Categorical Data Analysis For The Behavioral And Social Sciences 2nd Edition Razia Azen, Cindy M. Walker - Solutions

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The following are the marks of eight students in the Physics and Chemistry test. Find Karl Pearson’s and Spearman’s coefficient of correlation.
The exam scores of 15 students in an Economics and Statistics class are given in the following table. Calculate the Spearman correlation coefficient to estimate the relationship between the scores.Student ID Economics Statistics 1 94 88 2 95 60 3 35 84 4 75 100 5 100 80 6 46 90 7 93 70 8 81 75 9 60
Calculate the Karl Pearson coefficient of correlation between the net profits (in crores)and Non-performing assets (in crores) of the State Bank of India for ten years starting from 2009–2010 to 2018–2019.Financial Year Net Profits NPA 2009–10 12,020.54 10,870.17 2010–11 11,179.94 12,346.89
The following table summarizes India’s export and import values (in the US $ Million)for ten years starting from 2009–2010 to 2018–2019. Calculate the covariance and Pearson’s correlation coefficient and comment on the relationship Period (2018) Export Import 2009-10 1,78,751 2,88,373
Zero correlation implies that X and Y variables are independent. Comment.
The sales (₹) by a showroom in a month are given next. Calculate descriptive statistics using the data analysis option.Sales ( ₹)30,000 68,300 31,800 78,000 28,400 43,900 34,500 89,100 64,400 11,700 12,500 1,02,000 2,31,000 45,000 68,900 84,200 22,400 14,900 51,300 33,400 53,500 29,900 68,700
The number of taxpayers in 30 different cities is given next. Calculate the skewness and kurtosis and comment.
The ratings given by 40 people for a new feature on social media app are given next.Ratings 4 5 1 2 5 4 1 3 5 4 5 2 4 1 3 2 2 4 3 3 1 5 3 3 1 2 3 3 2 4
Calculate the standard deviation and coefficient of variation for the following data Height (cm) Weight (kg)160 54 172 66 165 59 150 55 182 74 175 69
The annual household income of ten individuals is given next:Individual Household Income (₹)A 80,000 B 1,20,000 C 3,60,000 D 1,10,000 E 2,40,000 F 1,00,000 G 60,000 H 55,000 I 2,50,000 J 69,000 Calculate the standard deviation and variance for the data.
The following table summarizes the GDP (in billion US Dollars) of 20 countries.Calculate the standard deviation and interpret the result.
The share prices for the last ten days of two firms, A and B, are next.Share Prices (A) (₹) Share Prices (B) (₹)3,325 2,657 1,248 529 3,131 2,237 673 2,300 787 1,057 1,345 1,736 1,544 1,863 3,247 1,728 2,583 819a) Calculate range for Firm A.b) Calculate range for Firm B.c) Which firm has
The table that follows summarizes the temperature (in Celsius) recorded for a month.Calculate range for the data. Day Temperature Day Temperature 12345678 33 19 28 20 27 21 33 9 10 26 11 32 12 29 13 14 15
The college decided to grade the students for a semester based on the following criteria.Criteria Weightage Assignment 10%Test 20%Exam 60%Viva 10%
Ten cars travelled from Delhi to Chandigarh.The table shows the petrol consumption for each car. Calculate the harmonic mean to estimate the average consumption of petrol per car.
XYZ Ltd. earned Net Profit (million US $) as follows in the last ten years. Calculate the geometric mean.Year Net Profit(Million US $)2019 67,160 2018 64,660 2017 63,500 2016 62,700 2015 63,050 2014 66,600 2013 66,400 2012 65,400 2011 57,830 2010 51,250
The maximum temperature recorded for November in Delhi is stated next. Calculate the median temperature and mode.
The sales targets achieved by 20 sales representatives of P Ltd are summarized next.Calculate mean and median for the data.Sale Representative Sales Sale Representative Sales 1 2,13,000 11 4,01,000 2 4,89,000 12 2,70,000 3 2,80,000 13 2,28,000 4 3,96,000 14 4,52,000 5 1,11,000 15 4,71,000 6
The following table provides the birth rate (per thousand) of different countries.
