Hormone replacement therapy (HRT) is thought to increase the risk of breast cancer. The accompanying data on x = percent of women using HRT and y = breast cancer incidence (cases per 100,000 women) for a region in Germany for 5 years appeared in paper "Decline in Breast Cancer Incidence after Decrease in Utilization of Hormone Replacement Therapy." The authors of the paper used a simple linear regression model Iin the describe the relationship between HRT use and breast cancer incidence. HRT Use Breast Cancer Incidence 46.30 103.30 40.60 105.00 39.50 100.00 36.60 93.80 30.00 83.50 n USE SALT (a) What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) (b) What is the estimated average change in breast cancer incidence (in cases per 100,000 women) associated with a 1 percentage point increase in HRT use? (Round your answer to four decimal places.) cases per 100,000 women (c) What breast cancer incidence (in cases per 100,000 women) would be predicted in a year when HRT use was 41%? (Round your answer to three decimal places.) cases per 100,000 women

Hormone replacement therapy (HRT) is thought to increase the risk of breast cancer. The accompanying data on x = percent of women using HRT and y = breast cancer incidence (cases per 100,000 women) for a region in Germany for 5 years appeared in paper "Decline in Breast Cancer Incidence after Decrease in Utilization of Hormone Replacement Therapy." The authors of the paper used a simple linear regression model Iin the describe the relationship between HRT use and breast cancer incidence. HRT Use Breast Cancer Incidence 46.30 103.30 40.60 105.00 39.50 100.00 36.60 93.80 30.00 83.50 n USE SALT (a) What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) (b) What is the estimated average change in breast cancer incidence (in cases per 100,000 women) associated with a 1 percentage point increase in HRT use? (Round your answer to four decimal places.) cases per 100,000 women (c) What breast cancer incidence (in cases per 100,000 women) would be predicted in a year when HRT use was 41%? (Round your answer to three decimal places.) cases per 100,000 women

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: FR=a+B0IL+YEXP+8FDI Where FR = yearly foreign reserves ($000°s), OIL = annual oil prices, EXP = yearly total exports ($000's) and FDI = annual foreign direct investment ($000`s). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491 OIL 85.39 18.46 4.626 0.0006 EXP -377.08 112.19 0.0057 FDI -396.99 160.66 -2.471 ** s = 2.45 R-sq = 96.3% R-sq (adj) = 95.3% Analysis of Variance Source DF MS F Regression 1991.31 663.77 ?? Error 12 77.4 6.45 Total 15 d) Hence test whether ß is significant. Give reasons for your answer. e) Perform the F Test making sure to state the null and alternative hypothesis. f) Given an interpretation of the term “R-sq“ and comment on its value.
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A retail company wants to understand the factors that impact its sales revenue. The company has collected data on the following variables for the past 5 months: Total sales revenue (Y), Average store foot traffic (XI), and Marketing budget (X3). Develop a multiple linear regression model to predict that, what is the impact of average store foot traffic, and marketing budget on total sales revenue for the retail company. The data is summarized in Table 4. Table 4 Average Store foot traffic (X1) Sales Revenue (Y) 50$ 5 130S 45$ 48$ 7 6 120$ 200$ 60$ 708 5 4 Marketing Budget (X2) 130$ 250$ Note: Average Store foot traffic is the average number of people enter in the store per minute
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The Wall Street Journal asked Concur Technologies, Inc., an expense management company, to examine data from million expense reports to provide insights regarding business travel expenses. Their analysis of the data showed that New York was the most expensive city. The following table shows the average daily hotel room rate () and the average amount spent on entertainment () for a random sample of of the most-visited U.S. cities. These data lead to the estimated regression equation . For these data . Click on the datafile logo to reference the data. Use Table 1 of Appendix B. full question attached in ss thanks for help aprpeicated aigjrowoirj
From the article “Association of cognitive functioning with retinal nerve fiber layer thickness” by van Koolwijk et al., in Investigative Ophthalmology & Visual Science, October 2009, Vol. 50, No. 10, below is table 2 showing the results of fitting several multiple linear regression models for different response variables. Write down the fitted model corresponding to the last row of the table. You can leave the intercept as hatB0. Interpret the coefficient values corresponding to the RNFL Thickness and Male variables.
A researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: FR=a+B0IL+YEXP+8FDI Where FR = yearly foreign reserves ($000°s), OIL = annual oil prices, EXP = yearly total exports ($000's) and FDI = annual foreign direct investment ($000`s). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491 OIL 85.39 18.46 4.626 0.0006 EXP -377.08 112.19 0.0057 FDI -396.99 160.66 -2.471 ** s = 2.45 R-sq = 96.3% R-sq (adj) = 95.3% Analysis of Variance Source DF MS F Regression 1991.31 663.77 ?? Error 12 77.4 6.45 Total 15 a) What is dependent and independent variables? b) Fully write out the regression equation
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Acrylamide is a chemical that is sometimes found in cooked starchy foods and which is thought to increase the risk of certain kinds of cancer. The paper "A Statistical Regression Model for the Estimation of Acrylamide Concentrations in French Fries for Excess Lifetime Cancer Risk Assessment"† describes a study to investigate the effect of x = frying time (in seconds) and y = acrylamide concentration (in micrograms per kg) in French fries. The data in the accompanying table are approximate values read from a graph that appeared in the paper. FryingTime AcrylamideConcentration 150 155 240 115 240 190 270 180 300 140 300 265 (a) Construct a scatterplot of these data. A scatterplot has 6 points.The horizontal axis is labeled "x" and ranges from 100 to 350.The vertical axis is labeled "y" and ranges from 50 to 350.The points are plotted from left to right in a horizontal direction starting from the lower left side of the diagram.Along the horizontal axis, there is 1 point at 150, 2 points…