A data set from a study that examined the effect of a specific diet on blood pressure is provided on the course web page. Participants (n = 72) were randomly assigned either to a group that was put on the diet (Diet = Present) or to a group that was not put on the diet (Diet = Absent), and researchers wanted to know whether the diet had a significant impact on blood pressure. The other variables in the data set are drug (X, Y, or Z) and a biofeedback condition (present or absent), but we wish to focus on the diet effect here. Test whether there is a statistically significant difference in mean blood pressure between the diet groups. Report the appropriate test statistic, df, and p-value. Biofeedback Drug Diet BldPres Present X Absent 170 Present X Absent 175 Present X Absent 165 Present X Absent 180 Present X Absent 160 Present X Absent 158 Present X Present 161 Present X Present 173 Present X Present 157 Present X Present 152 Present X Present 181 Present X Present 190 Present Y Absent 186 Present Y Absent 194 Present Y Absent 201 Present Y Absent 215 Present Y Absent 219 Present Y Absent 209 Present Y Present 164 Present Y Present 166 Present Y Present 159 Present Y Present 182 Present Y Present 187 Present Y Present 174 Present Z Absent 180 Present Z Absent 187 Present Z Absent 199 Present Z Absent 170 Present Z Absent 204 Present Z Absent 194 Present Z Present 162 Present Z Present 184 Present Z Present 183 Present Z Present 156 Present Z Present 180 Present Z Present 173 Absent X Absent 173 Absent X Absent 194 Absent X Absent 197 Absent X Absent 190 Absent X Absent 176 Absent X Absent 198 Absent X Present 164 Absent X Present 190 Absent X Present 169 Absent X Present 164 Absent X Present 176 Absent X Present 175 Absent Y Absent 189 Absent Y Absent 194 Absent Y Absent 217 Absent Y Absent 206 Absent Y Absent 199 Absent Y Absent 195 Absent Y Present 171 Absent Y Present 173 Absent Y Present 196 Absent Y Present 199 Absent Y Present 180 Absent Y Present 203 Absent Z Absent 202 Absent Z Absent 228 Absent Z Absent 190 Absent Z Absent 206 Absent Z Absent 224 Absent Z Absent 204 Absent Z Present 205 Absent Z Present 199 Absent Z Present 170 Absent Z Present 160 Absent Z Present 179 Absent Z Present 179

A statistical program is recommended. A company provides maintenance service for water-filtration systems throughout southern Florida. Customers contact the company with requests for maintenance service on their water-filtration systems. To estimate the service time and the service cost, the company's managers want to predict the repair time necessary for each maintenance request. Hence, repair time in hours is the dependent variable. Repair time is believed to be related to three factors, the number of months since the last maintenance service, the type of repair problem (mechanical or electrical), and the repairperson who performed the service. Data for a sample of 10 service calls are reported in the table below. ŷ = Repair Time in Hours 2.9 3.0 ŷ = 4.8 1.8 2.4 4.9 4.7 4.8 4.4 4.5 Months Since Last Service 2 6 8 3 2 7 9 8 4 6 Type of Repair Electrical Mechanical Electrical Mechanical Electrical Electrical Mechanical Mechanical Electrical Electrical Repairperson Dave Newton Dave…

The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technology was also given a foot length of 17.8 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 17.8 cm, what is the single value that is the best predicted height for that person? Click the icon to view the technology output. The single value that is the best predicted height is (Round to the nearest whole number as needed.) cm. Technology Output The regression equation is Height = 53.3+4.25 Foot Length Predictor Constant Coef SE Coef 53.28 11.44 Foot Length 4.2537 0.4699 S = 5.50739 R-Sq=72.3% R-Sq (adj) = 71.6% T 4.66 9.05 Predicted Values for New Observations New Obs 1 Fit SE Fit 128.996 1.733 (124.309, 133.683) 95% CI Foot New Obs Length 1 17.8 0.000 0.000 Values of Predictors for New…

Among the literature on quitting smoking are data detailing the relative successfulness of people of different ages in quitting smoking. A study of 400 adults who began various smoking-cessation programs produced the data in the table below. In the table, each participant is classified according to two variables: length of their smoking cessation period ("Less than two weeks", "Between two weeks and one year", or "At least one year") and age ("21-34", or "35 and over"). In the table, "less than two weeks" means that the individual returned to smoking within two weeks of beginning the program; "between two weeks and one year" means that the individual lasted the first two weeks without smoking but retuned to smoking within a year; and "at least one year" means that the individual has not smoked for at least a year since beginning the program. The table is a contingency table whose cells contain the respective observed frequencies of classifications of the 400 smokers. In addition, three…

Fifty-four wild bears were​ anesthetized, and then their weights and chest sizes were measured and listed in a data set. Results are shown in the accompanying display. Is there sufficient evidence to support the claim that there is a linear correlation between the weights of bears and their chest​ sizes? When measuring an anesthetized​ bear, is it easier to measure chest size than​ weight? If​ so, does it appear that a measured chest size can be used to predict the​ weight? Use a significance level of α=0.05. Correlation Results Correlation​ coeff, r: 0.955111 Critical​ r: ±0.2680855 ​P-value (two​ tailed): 0.000         Determine the null and alternative hypotheses.   H0​: ρ less than< not equals≠ less than< greater than> equals= nothing H1​: ρ

A doctor wanted to study the effect of four different treatments on mental health. A group of 100 adults experiencing depression volunteered for the study. The doctor randomly assigned one-fourth of them to each of four groups. Group 1 followed a specific exercise plan, group 2 followed a specific diet plan, group 3 followed an exercise and diet plan, and group 4 did not follow any plan. After 4 weeks, the doctor contacted each participant and asked them if they felt any better. The results are displayed in the table.    The doctor would like to know if these data provide convincing evidence that the distribution of responses differs across the treatment groups in the population of all patients like these. The random condition is met. The 10% condition is not needed because random sampling did not take place. This table of expected counts shows that all expected counts are at least 5.   What is the value of the chi-square test statistic? χ‑2 = 5.03 χ‑2 = 6.26 χ‑2 = 25.33 χ‑2 = 39.22