Prepare a suitable graph from the following data of a firm.Year Exports (in ₹) Imports (in ₹)2010–11 459 305 2011–12 320 290 2012–13 350 340 2013–14 380 400 2014–15 300 280 2015–16 280 315 2016–17 350 370
The following data is about the household expenditure of families A and B per month(in ₹ 000).Items of Expenditure Family A Family B Food 50 70 Clothing 30 50 Rent 25 35 Education 10 25 Medical 9 20 Others 15 30 Draw a suitable graph. Which item did family A and B spend the most and the least?
The following table presents the annual data of the sunshine company about its shoe saleChoose the type of graph to be drawn for this firm and draw it in Excel. Year Pair of Shoes Sold 1990-91 20 1991-92 45 1992-93 110 1993-94 245 1994-95 290 1995-96 250 1996-97 503
The following table gives the data of age and number of persons corresponding to that age in a particular localityIdentify the type of graph suitable for given data and prepare it in Excel. Also, interpret the results. Age 18 21 Number of Persons 5 25 24 15 27 40 31 46 35 92 37 70 39 64 42 33 45 42
Summarize the class results by using the Pivot Table.
Name the students who topped the class and who scored the least marks.
How many students scored more than 80 marks (use COUNTIF function)?
Using the SUMIF function, calculate the aggregate marks obtained by students scoring more than 80.
What average marks did the class obtain in theory and practical?
Using the AutoSum function, calculate the aggregate marks of all students in theory and practical.
If the total marks are increased to 300, compute the new table showing the combined upgraded marks.
Complete the table.
11.10 For the model described in Problem 11.9, suppose that the variance of U0j(i.e., τ0 2) is estimated to be 0.2.a. Interpret this value in the context of this model and variables.b. How does this variance inform your answers to Problem 11.9?
11.9 For the data set described in Problem 11.6, suppose that you obtain the following results for a random intercept model, where πij is the predicted probability of proficiency, AGEc is mean-centered age using the mean age of 50 years, and STAFFc is the mean-centered number of clinic staff using
11.8 For the model described in Problem 11.7, suppose that the variance of U0j(i.e., τ0 2) is estimated to be 0.4.a. Interpret this value in the context of this model and variables.b. How does this variance inform your answers to Problem 11.7?
11.7 For the data set described in Problem 11.6, suppose that you obtain the following results for the random intercept model, where πij is the predicted probability of proficiency and AGEc is mean-centered age using the mean age of 50 years:logit πij ij AG j ( ) = − . 3 0 2( ) Ec +U0a. Fully
11.6 Suppose that you are analyzing a data set consisting of a random sample of patients who were selected from a random sample of clinics, and you would like to predict whether a given patient will fully recover from a flu-like virus based on the patient’s age (measured in years).a.
11.5 For the model described in Problem 11.4, suppose that the variance of U0j(i.e., τ0 2) is estimated to be 0.2 and the variance of U1j(i.e., τ1 2) is estimated to be 0.1.a. Interpret these values in the context of this model and variables.b. How do these variances inform your answers to
11.4 For the data set described in Problem 11.1, suppose that you obtain the following results for the random slope model, where πij is the predicted probability of proficiency and ATTc is mean-centered attendance using the mean attendance of 150 days:logit πij ij j j ij ( ) = + . 18 03. 5( )
11.3 For the model described in Problem 11.2, suppose that the variance of U0j(i.e., τ0 2) is estimated to be 0.3.a. Interpret this value in the context of this model and variables.b. How does this variance inform your answers to Problem 11.2?
11.2 For the data set described in Problem 11.1, suppose that you obtain the following results for the random intercept model, where πij is the predicted probability of proficiency and ATTc is mean-centered attendance using the mean attendance of 150 days:logit πij ij ( ) = + ( ) ATTc +U j . 2
11.1 Suppose that you are analyzing a data set consisting of a random sample of students who were selected from a random sample of schools, and you would like to predict whether a given student will achieve proficiency in reading based on the student’s school attendance (e.g., number of days
10.10 Pick your own research question that can be addressed using variables from the 2006 GSS data set and a logistic regression model with at least two predictors and a response variable containing at least three categories.a. State your research question and expectations regarding the findings.b.
