Concept explainers
Do taller adults make more money? The authors of the paper “Stature and Status: Height, Ability, and Labor Market Outcomes” (Journal of Political Economics [2008]: 499–532) investigated the association between height and earnings. They used the simple linear regression model to describe the relationship between x = Height (in inches) and y = log(Weekly gross earnings in dollars) in a very large sample of men. The logarithm of weekly gross earnings was used because this transformation resulted in a relationship that was approximately linear.
The paper reported that the slope of the estimated regression line was b = 0.023 and the standard deviation of b was sb = 0.004. Carry out a hypothesis test to decide if there is convincing evidence of a useful linear relationship between height and the logarithm of weekly earnings. Assume that the basic assumptions of the simple linear regression model are reasonably met.
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Chapter 13 Solutions
Introduction To Statistics And Data Analysis
- A mail-order business selling personal computer supplies, software and hardware maintains a centralized warehouse. Management is currently examining the process of distribution from the warehouse and wants to study the factors that affect the warehouse distribution costs. Data collected over 24 random months contain the warehouse’s distribution cost (in thousands of Rands), the sales (in thousands of Rands) and the number of orders received. A multiple linear regression model was fitted to the data by using Stat1.2. Use the output to answer the questions that follow by typing only the letter of the correct option in the answer boxes. Variablesy: Warehouse Distribution Costx1: Salesx2: Number of Orders Model Fitting StatisticsR2 = 0.8504Adj R2: ? Regression Coefficients Beta Parameter Standard b Parameter Standard Estimates…arrow_forwardA psychological study aimed at predicting Al Ain secondary school students’ mental health scores via their scores in the life satisfaction scale. The researcher examines the following null hypothesis: there is no significant relationship between Al Ain secondary school students’ mental health and their life satisfaction scores at 0.05 level of significance. Use the following data to establish the required regression equation. Student # Mental health score out of 50 Life- satisfaction score out of 100 1 40 80 2 41 87 3 34 90 4 30 78 5 44 89 6 42 85 7 45 88 8 32 77 9 47 90 10 22 57 11 30 78 12 28 77 13 35 76 14 40 84 15 31 76 16 39 80 17 41 84 18 24 60 19 22 50 20 37 75 21 40 80 22 38 78 23 29 60 24 24 55 25 24 62 26 29 61 27 32 65 28 34 66 29 28 67…arrow_forwardA newspaper used an estimated regression equation to describe the relationship between y = error percentage for subjects reading a four-digit liquid crystal display and the independent variables x1 = level of backlight, x2 = character subtense, x3 = viewing angle, and x4 = level of ambient light. From a table given in the article, SSRegr = 21.6, SSResid = 22, and n = 30. What is the value of the test statistic F What is the P-value What is r2 What is Searrow_forward
- Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation Based on a significance level of 5%, which of the following independent variables significantly predict the cash price of wheat? a. Economic Growth b. Population Growth c. Meat Consumption d. All the independent variables significantly predict the cash price of wheat.arrow_forwardMultiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. The actual cash price of wheat under investigation in 1963 was $2.13. Based on the comparison of the correct predicted cash price calculated in the previous question and the actual cash price, what does the evidence suggest about Cargill, Inc.? a. Because the predicted price is relatively close to the actual price (within one cent), Cargill, Inc. probably did not artificially manipulate the price of wheat.…arrow_forwardMultiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. In 1963, during the period in question, economic growth was 3.8; population growth was 1.40; and meat consumption was 152.95. Based on these values, what would be the predicted cash price of wheat at this time in 1963?arrow_forward
