A regression was done for 20 cities with the latitude (Latitude), a measurement of the distance of a location on the Earth from the equator in degrees and the average January temperatures (Temp) as the dependent variable measure in. The regression equation is a) The value of the slope is [Select] [Select] TEMP=48.4 -0.31 x LATITUDE b) The predicted average January temperatures for the 20 cities for a latitude of 45 degrees is [Select] ✓. If in fact the [Select] average January temperature for a latitude of 45 degrees is 40° F the residual is [Select] which is a(n) which means [Select] c) The coefficient of determination is R² = 66.2%. This value means [Select] The correlation r, is which means [Select] d) The y-intercept is [Select] the problem interprets to [Select] make sense in the context of the problem? [Select] which in the context of Dos this value

A regression was done for 20 cities with the latitude (Latitude), a measurement of the distance of a location on the Earth from the equator in degrees and the average January temperatures (Temp) as the dependent variable measure in. The regression equation is a) The value of the slope is [Select] [Select] TEMP=48.4 -0.31 x LATITUDE b) The predicted average January temperatures for the 20 cities for a latitude of 45 degrees is [Select] ✓. If in fact the [Select] average January temperature for a latitude of 45 degrees is 40° F the residual is [Select] which is a(n) which means [Select] c) The coefficient of determination is R² = 66.2%. This value means [Select] The correlation r, is which means [Select] d) The y-intercept is [Select] the problem interprets to [Select] make sense in the context of the problem? [Select] which in the context of Dos this value

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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Give the equation of the regression line: -913.2969+0.4808years(y) a) Plug an actual x value into the equation and discuss the difference between the real y and the predicted yb) Discuss the domain of your datac) Include the scatterplot with line of best fit
A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (x) and number of sit ups a person can do (y). The results of the regression were: y=ax+ba=-1.114b=38.796r2=0.815409r=-0.903 Use this to predict the number of sit ups a person who watches 7 hours of TV can do, and please round your answer to a whole number.
Ariel was running analyses over and over in census data and came across a correlation between weight and debt (r=.78). Independent variable is weight and dependent variable is debt. b) Curious Ariel noted some statistics on these weight (M=160lb, SD=15lb) and debt (M=196k, SD=20k). If Ariel wanted to calculate a regression equation, what would her slope and intercept be?
Find the slope between the points (4/3,4/13) and (19/3,18/13)
Using the weights (lb) and highway fuel consumption amounts (mi/gal) of the 48 cars listed in the accompanying data set, one gets this regression equation: y = 58.9-0.00749x, where x represents weight. Complete parts (a) through (d). Click the icon to view the car data. The site to your captio V.UVITU. D. The slope is -0.00749 and the y-intercept is 58.9. c. What is the predictor variable? ... OA. The predictor variable is highway fuel consumption, which is represented by x. OB. The predictor variable is highway fuel consumption, which is represented by y. C. The predictor variable is weight, which is represented by x. OD. The predictor variable is weight, which is represented by y. d. Assuming that there is a significant linear correlation between weight and highway fuel consumption, what is the best predicted value for a car that weighs 2994 lb? The best predicted value of highway fuel consumption of a car that weighs 2994 lb is (Round to one decimal place as needed.) mi/gal.
please use this situation: A small theater company has a linear regression model to estimate y = the concession stand sales in dollars, based on knowing x = the number of people in attendance. The regression equation is: = 6.72x + 11.50 and the correlation coefficient was r = 0.781. The data set saw the number of people in attendance ranging from a minimum of 18 people to a maximum of 170 people. 1) How reliable would it be to make a prediction for the concession sales amount if there were 500 people in attendance? Explain.
Q1) A real estate consultant is considering developing a series of price models for residential houses in different areas of the province of Ontario, in Canada. The dataset, provided by the Windsor and Essex County Board, covers residential home sales in Windsor. To develop the model, the consultant performs a linear regression to estimate the price (in Canadian dollars) as a linear function of the size of the apartment (in square feet) and obtains the following Excel result. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.53580413 0.28708607 0.28577556 41101.9362 546 ANOVA df MS Significance F 1 3.70082E+11 3.7008E+11 219.065461 6.74643E-42 Regression Residual Total 544 9.19017E+11 1689369158 545 1.2891E+12 Coefficients Standard Error 62171.5874 4537.036582 13.7031268 6.2969E-37 53259.33066 71083.8441 t Stat P-value Lower 95% Upper 95% Intercept LOTSIZE 12.0187669 0.81203165 14.8008601 6.7464E-42 10.42366523 13.6138685 a) Write…
Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂ = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…
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