10.9 Explain what the proportional odds assumption means in the context of the study described in Problem 10.7.
10.8 Use the information from Problem 10.7 to answer the following (show your work):a. What is the predicted cumulative probability for a boy to reach the fourth proficiency level (level 3) or less? What is the predicted cumulative probability for a boy to reach the third proficiency level (level
10.7 O’Connell (2006) used data from the Early Childhood Longitudinal Study to examine how gender affects literacy proficiency for first-grade children. The outcome variable of literacy proficiency consisted of six categories, level 0 to level 5, ordered in terms of the level of proficiency
10.6 Explain what the proportional odds assumption requires in general. Use the variables from the model in Problem 10.5 to illustrate your answer specifically and explain whether the assumption is satisfied for this model.
10.5 Use the logist1 data from www.psych.yorku.ca/lab/psy6140/ex/logistic.htm to fit the proportional odds model predicting the level of improvement from sex and treatment.Assume that the level of improvement is ordinal, with categories representing 0 = no improvement, 1 = some improvement, 2 =
10.4 Use the logist1 data from www.psych.yorku.ca/lab/psy6140/ex/logistic.htm to fit a logistic regression model predicting the level of improvement from the patient’s sex and treatment(without their interaction). Assume that the level of improvement is a nominal variable, and provide substantive
10.3 Use the diabetes data set (see also Problem 8.11, http://friendly.apps01.yorku.ca/psy6140/examples/logistic/logidiab.sas, from Michael Friendly’s web site) to fit a logistic regression model predicting the type of diabetes from the four glucose (or insulin) level predictors.Use the normal
10.2 Refer to the information in Problem 10.1 to answer the following questions (show your work):a. What is the logistic regression model to predict the preference of vanilla over strawberry ice cream? What is the predicted probability of this preference for males?What is the predicted probability
10.1 Suppose that we would like to investigate whether gender has a significant effect on one’s choice of ice cream flavor. The data file (obtained from https://stats.idre.ucla.edu/stat/spss/output/mlogit.sav) consists of 200 high school students and includes each student’s favorite ice cream
9.10 Pick your own research question that can be addressed using variables from the 2006 GSS data set and a logistic regression model with at least two predictors.a. State your research question and expectations regarding the findings.b. Explain why a logistic regression model is more appropriate
9.8 Choose a research question that can be addressed with the data from Problem 7.4 using a logistic regression analysis. Explain which of the variables would be used as the outcome and which variables would be used as predictors. Fit the logistic regression model to address your research question.
9.7 Using the data from Problem 7.2, create a binary response variable to represent agreement with spanking (e.g., combine the first two agreement categories and combine the last two agreement categories). Use the other two variables as predictors. Explain your research question, choice of the
9.6 Use the data from Problem 7.1 to predict the probability that a respondent will favor the death penalty from the respondent’s fundamentalism level and sex as well as their interaction. Fully interpret the results of the analysis, including substantive interpretations of the model’s overall
9.5 Table 9.7 provides data (obtained from Michael Friendly’s web site at York University)pertaining to the relationship between a treatment for diabetes (treated or placebo), sex(male or female), and improvement status (improved or not improved).a. Fit the logistic regression model to predict
9.4 Use the 2006 GSS data set to predict whether a respondent favors the death penalty for murder (CAPPUN) from the respondent’s sex (SEX) and race (RACE) as well as their interaction.a. Use the likelihood ratio test to test the interaction between SEX and RACE. Report and fully interpret the
9.3 Fit the log-linear model that is equivalent to the logistic regression model from Problem 9.2a. Indicate the parameter estimates that are equivalent across both models and show that their interpretations are equivalent (for example, show that both models predict the same odds or probabilities
9.2 Using the data in Table 7.2 from the 1992 United States presidential election, logistic regression was used to model voting for Clinton (i.e., yes rather than no) from the voter’s political view (liberal, moderate, or conservative). Using dummy coding for political view(X) where conservative