- Multiple regression is sometimes used in litigation. In the case of Cargill, Inc. v. Hardin (1971), the prosecution charged that the cash price of wheat was manipulated in violation of the Commodity Exchange Act. In a statistical study conducted for this case, a multiple regression model was constructed to predict the cash price of wheat using three supply-and-demand explanatory variables: economic growth, population growth, and meat consumption. Data for 24 years were used to construct the regression equation, and a prediction for the suspect period was computed from this equation. The following output represents the regression analysis. . Before the judge and jury consider the results of the regression model, they must ensure that the model is valid. What is the proper hypothesis test for this model, and what is the proper conclusion?arrow_forwardIn a study investigating maternal risk factors for congenital syphilis, syphilis is treated as a binary outcome variable, where 1 represents the presence of disease in a newborn and 0 represents absence of disease. The estimated coefficients from a logistic regression model containing the predictors cocaine or crack use, marital status, number of prenatal visits to a doctor, alcohol use and level of education are included in the table below. The estimated intercept is not included in the table. Variable Coefficient Cocaine/Crack Use 1.354 Marital Status 0.779 Number of Prenatal Visits -0.098 Alcohol Use 0.723 Level of Education 0.298 The estimated coefficient of cocaine or crack use has a standard error of 0.162. Construct a 95% confidence interval for the population odds ratio comparing women who used cocaine or crack versus those who did not. Conduct a test of the null hypothesis that the coefficient associated with cocaine or crack use is…arrow_forwardIn an attempt to develop a model of wine quality as judged by wine experts, data on alcohol content and wine quality was collected from variants of a particular wine. From a sample of 17 wines, a model was created using the percentages of alcohol to predict wine quality. From the results of that regression, b1=0.4386 and Sb1=0.1141. a. At the 0.05 level of significance, is there evidence of a linear relationship between the percentage of alcohol and wine quality? b. Construct a 95% confidence interval estimate of the population slope, β1. b. The 95% confidence interval is __ ≤ β1 ≤ __ (Round to three decimal places as needed.)arrow_forward
- A study is conducted to determine if there is a relationship between the two variables, blood haemoglobin (Hb) levels and packed cell volumes (PCV) in the female population. A simple linear regression analysis was performed using SPSS. Based on the SPSS output of the ANOVA table, which of the following statements is the CORRECT interpretation? 1. The regression model statistically significantly predicts the blood haemoglobin level. 2. About 39.98 % of variance in Hb is explained by PCV. 3. The regression model does not fit the data. 4. There is significant contribution of Hb towards PCV.arrow_forwardData was collected on 54 observations on a response of interest, y, and four potential predictor variables x1, x2, x3, and x4. The output from regression analyses of the data is attached to the end of the page. d) Is the variable from your best simple linear regression model (from part a) included in the model with the lowest overall MSE (part b)? Briefly explain why it could happen that the best single variable is not in the best overall model. e) Following the best subsets regression results, the sums of squares for regression and error (also called residual) are displayed for several models. Using the regression sums of squares information for the full model containing all four x variables, calculate i) the R2 value for the full model, ii) the F statistic for the test of the H0: b1 = b2 = b3 = b4 = 0, and iii) the standard deviation of the residuals for the full model. f) Using the regression sums of squares information, test the null hypothesis H0:b2 = b4 = 0 for the full model.…arrow_forwardConsider the accompanying data on x = research and development expenditure (millions of dollars) and y = growth rate (% per year) for eight different industries. x 2.025 5.039 0.906 3.573 1.157 0.327 0.378 0.191 y 1.90 3.96 2.44 0.88 0.37 −0.90 0.49 1.01 (a) Would a simple linear regression model provide useful information for predicting growth rate from research and development expenditure? Test the appropriate hypotheses using a 0.05 significance level. Calculate the test statistic. (Round your answer to two decimal places.) t = Use technology to find the P-value for this test. (Round your answer to four decimal places.) P-value = What can you conclude? Reject H0. We have convincing evidence of a useful linear relationship between growth rate and research and development expenditure.Fail to reject H0. We have convincing evidence of a useful linear relationship between growth rate and research and development expenditure. Fail to reject H0. We do not have…arrow_forward
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