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Is the Federal Reserve an independent institution? Explain. Solution The Federal Reserve is considered as an independent central bank. It is independent since its decisions do not have to be ratified by the President or Congress. The Federal Reserve System was created by Congress in 1913 \"to provide for the establishment of Federal reserve banks, to furnish an elastic currency, to afford means of rediscounting commercial paper, to establish a more effective supervision of banking in the United States, and for other purposes.\".

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Is the agency problem unique to for-profit organizations or does it occur in not-for-profits as well? Solution Yes Agency problem is unique or rare for profit organisation hardly u can find agency problem but these problems occures in non for profits oraganisaton frequently . Agency problems in NFPs arising from excess holdings of endowment assets.Research explores agency costs of excess cash holdings in for-profit firms, and, although the evidence is somewhat mixed, researchers generally find that excess cash is associated with excessive acquisitions and investment. Compared to cash holdings in for-profit firms. Excessive acquisitions and investment. Compared to cash holdings in for-profit firms, NFPs hold substantially greater assets in their endowments. Further, NFPs do not have obvious residual claimants with a strong interest and ability to monitor management, and unlike for-profits, it is generally not feasible for NFPs to return excess assets to donors...

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Is the Fed currently pursuing an expansionary, neutral, or contractionary monetary policy? What if any difficulties do you think may be encountered in implementing the current policy. Solution Fed is currently pursuing an expansionary monetary policy. Since the unemployment and inflation are growing very fast in the country, the policy maker wants to create more opportunity in the economy. Expansion creates opportunity of getting jobs and brings down the inflation by supplying the required products. Difficulty: Expansion requires money investment. The economy is suffering with recession now. Therefore, acquiring money for investments would be a difficult task..

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