9.1 Suppose that a logistic regression model contains one categorical predictor with three categories: A, B, and C. Using dummy coding for the predictor with category A as the reference category, the model has the following parameters: α = −1.0, β1= 2.0, and β2= 1.0.a. What does each of the
8.11 The diabetes data set, provided by Michael Friendly at http://friendly.apps01.yorku.ca/psy6140/examples/logistic/logidiab.sas, contains 145 observations on four measures of glucose or insulin levels, relative weight, and diabetic status (group). Use this data set to predict whether a patient
8.10 Pick your own research question that can be addressed using variables from the 2006 GSS data set and a logistic regression analysis. You should include at least two predictors.a. State your research question and expectations regarding the findings.b. Explain why a logistic regression analysis
8.9 Use the 2006 GSS data set (e.g., at http://sda.berkeley.edu/archive.htm) to predict whether a respondent favors sex education in the public schools (SEXEDUC) from the respondent’s age (AGE), highest year of school completed (EDUC), and socioeconomic index (SEI). Note: For valid model
8.8 Use the ADD data set to select a binary outcome measure and at least two continuous predictors of this outcome.a. Fit the logistic regression model and report its fit.b. Conduct the appropriate tests to determine if any predictors can or should be dropped from the model. Explain your process
8.7 Compare the models fit in Problems 8.5 and 8.6:a. Conduct the likelihood ratio (G2) test to compare the two models. Is there a significant difference between these two models in terms of fit? What conclusions can you draw from these results?b. Report the Wald test statistic that corresponds to
8.6 Use the ADD data set and fit a logistic regression model to predict whether a student dropped out before completing high school from average ADD score (ADDSC) only.a. Fully interpret the value, direction, and statistical significance of the slope estimate in this model.b. Does this model
8.5 Use the ADD data set and fit a logistic regression model to predict whether a student dropped out before completing high school (DROPOUT) from average ADD score(ADDSC) and ninth-grade GPA.a. Does the model with both of the predictors provide significantly better fit (or prediction) than a model
8.4 Consider a logistic regression model with two predictors and the following parameters:the intercept is α = 1.5, and the slopes are β1= 0.4 and β2= −0.1.a. Compute the predicted odds when X1= 2 and X2= 4.b. Compute the predicted odds when X1= 2 and X2= 5.c. Based on your results to parts
8.3 Consider a logistic regression model with two predictors and the following parameters:the intercept is α = 2, and the slopes are β1= 0.5 and β2= −0.3.a. Compute the predicted odds when X1= 2 and X2= 5.b. Compute the predicted odds when X1= 3 and X2= 5.c. Based on your results to parts (a)
8.2 Consider a logistic regression model with two predictors and the following parameters:the intercept is α = 1.5, and the slopes are β1= 0.4 and β2= −0.1.a. Compute the predicted logit of the probability when X1= 0 and X2= 0.b. Compute the predicted odds when X1= 0 and X2= 0.c. Based on your
8.1 Consider a logistic regression model with two predictors and the following parameters:the intercept is α = 2, and the slopes are β1= 0.5 and β2= −0.3.a. Compute the predicted logit of the probability when X1= 0 and X2= 0.b. Compute the predicted odds when X1= 0 and X2= 0.
7.8 What are the advantages and disadvantages of fitting a log-linear model rather than using the procedures described in Chapter 5? When would a researcher be more likely to fit a log-linear model than to use the tests of association described in Chapter 5?
7.7 In Chapter 5 (Section 5.6), we discussed the Berkeley graduate admissions data, where the variables of interest involved the applicant’s sex and admission to graduate school across six departments. The data are presented in Table 5.10 and can be analyzed using log-linear models. Remove
7.6 In Chapter 5 (Section 5.6), we discussed the Berkeley graduate admissions data, where the variables of interest involved the applicant’s sex and admission to graduate school across six departments. The data are presented in Table 5.10 and can be analyzed using log-linear models.a. Fit the
7.5 Table 7.24 depicts data obtained from the 1994 administration of the GSS. Describe how you found the most parsimonious log-linear model for this data, fit this model, and use the model parameters (or fitted values) to provide a substantive interpretation of any associations that exist between
7.4 Table 7.23 depicts data that was obtained from the 1994 GSS on respondents’ sex, whether they are separating from a spouse/partner, ability to afford needed medical care, and condition of home.a. Determine the best fitting, most parsimonious model and describe the process you used to do so.
