Suppose that the weight of a particular hand weight is advertised to be 5 pounds, but it can actually be as little as 4.5 pounds or as much as 5.1 pounds. What is the variance of the weight? Round your answer to two decimal places.
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- A researcher finds that the amount of exercise one gets a week and cortisol levels are negatively correlated, r= 0.60. How much variance in cortisol levels is explained by the amount of exercise?Match the statement on the left with the most likely answer on the right. Each answer is used one time. The number of hours worked and the V [ Choose ] amount of your paycheck. This would probably be a negative correlation. This would probably have no correlation. The number of people in your office and This would probably be a positive correlation. your job satisfaction. This would be a strong positive correlation. This would be a strong negative correlation. The number of miles you drive and the cost [ Choose ] of your car insurance The number of hours at your job in a week [ Choose ] and your grade point average. The miles you walk and your risk for heart [ Choose ] disease.Will the following variables have positive correlation, negative correlation, or no correlation? IQ and annual salary Will these variables have positive correlation, negative correlation, or no correlation? O No correlation O Negative Positive Click to select your answer. acer esc @ 2$ % 8 W e r tab Dロ d f 9h a しの づ త #3 の
- Ta Q The lengths (in kilometers) of a random sample of 22 rivers on the South Island of New Zealand that flow to the Pacific Ocean are listed in table below. Lengths of Rivers (in km) 66 93 138 95 64 322 90 161 167 72 56 80 169 64 121 209 98 97 145 140 288 97Which of the following statements is true? 1. The R (ie., R-squared) tells us the percentage of the variation in the dependent variable (cost) that is explained by variation in the independent variable (activity) II The R (e. R-squared) varies from 0% to 100%, and the lower the percentage, the better the fit of the data to a straight line. ONeither statement is true. Only statement I is true. Only statement II is tru. Both statements are true.I need help with this one please thank you!
- Suppose the correlation between height and weight for adults is +0.80. What proportion of the variability in weight can be explained by the relationship with height? A. 20% B. 36% C. 64% D. 80%Assume the correlation coefficient between advertising dollars spent and sales is .78. How much of the variability in sales is explained by the relationship between sales and advertising dollars? a. 39.16% b. 60.84% c. 78% d. 22%The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. X Y 11.4 8.4 6.6 13.7 11 9.3 X = thousands of automatic weapons y = murders per 100,000 residents 3.7 7.5 This data can be modeled by the equation y = Answer = 2.4 2.6 2.4 0.4 6.3 5.9 6.2 4.1 to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. Answer = 0.84x +4.01. Use this equation A) How many murders per 100,000 residents can be expected in a state with 6.2 thousand automatic weapons? Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 6.9 thousand automatic weapons? Round to 3 decimal places.
- Listed below are amounts (in millions of dollars) collected from parking meters by Brinks. Find coefficient of variation The data were provided by the attorney for New York City, and they are listed on the DASL Web site. Use s= 2.137 Collection contractor was Brinks: 71 66 72 69 68 69 Group of answer choices 3.1% 11.5% 1.5% 4.1%Suppose you pulled info regarding the sex of the victim and offender for murders in the United States. The data look something like this: Sex of Vic. Sex of Off. F M F 124 421 M 1609 3725 Assume that the sex of the victim is the response variable. What is the relative risk of being killed by female offender for male victims?Select the most appropriate response. If the correlation between a person’s age and annual income is 0.60, then the coefficient of determination tells us that: 36% of the variation in a person’s annual income can be explained by the predictor variable age. 36% of a person’s annual income can be explained by their age 60% of the variation in a person’s annual income can be explained by the predictor variable age 60% of a person’s annual income can be explained by their age