A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-1.244 b=35.185 r2-0.974169 r=-0.987 Assume the correlation is significant, and use this to predict the mumber of situps a person who watches 5.5 hours of TV can do (to one decimal place)

A psychology professor studies the relationship between anxiety(X) and test performance (Y). Ten students take an anxiety questionnaire prior to taking the final exam. Below is the SPSS printout of the regression equation. Correlations   Anxiety FinalExam Anxiety Pearson Correlation 1 -.691* Sig. (2-tailed)   .027 N 10 10 FinalExam Pearson Correlation -.691* 1 Sig. (2-tailed) .027   N 10 10 *. Correlation is significant at the 0.05 level (2-tailed).   ANOVAa   Model Sum of Squares df Mean Square F Sig.   1 Regression 861.959 1 861.959 7.301 .027b   Residual 944.441 8 118.055       Total 1806.400 9         a. Dependent Variable: FinalExam   b. Predictors: (Constant), Anxiety   Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 125.883…

A regression line is used to satisfactorily describe the relationship between the width of tires in inches and lifespan (in months) of 14 tires in a tire performance study. The regression analysis is shown. Variable Constant Coeff SE Coefft Ratio p-Value 7.3985 0.5638 13.12 Width of tires 3.9571 3.9571 0.7382 R squared = 88.5% O 7.3985 ± 2.624(0.5638) O 3.9571±2.65(0.7382) O 3.9571±2.624(0.7382) 5.36 Which expression should be used to compute a 98 percent confidence interval for the slope of the regression line? O 7.3985 +2.681(0.5638) O 3.9571 +2.681(0.7382) 0.034 0.005 R squared (adj) = 87.9%

A manager at a local bank analyzed the relationship between monthly salary and three independent variables: Length of service (measured in months), Gender (0 = female, 1= male), and Job type (0 = clerical, 1 = technical). The following ANOVA summarizes the %3D regression results. ANOVA Source of Variation df Sum of Squares Mean Square F Regression 3 1,004,346.771 334,782.257 5.96 Residual 26 1,461,134.596 56,197.48445 Total 29 2,465,481.367 Coefficients Standard Error t-Stat p-value Intercept 784.92 322.25 2.44 0.02 Service 9.19 3.20 2.87 0.01 Gender 222.78 89.00 2.50 0.02 Job -28.21 89.61 -0.31 0.76 The level of significance is 0.05. In the regression model, which of the following are dummy variables?

approximately the same amount of wine per week. Here are the data (for drinkers only):" CANCER Wine intake x 2.5 8.5 15.5 26.5 (grams per day) Relative risk y 1.00 1.08 1.15 1.22 (a) Make a scatterplot of these data. Based on the scatter- plot, do you expect the correlation to be positive or negative? Near ±1 or not? (b) Find the correlation r between wine intake and relative risk. Do the data show that women who consume more wine tend to have higher relative risks of breast cancer? Wine and cancer in women. Some studies have suggested that a nightly glass of wine may not only take the edge off a day but also improve health. Is wine good for your health? A study of nearly 1.3 million middle-aged British women examined wine consumption and the risk of breast cancer. The researchers were interested in how risk changed as wine consumption increased. Risk is based on breast cancer rates in drinkers relative to breast cancer rates in nondrinkers in the study, with higher values indicating…

Weekly sales of a company’s product (y) and those of its main competitor (x) were recorded for one year. Use ?=0.10. a) Conduct a regression analysis of these data.  b) Plot the residuals versus the time periods. Does there appear to be autocorrelation?  c) Perform the Durbin-Watson test. Is there evidence of autocorrelation? Use ?=0.10  d) If autocorrelation was detected in Part c, propose an alternative regression model to remedy the problem. Use the computer to generate the statistics associated with this model.  e) Redo Parts b and c. Compare the two models.  PLEASE ONLY USE EXCEL AND SHOW ALL EXCEL COMMANDS! Company Competitor 1383 1153 2022 1067 2429 1296 901 1406 2219 1254 1896 1335 1910 1204 1539 1076 2048 1246 2881 1098 2395 1258 1349 955 2052 954 2675 1363 1588 1022 2704 1155 3148 1807 2822 1295 2217 1116 2260 1323 3040 1326 3137 1107 2977 1163 2599 1254 2412 1017 3187 938 2598 1270 2995 1042 3046 1318 3858 1497…

Consider a regression that uses X = # of 60 pound bench press repetitions to predict Y = maximum bench press amount in pounds…..for the ith student among a sample of high school athletes.  The estimated predicted sample regression is as follows Maxi= 68.91 + .6885Repsi            *the ei is left off for simplicity   The table below lists the actual data from which the regression above was estimated.  For example, a student in the study who trained by doing 12 “60 pound reps” accomplished a maximum bench press amount of 76 pounds.   Xi Yi 12 76 15 82 17 79 3 71   Predict the average maximum bench press for a student that does 17 “60 pound reps”      a)  a.  around 68.85 pounds   b)  b.  around 85.00 pounds   c)  c.  around 80.61 pounds   d)  d.  around 68.91 pounds

A veterinarian collects data about puppies in her practice. She makes ascatterplot with their age in days on the horizontal axis and weight in ounces (oz)on the vertical axis and sees that the scatterplot is football shaped. The averageage of the puppies is 42 day, with an SD of 7 days. The average weight is 25 oz,with an SD of 5 oz. The correlation is moderately strong, with r = 0.7.(a) The age of two puppies differ by 20 days. The difference in their weight ispredicted to be ________________ oz.(b) The baseline prediction for the weight of puppies ignores their age and justuses the information from all puppies. The baseline predicted weight is________________ oz and the RMS error for this prediction is ________________ oz.(c) Suppose we converted the units of age from days to weeks. The average ageis now _______________ weeks, with an SD of _______________ weeks. Thecorrelation between age in weeks and weight in oz is nowr = ___________________

A regression model to predict Y, the state burglary rate per 100,000 people, used the following four state predictors: X1 = median age, X2 = number of bankruptcies per 1,000 population, X3 = federal expenditures per capita (a leading predictor), and X4 = high school graduation percentage. Click here for the Excel Data File (a) Using the sample size of 45 people, calculate the tcalc and p-value in the table given below. (Negative values should be indicated by a minus sign. Leave no cells blank - be certain to enter "0" wherever required. Round your t-values to 3 decimal places and p- values to 4 decimal places.) Predictor Intercept AgeMed Coefficient SE tcalc p-value 4,641.0430 798.0634 -28.8630 12.4684 Bankrupt 20.1604 12.1079 FedSpend HSGrad% -0.0181 0.0181 -30.3196 7.1136 (b-1) What is the critical value of Student's tin Appendix D for a two-tailed test at a = .01? (Round your answer to 3 decimal places.) -value =

A study was conducted to determine if the number of hours of sleep per night was related to exam performance on an exam taken the following day. The correlation coefficient was found to be .78. Which statement is FALSE? The relationship between hours of sleep and exam performance the following day is strong. As the number of hours of sleep increases, exam performance is also expected to increase at a similar rate. The r-value is .78. The number of hours of sleep cannot be used to estimate or predict possible performance on an exam the following day.