7.3 Table 7.22 depicts data that was obtained from the 1994 administration of the GSS on whether the respondent was born in the United States, the respondent’s sex, and the respondent’s ability to use the Internet.a. Fit the homogeneous association model to the data and examine its standardized
7.2 Table 7.21 depicts data that was obtained from the 1994 administration of the GSS on respondents’ sex, race, and level of agreement with spanking to discipline a child. Suppose that the main research question of interest is whether and how sex is associated with endorsement of spanking, as
7.1 Table 7.20 depicts data that was obtained from the 1994 administration of the General Social Survey (GSS) on respondents’ sex, level of fundamentalism, and whether they favor or oppose the death penalty for murder. Find the most parsimonious log-linear model for this data and interpret its
6.11 Suppose that the results shown in Figure 6.8 were obtained for the study described in Problem 6.8, using only x4= years of schooling as a predictor:a. What are the estimated values of the intercept (α) and slope (β) for this GLM?b. What is the likelihood ratio test statistic for the fit of
6.10 Refer again to the overall model described in Problem 6.8. Explain your reasoning for each of the following:a. What is the random component of the GLM used in this study?b. What is the systematic component of the GLM used in this study?c. What is the most appropriate link function for the GLM
6.9 Refer again to the study described in Problem 6.8. Suppose that the model containing only the “demographic” variables, x3= annual income and x4= years of schooling, results in −2ln(likelihood) = 574.a. Is the difference between the model containing all four predictors and the model
6.8, what is the value of the likelihood ratio statistic for the overall fit of the model(containing all four predictors)?b. Is the likelihood ratio statistic for the overall fit obtained in part (a) statistically significant? What conclusions (as related to this study) can be drawn based on this
6.8 Suppose that a study was conducted to predict whether an individual regularly smoked, or the expected probability of smoking, based on four continuous predictors such as x1= smoking exposure, x2= healthy lifestyle (e.g., diet, exercise), x3= annual income, and x4= years of schooling. Further,
6.7 Use the data in Table 6.2, where X represents a continuous predictor and Y represents a probability.a. Create a scatter plot of the data (with values of X on the horizontal axis and values of Y on the vertical axis). Describe the shape of the relationship between X and Y. Is it linear
6.6 Come up with an example of a study in which the outcome variable is likely to follow the binomial distribution. Explain what the random component, systematic component, and link function would be in this case.
6.5 Come up with an example of a study in which the outcome variable is likely to follow the Poisson distribution. Explain what the random component, systematic component, and link function would be in this case.
6.4 Suppose that a researcher would like to predict the probability that a voter will vote for a particular candidate based on several characteristics of the voter such as family income, educational level, race, and gender. Explain your reasoning for each of the following:a. What is the random
6.3 Suppose that a researcher would like to predict the number of students who are absent from a given classroom per day based on several predictors such as family income, race, and gender. Explain your reasoning for each of the following:a. What is the random component (outcome and its
6.2 What advantages do GLMs (generalized linear models) have over general linear models(such as regression and ANOVA models)?
6.1 What is the general purpose of a link function, and why is a link function other than the identity needed in some cases but not in others?
5.15 In a three-way contingency table, if there is no homogeneous association, is it possible for any pair of the variables to be conditionally independent? Explain your answer.
5.14 In a three-way contingency table, suppose that X and Y are conditionally independent (conditional on Z). Will the data then necessarily show homogeneous association?Explain your answer.
5.13 Think of three categorical variables that theoretically should display conditional independence and explain why you believe that to be the case.
5.12 Think of three categorical variables that theoretically should display homogeneous association and explain why you believe that to be the case.
5.11 Using a substantive example, explain the difference between homogeneous association and conditional independence.
5.10 Using a substantive example, explain the difference between marginal and conditional associations.
5.9 Use the data in Table 5.17, obtained from the 2003 PISA study, and combine the categories of every lesson and most lessons, as well the categories of some lessons and never or hardly never. Similarly, combine the categories of strongly agree and agree, as well as the categories of strongly
5.8 Use the data in Table 5.16, obtained from the 2003 PISA study. Is there homogeneous association or a three-way association among these variables? Justify your answer with the appropriate statistical test and provide a substantive interpretation of the results?
5.7 Use the data in Table 5.15, obtained from the 2003 Programme for International Student Assessment (PISA) study. Come up with three specific research questions that can be answered using this data set. For each question, report the appropriate statistical tests and provide a substantive

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Categorical Data Analysis For The Behavioral And Social Sciences 2nd Edition Razia Azen, Cindy M. Walker - Solutions

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